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Sample records for imaging accurately predict

  1. ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

    Science.gov (United States)

    Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2016-05-01

    At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

  2. Mental models accurately predict emotion transitions.

    Science.gov (United States)

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  3. Mental models accurately predict emotion transitions

    Science.gov (United States)

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  4. Predicting plant biomass accumulation from image-derived parameters

    Science.gov (United States)

    Chen, Dijun; Shi, Rongli; Pape, Jean-Michel; Neumann, Kerstin; Graner, Andreas; Chen, Ming; Klukas, Christian

    2018-01-01

    Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species. PMID:29346559

  5. A new, accurate predictive model for incident hypertension

    DEFF Research Database (Denmark)

    Völzke, Henry; Fung, Glenn; Ittermann, Till

    2013-01-01

    Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures.......Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....

  6. Can magnetic resonance imaging accurately predict concordant pain provocation during provocative disc injection?

    International Nuclear Information System (INIS)

    Kang, Chang Ho; Kim, Yun Hwan; Kim, Jung Hyuk; Chung, Kyoo Byung; Sung, Deuk Jae; Lee, Sang-Heon; Derby, Richard

    2009-01-01

    To correlate magnetic resonance (MR) image findings with pain response by provocation discography in patients with discogenic low back pain, with an emphasis on the combination analysis of a high intensity zone (HIZ) and disc contour abnormalities. Sixty-two patients (aged 17-68 years) with axial low back pain that was likely to be disc related underwent lumbar discography (178 discs tested). The MR images were evaluated for disc degeneration, disc contour abnormalities, HIZ, and endplate abnormalities. Based on the combination of an HIZ and disc contour abnormalities, four classes were determined: (1) normal or bulging disc without HIZ; (2) normal or bulging disc with HIZ; (3) disc protrusion without HIZ; (4) disc protrusion with HIZ. These MR image findings and a new combined MR classification were analyzed in the base of concordant pain determined by discography. Disc protrusion with HIZ [sensitivity 45.5%; specificity 97.8%; positive predictive value (PPV), 87.0%] correlated significantly with concordant pain provocation (P < 0.01). A normal or bulging disc with HIZ was not associated with reproduction of pain. Disc degeneration (sensitivity 95.4%; specificity 38.8%; PPV 33.9%), disc protrusion (sensitivity 68.2%; specificity 80.6%; PPV 53.6%), and HIZ (sensitivity 56.8%; specificity 83.6%; PPV 53.2%) were not helpful in the identification of a disc with concordant pain. The proposed MR classification is useful to predict a disc with concordant pain. Disc protrusion with HIZ on MR imaging predicted positive discography in patients with discogenic low back pain. (orig.)

  7. Quicksilver: Fast predictive image registration - A deep learning approach.

    Science.gov (United States)

    Yang, Xiao; Kwitt, Roland; Styner, Martin; Niethammer, Marc

    2017-09-01

    This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  9. A flexible and accurate digital volume correlation method applicable to high-resolution volumetric images

    Science.gov (United States)

    Pan, Bing; Wang, Bo

    2017-10-01

    Digital volume correlation (DVC) is a powerful technique for quantifying interior deformation within solid opaque materials and biological tissues. In the last two decades, great efforts have been made to improve the accuracy and efficiency of the DVC algorithm. However, there is still a lack of a flexible, robust and accurate version that can be efficiently implemented in personal computers with limited RAM. This paper proposes an advanced DVC method that can realize accurate full-field internal deformation measurement applicable to high-resolution volume images with up to billions of voxels. Specifically, a novel layer-wise reliability-guided displacement tracking strategy combined with dynamic data management is presented to guide the DVC computation from slice to slice. The displacements at specified calculation points in each layer are computed using the advanced 3D inverse-compositional Gauss-Newton algorithm with the complete initial guess of the deformation vector accurately predicted from the computed calculation points. Since only limited slices of interest in the reference and deformed volume images rather than the whole volume images are required, the DVC calculation can thus be efficiently implemented on personal computers. The flexibility, accuracy and efficiency of the presented DVC approach are demonstrated by analyzing computer-simulated and experimentally obtained high-resolution volume images.

  10. Highly Accurate Prediction of Jobs Runtime Classes

    OpenAIRE

    Reiner-Benaim, Anat; Grabarnick, Anna; Shmueli, Edi

    2016-01-01

    Separating the short jobs from the long is a known technique to improve scheduling performance. In this paper we describe a method we developed for accurately predicting the runtimes classes of the jobs to enable this separation. Our method uses the fact that the runtimes can be represented as a mixture of overlapping Gaussian distributions, in order to train a CART classifier to provide the prediction. The threshold that separates the short jobs from the long jobs is determined during the ev...

  11. Characterization, prediction, and correction of geometric distortion in 3 T MR images

    International Nuclear Information System (INIS)

    Baldwin, Lesley N.; Wachowicz, Keith; Thomas, Steven D.; Rivest, Ryan; Gino Fallone, B.

    2007-01-01

    The work presented herein describes our methods and results for predicting, measuring and correcting geometric distortions in a 3 T clinical magnetic resonance (MR) scanner for the purpose of image guidance in radiation treatment planning. Geometric inaccuracies due to both inhomogeneities in the background field and nonlinearities in the applied gradients were easily visualized on the MR images of a regularly structured three-dimensional (3D) grid phantom. From a computed tomography scan, the locations of just under 10 000 control points within the phantom were accurately determined in three dimensions using a MATLAB-based computer program. MR distortion was then determined by measuring the corresponding locations of the control points when the phantom was imaged using the MR scanner. Using a reversed gradient method, distortions due to gradient nonlinearities were separated from distortions due to inhomogeneities in the background B 0 field. Because the various sources of machine-related distortions can be individually characterized, distortions present in other imaging sequences (for which 3D distortion cannot accurately be measured using phantom methods) can be predicted negating the need for individual distortion calculation for a variety of other imaging sequences. Distortions were found to be primarily caused by gradient nonlinearities and maximum image distortions were reported to be less than those previously found by other researchers at 1.5 T. Finally, the image slices were corrected for distortion in order to provide geometrically accurate phantom images

  12. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

    Science.gov (United States)

    Patel, Meenal J; Andreescu, Carmen; Price, Julie C; Edelman, Kathryn L; Reynolds, Charles F; Aizenstein, Howard J

    2015-10-01

    Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment. Copyright © 2015 John Wiley

  13. Influential Factors for Accurate Load Prediction in a Demand Response Context

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Kjærgaard, Mikkel Baun; Jørgensen, Bo Nørregaard

    2016-01-01

    Accurate prediction of a buildings electricity load is crucial to respond to Demand Response events with an assessable load change. However, previous work on load prediction lacks to consider a wider set of possible data sources. In this paper we study different data scenarios to map the influence....... Next, the time of day that is being predicted greatly influence the prediction which is related to the weather pattern. By presenting these results we hope to improve the modeling of building loads and algorithms for Demand Response planning.......Accurate prediction of a buildings electricity load is crucial to respond to Demand Response events with an assessable load change. However, previous work on load prediction lacks to consider a wider set of possible data sources. In this paper we study different data scenarios to map the influence...

  14. Accurate predictions for the LHC made easy

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    The data recorded by the LHC experiments is of a very high quality. To get the most out of the data, precise theory predictions, including uncertainty estimates, are needed to reduce as much as possible theoretical bias in the experimental analyses. Recently, significant progress has been made in computing Next-to-Leading Order (NLO) computations, including matching to the parton shower, that allow for these accurate, hadron-level predictions. I shall discuss one of these efforts, the MadGraph5_aMC@NLO program, that aims at the complete automation of predictions at the NLO accuracy within the SM as well as New Physics theories. I’ll illustrate some of the theoretical ideas behind this program, show some selected applications to LHC physics, as well as describe the future plans.

  15. Can phenological models predict tree phenology accurately under climate change conditions?

    Science.gov (United States)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  16. Prediction of Accurate Mixed Mode Fatigue Crack Growth Curves using the Paris' Law

    Science.gov (United States)

    Sajith, S.; Krishna Murthy, K. S. R.; Robi, P. S.

    2017-12-01

    Accurate information regarding crack growth times and structural strength as a function of the crack size is mandatory in damage tolerance analysis. Various equivalent stress intensity factor (SIF) models are available for prediction of mixed mode fatigue life using the Paris' law. In the present investigation these models have been compared to assess their efficacy in prediction of the life close to the experimental findings as there are no guidelines/suggestions available on selection of these models for accurate and/or conservative predictions of fatigue life. Within the limitations of availability of experimental data and currently available numerical simulation techniques, the results of present study attempts to outline models that would provide accurate and conservative life predictions.

  17. Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging.

    Science.gov (United States)

    Choi, Lark Kwon; You, Jaehee; Bovik, Alan Conrad

    2015-11-01

    We propose a referenceless perceptual fog density prediction model based on natural scene statistics (NSS) and fog aware statistical features. The proposed model, called Fog Aware Density Evaluator (FADE), predicts the visibility of a foggy scene from a single image without reference to a corresponding fog-free image, without dependence on salient objects in a scene, without side geographical camera information, without estimating a depth-dependent transmission map, and without training on human-rated judgments. FADE only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. Fog aware statistical features that define the perceptual fog density index derive from a space domain NSS model and the observed characteristics of foggy images. FADE not only predicts perceptual fog density for the entire image, but also provides a local fog density index for each patch. The predicted fog density using FADE correlates well with human judgments of fog density taken in a subjective study on a large foggy image database. As applications, FADE not only accurately assesses the performance of defogging algorithms designed to enhance the visibility of foggy images, but also is well suited for image defogging. A new FADE-based referenceless perceptual image defogging, dubbed DEnsity of Fog Assessment-based DEfogger (DEFADE) achieves better results for darker, denser foggy images as well as on standard foggy images than the state of the art defogging methods. A software release of FADE and DEFADE is available online for public use: http://live.ece.utexas.edu/research/fog/index.html.

  18. Predicting perceptual quality of images in realistic scenario using deep filter banks

    Science.gov (United States)

    Zhang, Weixia; Yan, Jia; Hu, Shiyong; Ma, Yang; Deng, Dexiang

    2018-03-01

    Classical image perceptual quality assessment models usually resort to natural scene statistic methods, which are based on an assumption that certain reliable statistical regularities hold on undistorted images and will be corrupted by introduced distortions. However, these models usually fail to accurately predict degradation severity of images in realistic scenarios since complex, multiple, and interactive authentic distortions usually appear on them. We propose a quality prediction model based on convolutional neural network. Quality-aware features extracted from filter banks of multiple convolutional layers are aggregated into the image representation. Furthermore, an easy-to-implement and effective feature selection strategy is used to further refine the image representation and finally a linear support vector regression model is trained to map image representation into images' subjective perceptual quality scores. The experimental results on benchmark databases present the effectiveness and generalizability of the proposed model.

  19. Accurate and robust brain image alignment using boundary-based registration.

    Science.gov (United States)

    Greve, Douglas N; Fischl, Bruce

    2009-10-15

    The fine spatial scales of the structures in the human brain represent an enormous challenge to the successful integration of information from different images for both within- and between-subject analysis. While many algorithms to register image pairs from the same subject exist, visual inspection shows that their accuracy and robustness to be suspect, particularly when there are strong intensity gradients and/or only part of the brain is imaged. This paper introduces a new algorithm called Boundary-Based Registration, or BBR. The novelty of BBR is that it treats the two images very differently. The reference image must be of sufficient resolution and quality to extract surfaces that separate tissue types. The input image is then aligned to the reference by maximizing the intensity gradient across tissue boundaries. Several lower quality images can be aligned through their alignment with the reference. Visual inspection and fMRI results show that BBR is more accurate than correlation ratio or normalized mutual information and is considerably more robust to even strong intensity inhomogeneities. BBR also excels at aligning partial-brain images to whole-brain images, a domain in which existing registration algorithms frequently fail. Even in the limit of registering a single slice, we show the BBR results to be robust and accurate.

  20. Comparison of an Imaging Software and Manual Prediction of Soft Tissue Changes after Orthognathic Surgery

    Directory of Open Access Journals (Sweden)

    M. S. Ahmad Akhoundi

    2012-01-01

    Full Text Available Objective: Accurate prediction of the surgical outcome is important in treating dentofacial deformities. Visualized treatment objectives usually involve manual surgical simulation based on tracing of cephalometric radiographs. Recent technical advancements have led to the use of computer assisted imaging systems in treatment planning for orthognathic surgical cases. The purpose of this study was to examine and compare the ability and reliability of digitization using Dolphin Imaging Software with traditional manual techniques and to compare orthognathic prediction with actual outcomes.Materials and Methods: Forty patients consisting of 35 women and 5 men (32 class III and 8 class II with no previous surgery were evaluated by manual tracing and indirect digitization using Dolphin Imaging Software. Reliability of each method was assessed then the two techniques were compared using paired t test.Result: The nasal tip presented the least predicted error and higher reliability. The least accurate regions in vertical plane were subnasal and upper lip, and subnasal and pogonion in horizontal plane. There were no statistically significant differences between the predictions of groups with and without genioplasty.Conclusion: Computer-generated image prediction was suitable for patient education and communication. However, efforts are still needed to improve accuracy and reliability of the prediction program and to include changes in soft tissue tension and muscle strain.

  1. NNLOPS accurate predictions for $W^+W^-$ production arXiv

    CERN Document Server

    Re, Emanuele; Zanderighi, Giulia

    We present novel predictions for the production of $W^+W^-$ pairs in hadron collisions that are next-to-next-to-leading order accurate and consistently matched to a parton shower (NNLOPS). All diagrams that lead to the process $pp\\to e^- \\bar \

  2. Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

    International Nuclear Information System (INIS)

    Hormuth II, David A; Weis, Jared A; Barnes, Stephanie L; Miga, Michael I; Yankeelov, Thomas E; Rericha, Erin C; Quaranta, Vito

    2015-01-01

    Reaction–diffusion models have been widely used to model glioma growth. However, it has not been shown how accurately this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. To this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction–diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor ‘grown’ for ten days as dictated by the reaction–diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n = 9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging, were used to evaluate the model’s accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error 0.92) and local (CCC values >0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error >11.7%, Dice <0.81) and higher local (CCC <0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction–diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions. (paper)

  3. ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

    Science.gov (United States)

    Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

    2016-11-01

    ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P DRAGON score estimates (P DRAGON score estimates (P DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.

  4. Estimating Accurate Target Coordinates with Magnetic Resonance Images by Using Multiple Phase-Encoding Directions during Acquisition.

    Science.gov (United States)

    Kim, Minsoo; Jung, Na Young; Park, Chang Kyu; Chang, Won Seok; Jung, Hyun Ho; Chang, Jin Woo

    2018-06-01

    Stereotactic procedures are image guided, often using magnetic resonance (MR) images limited by image distortion, which may influence targets for stereotactic procedures. The aim of this work was to assess methods of identifying target coordinates for stereotactic procedures with MR in multiple phase-encoding directions. In 30 patients undergoing deep brain stimulation, we acquired 5 image sets: stereotactic brain computed tomography (CT), T2-weighted images (T2WI), and T1WI in both right-to-left (RL) and anterior-to-posterior (AP) phase-encoding directions. Using CT coordinates as a reference, we analyzed anterior commissure and posterior commissure coordinates to identify any distortion relating to phase-encoding direction. Compared with CT coordinates, RL-directed images had more positive x-axis values (0.51 mm in T1WI, 0.58 mm in T2WI). AP-directed images had more negative y-axis values (0.44 mm in T1WI, 0.59 mm in T2WI). We adopted 2 methods to predict CT coordinates with MR image sets: parallel translation and selective choice of axes according to phase-encoding direction. Both were equally effective at predicting CT coordinates using only MR; however, the latter may be easier to use in clinical settings. Acquiring MR in multiple phase-encoding directions and selecting axes according to the phase-encoding direction allows identification of more accurate coordinates for stereotactic procedures. © 2018 S. Karger AG, Basel.

  5. The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration

    Science.gov (United States)

    Saenz, Daniel L.; Kim, Hojin; Chen, Josephine; Stathakis, Sotirios; Kirby, Neil

    2016-09-01

    The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu’s method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited a persisting difference in accuracy between the discrete images and the unprocessed image pair. A minimum detail of three levels allows a realistic patient proxy for use with Velocity and MIM deformation algorithms.

  6. Deformable meshes for medical image segmentation accurate automatic segmentation of anatomical structures

    CERN Document Server

    Kainmueller, Dagmar

    2014-01-01

    ? Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatom

  7. SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

    International Nuclear Information System (INIS)

    Jia, J; Cao, R; Pei, X; Wang, H; Hu, L

    2015-01-01

    Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy

  8. SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Jia, J; Cao, R; Pei, X; Wang, H; Hu, L [Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, Anhui, 230031 (China); Engineering Technology Research Center of Accurate Radiotherapy of Anhui Province, Hefei 230031 (China); Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, SuZhou (China)

    2015-06-15

    Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy.

  9. Towards cycle-accurate performance predictions for real-time embedded systems

    NARCIS (Netherlands)

    Triantafyllidis, K.; Bondarev, E.; With, de P.H.N.; Arabnia, H.R.; Deligiannidis, L.; Jandieri, G.

    2013-01-01

    In this paper we present a model-based performance analysis method for component-based real-time systems, featuring cycle-accurate predictions of latencies and enhanced system robustness. The method incorporates the following phases: (a) instruction-level profiling of SW components, (b) modeling the

  10. Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

    Science.gov (United States)

    Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

    2018-02-01

    Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

  11. PREDICTION OF SOLAR FLARES USING UNIQUE SIGNATURES OF MAGNETIC FIELD IMAGES

    Energy Technology Data Exchange (ETDEWEB)

    Raboonik, Abbas; Safari, Hossein; Alipour, Nasibe [Department of Physics, University of Zanjan, P.O. Box 45195-313, Zanjan (Iran, Islamic Republic of); Wheatland, Michael S., E-mail: raboonik@alumni.znu.ac.ir, E-mail: safari@znu.ac.ir [Sydney Institute for Astronomy, School of Physics, The University of Sydney, NSW 2006 (Australia)

    2017-01-01

    Prediction of solar flares is an important task in solar physics. The occurrence of solar flares is highly dependent on the structure and topology of solar magnetic fields. A new method for predicting large (M- and X-class) flares is presented, which uses machine learning methods applied to the Zernike moments (ZM) of magnetograms observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for a period of six years from 2010 June 2 to 2016 August 1. Magnetic field images consisting of the radial component of the magnetic field are converted to finite sets of ZMs and fed to the support vector machine classifier. ZMs have the capability to elicit unique features from any 2D image, which may allow more accurate classification. The results indicate whether an arbitrary active region has the potential to produce at least one large flare. We show that the majority of large flares can be predicted within 48 hr before their occurrence, with only 10 false negatives out of 385 flaring active region magnetograms and 21 false positives out of 179 non-flaring active region magnetograms. Our method may provide a useful tool for the prediction of solar flares, which can be employed alongside other forecasting methods.

  12. Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

    Energy Technology Data Exchange (ETDEWEB)

    Voisin, Sophie [ORNL; Pinto, Frank M [ORNL; Morin-Ducote, Garnetta [University of Tennessee, Knoxville (UTK); Hudson, Kathy [University of Tennessee, Knoxville (UTK); Tourassi, Georgia [ORNL

    2013-01-01

    Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADs images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.

  13. Development of anatomically and dielectrically accurate breast phantoms for microwave imaging applications

    Science.gov (United States)

    O'Halloran, M.; Lohfeld, S.; Ruvio, G.; Browne, J.; Krewer, F.; Ribeiro, C. O.; Inacio Pita, V. C.; Conceicao, R. C.; Jones, E.; Glavin, M.

    2014-05-01

    Breast cancer is one of the most common cancers in women. In the United States alone, it accounts for 31% of new cancer cases, and is second only to lung cancer as the leading cause of deaths in American women. More than 184,000 new cases of breast cancer are diagnosed each year resulting in approximately 41,000 deaths. Early detection and intervention is one of the most significant factors in improving the survival rates and quality of life experienced by breast cancer sufferers, since this is the time when treatment is most effective. One of the most promising breast imaging modalities is microwave imaging. The physical basis of active microwave imaging is the dielectric contrast between normal and malignant breast tissue that exists at microwave frequencies. The dielectric contrast is mainly due to the increased water content present in the cancerous tissue. Microwave imaging is non-ionizing, does not require breast compression, is less invasive than X-ray mammography, and is potentially low cost. While several prototype microwave breast imaging systems are currently in various stages of development, the design and fabrication of anatomically and dielectrically representative breast phantoms to evaluate these systems is often problematic. While some existing phantoms are composed of dielectrically representative materials, they rarely accurately represent the shape and size of a typical breast. Conversely, several phantoms have been developed to accurately model the shape of the human breast, but have inappropriate dielectric properties. This study will brie y review existing phantoms before describing the development of a more accurate and practical breast phantom for the evaluation of microwave breast imaging systems.

  14. Hounsfield unit density accurately predicts ESWL success.

    Science.gov (United States)

    Magnuson, William J; Tomera, Kevin M; Lance, Raymond S

    2005-01-01

    Extracorporeal shockwave lithotripsy (ESWL) is a commonly used non-invasive treatment for urolithiasis. Helical CT scans provide much better and detailed imaging of the patient with urolithiasis including the ability to measure density of urinary stones. In this study we tested the hypothesis that density of urinary calculi as measured by CT can predict successful ESWL treatment. 198 patients were treated at Alaska Urological Associates with ESWL between January 2002 and April 2004. Of these 101 met study inclusion with accessible CT scans and stones ranging from 5-15 mm. Follow-up imaging demonstrated stone freedom in 74.2%. The overall mean Houndsfield density value for stone-free compared to residual stone groups were significantly different ( 93.61 vs 122.80 p ESWL for upper tract calculi between 5-15mm.

  15. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2008-01-01

    Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate...

  16. Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure.

    Science.gov (United States)

    Scribbans, T D; Berg, K; Narazaki, K; Janssen, I; Gurd, B J

    2015-09-01

    There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate (HR) during intermittent sport. The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost (VO2) and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males (19.9±1.1yrs) were recruited to establish the relationship between %VO2peak and %HRmax during low-intensity steady state endurance (END), moderate-intensity interval (MOD) and high intensity-interval exercise (HI), as performed on a cycle ergometer. Three equations (END, MOD, and HI) for predicting %VO2peak based on %HRmax were developed. HR and VO2 were directly measured during basketball games (6 male, 20.8±1.0 yrs; 6 female, 20.0±1.3yrs) and volleyball drills (12 female; 20.8±1.0yrs). Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations. The END and MOD equations accurately predicted VO2 and energy expenditure, while the HI equation underestimated, and the previously published equations systematically overestimated VO2 and energy expenditure. Intermittent sport VO2 and energy expenditure can be accurately predicted from heart rate data using either the END (%VO2peak=%HRmax x 1.008-17.17) or MOD (%VO2peak=%HRmax x 1.2-32) equations. These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport.

  17. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.

    Directory of Open Access Journals (Sweden)

    Jaroslav Bendl

    2014-01-01

    Full Text Available Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

  18. A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities

    Directory of Open Access Journals (Sweden)

    Wenqiong Xue

    2018-03-01

    Full Text Available Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biologic measures. We propose a Bayesian hierarchical model to predict disease status, which is able to incorporate information from both functional and structural brain imaging scans. We consider a two-stage whole brain parcellation, partitioning the brain into 282 subregions, and our model accounts for correlations between voxels from different brain regions defined by the parcellations. Our approach models the imaging data and uses posterior predictive probabilities to perform prediction. The estimates of our model parameters are based on samples drawn from the joint posterior distribution using Markov Chain Monte Carlo (MCMC methods. We evaluate our method by examining the prediction accuracy rates based on leave-one-out cross validation, and we employ an importance sampling strategy to reduce the computation time. We conduct both whole-brain and voxel-level prediction and identify the brain regions that are highly associated with the disease based on the voxel-level prediction results. We apply our model to multimodal brain imaging data from a study of Parkinson's disease. We achieve extremely high accuracy, in general, and our model identifies key regions contributing to accurate prediction including caudate, putamen, and fusiform gyrus as well as several sensory system regions.

  19. A new, accurate predictive model for incident hypertension.

    Science.gov (United States)

    Völzke, Henry; Fung, Glenn; Ittermann, Till; Yu, Shipeng; Baumeister, Sebastian E; Dörr, Marcus; Lieb, Wolfgang; Völker, Uwe; Linneberg, Allan; Jørgensen, Torben; Felix, Stephan B; Rettig, Rainer; Rao, Bharat; Kroemer, Heyo K

    2013-11-01

    Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures. The primary study population consisted of 1605 normotensive individuals aged 20-79 years with 5-year follow-up from the population-based study, that is the Study of Health in Pomerania (SHIP). The initial set was randomly split into a training and a testing set. We used a probabilistic graphical model applying a Bayesian network to create a predictive model for incident hypertension and compared the predictive performance with the established Framingham risk score for hypertension. Finally, the model was validated in 2887 participants from INTER99, a Danish community-based intervention study. In the training set of SHIP data, the Bayesian network used a small subset of relevant baseline features including age, mean arterial pressure, rs16998073, serum glucose and urinary albumin concentrations. Furthermore, we detected relevant interactions between age and serum glucose as well as between rs16998073 and urinary albumin concentrations [area under the receiver operating characteristic (AUC 0.76)]. The model was confirmed in the SHIP validation set (AUC 0.78) and externally replicated in INTER99 (AUC 0.77). Compared to the established Framingham risk score for hypertension, the predictive performance of the new model was similar in the SHIP validation set and moderately better in INTER99. Data mining procedures identified a predictive model for incident hypertension, which included innovative and easy-to-measure variables. The findings promise great applicability in screening settings and clinical practice.

  20. Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

    International Nuclear Information System (INIS)

    Voisin, Sophie; Tourassi, Georgia D.; Pinto, Frank; Morin-Ducote, Garnetta; Hudson, Kathleen B.

    2013-01-01

    Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content

  1. Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

    Energy Technology Data Exchange (ETDEWEB)

    Voisin, Sophie; Tourassi, Georgia D. [Biomedical Science and Engineering Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Pinto, Frank [School of Engineering, Science, and Technology, Virginia State University, Petersburg, Virginia 23806 (United States); Morin-Ducote, Garnetta; Hudson, Kathleen B. [Department of Radiology, University of Tennessee Medical Center at Knoxville, Knoxville, Tennessee 37920 (United States)

    2013-10-15

    Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.

  2. The prognostic and predictive value of sstr_2-immunohistochemistry and sstr_2-targeted imaging in neuroendocrine tumors

    International Nuclear Information System (INIS)

    Brunner, Philippe; Joerg, Ann-Catherine; Mueller-Brand, Jan; Glatz, Katharina; Bubendorf, Lukas; Radojewski, Piotr; Umlauft, Maria; Spanjol, Petar-Marko; Krause, Thomas; Dumont, Rebecca A.; Walter, Martin A.; Marincek, Nicolas; Maecke, Helmut R.; Briel, Matthias; Schmitt, Anja; Perren, Aurel

    2017-01-01

    Our aim was to assess the prognostic and predictive value of somatostatin receptor 2 (sstr_2) in neuroendocrine tumors (NETs). We established a tissue microarray and imaging database from NET patients that received sstr_2-targeted radiopeptide therapy with yttrium-90-DOTATOC, lutetium-177-DOTATOC or alternative treatment. We used univariate and multivariate analyses to identify prognostic and predictive markers for overall survival, including sstr_2-imaging and sstr_2-immunohistochemistry. We included a total of 279 patients. In these patients, sstr_2-immunohistochemistry was an independent prognostic marker for overall survival (HR: 0.82, 95 % CI: 0.67 - 0.99, n = 279, p = 0.037). In DOTATOC patients, sstr_2-expression on immunohistochemistry correlated with tumor uptake on sstr_2-imaging (n = 170, p < 0.001); however, sstr_2-imaging showed a higher prognostic accuracy (positive predictive value: +27 %, 95 % CI: 3 - 56 %, p = 0.025). Sstr_2-expression did not predict a benefit of DOTATOC over alternative treatment (p = 0.93). Our results suggest sstr_2 as an independent prognostic marker in NETs. Sstr_2-immunohistochemistry correlates with sstr_2-imaging; however, sstr_2-imaging is more accurate for determining the individual prognosis. (orig.)

  3. PROCEDURES FOR ACCURATE PRODUCTION OF COLOR IMAGES FROM SATELLITE OR AIRCRAFT MULTISPECTRAL DIGITAL DATA.

    Science.gov (United States)

    Duval, Joseph S.

    1985-01-01

    Because the display and interpretation of satellite and aircraft remote-sensing data make extensive use of color film products, accurate reproduction of the color images is important. To achieve accurate color reproduction, the exposure and chemical processing of the film must be monitored and controlled. By using a combination of sensitometry, densitometry, and transfer functions that control film response curves, all of the different steps in the making of film images can be monitored and controlled. Because a sensitometer produces a calibrated exposure, the resulting step wedge can be used to monitor the chemical processing of the film. Step wedges put on film by image recording machines provide a means of monitoring the film exposure and color balance of the machines.

  4. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.

    Science.gov (United States)

    Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

    2015-07-07

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  5. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

    Directory of Open Access Journals (Sweden)

    Xin Deng

    2015-07-01

    Full Text Available Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  6. Multi-fidelity machine learning models for accurate bandgap predictions of solids

    International Nuclear Information System (INIS)

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    2016-01-01

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.

  7. Rapid and accurate prediction and scoring of water molecules in protein binding sites.

    Directory of Open Access Journals (Sweden)

    Gregory A Ross

    Full Text Available Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

  8. Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image

    Directory of Open Access Journals (Sweden)

    Zelang Miao

    2015-01-01

    Full Text Available The extraction of road centerline from the classified image is a fundamental image analysis technology. Common problems encountered in road centerline extraction include low ability for coping with the general case, production of undesired objects, and inefficiency. To tackle these limitations, this paper presents a novel accurate centerline extraction method using Gaussian mixture model (GMM and subspace constraint mean shift (SCMS. The proposed method consists of three main steps. GMM is first used to partition the classified image into several clusters. The major axis of the ellipsoid of each cluster is extracted and deemed to be taken as the initial centerline. Finally, the initial result is adjusted using SCMS to produce precise road centerline. Both simulated and real datasets are used to validate the proposed method. Preliminary results demonstrate that the proposed method provides a comparatively robust solution for accurate centerline extraction from a classified image.

  9. MO-AB-BRA-10: Cancer Therapy Outcome Prediction Based On Dempster-Shafer Theory and PET Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Lian, C [Sorbonne University, University of Technology of Compiegne, CNRS, UMR 7253 Heudiasyc, 60205 Compiegne (France); University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen (France); Li, H; Chen, H; Robinson, C. [Washington University School of Medicine, Saint Louis, MO (United States); Denoeux, T [Sorbonne University, University of Technology of Compiegne, CNRS, UMR 7253 Heudiasyc, 60205 Compiegne (France); Vera, P [Centre Henri-Becquerel, 76038 Rouen (France); University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen (France); Ruan, S [University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen (France)

    2015-06-15

    Purpose: In cancer therapy, utilizing FDG-18 PET image-based features for accurate outcome prediction is challenging because of 1) limited discriminative information within a small number of PET image sets, and 2) fluctuant feature characteristics caused by the inferior spatial resolution and system noise of PET imaging. In this study, we proposed a new Dempster-Shafer theory (DST) based approach, evidential low-dimensional transformation with feature selection (ELT-FS), to accurately predict cancer therapy outcome with both PET imaging features and clinical characteristics. Methods: First, a specific loss function with sparse penalty was developed to learn an adaptive low-rank distance metric for representing the dissimilarity between different patients’ feature vectors. By minimizing this loss function, a linear low-dimensional transformation of input features was achieved. Also, imprecise features were excluded simultaneously by applying a l2,1-norm regularization of the learnt dissimilarity metric in the loss function. Finally, the learnt dissimilarity metric was applied in an evidential K-nearest-neighbor (EK- NN) classifier to predict treatment outcome. Results: Twenty-five patients with stage II–III non-small-cell lung cancer and thirty-six patients with esophageal squamous cell carcinomas treated with chemo-radiotherapy were collected. For the two groups of patients, 52 and 29 features, respectively, were utilized. The leave-one-out cross-validation (LOOCV) protocol was used for evaluation. Compared to three existing linear transformation methods (PCA, LDA, NCA), the proposed ELT-FS leads to higher prediction accuracy for the training and testing sets both for lung-cancer patients (100+/−0.0, 88.0+/−33.17) and for esophageal-cancer patients (97.46+/−1.64, 83.33+/−37.8). The ELT-FS also provides superior class separation in both test data sets. Conclusion: A novel DST- based approach has been proposed to predict cancer treatment outcome using PET

  10. Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks.

    Science.gov (United States)

    Sun, X; Chen, K J; Berg, E P; Newman, D J; Schwartz, C A; Keller, W L; Maddock Carlin, K R

    2014-02-01

    The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n = 102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat. © 2013.

  11. Predictive images of postoperative levator resection outcome using image processing software.

    Science.gov (United States)

    Mawatari, Yuki; Fukushima, Mikiko

    2016-01-01

    This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection. Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller's muscle complex (levator resection). Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop ® ). Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery. Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2%) were satisfied with their postoperative appearances, and 55 patients (84.8%) positively responded to the usefulness of processed images to predict postoperative appearance. Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery.

  12. Differential contribution of visual and auditory information to accurately predict the direction and rotational motion of a visual stimulus.

    Science.gov (United States)

    Park, Seoung Hoon; Kim, Seonjin; Kwon, MinHyuk; Christou, Evangelos A

    2016-03-01

    Vision and auditory information are critical for perception and to enhance the ability of an individual to respond accurately to a stimulus. However, it is unknown whether visual and auditory information contribute differentially to identify the direction and rotational motion of the stimulus. The purpose of this study was to determine the ability of an individual to accurately predict the direction and rotational motion of the stimulus based on visual and auditory information. In this study, we recruited 9 expert table-tennis players and used table-tennis service as our experimental model. Participants watched recorded services with different levels of visual and auditory information. The goal was to anticipate the direction of the service (left or right) and the rotational motion of service (topspin, sidespin, or cut). We recorded their responses and quantified the following outcomes: (i) directional accuracy and (ii) rotational motion accuracy. The response accuracy was the accurate predictions relative to the total number of trials. The ability of the participants to predict the direction of the service accurately increased with additional visual information but not with auditory information. In contrast, the ability of the participants to predict the rotational motion of the service accurately increased with the addition of auditory information to visual information but not with additional visual information alone. In conclusion, this finding demonstrates that visual information enhances the ability of an individual to accurately predict the direction of the stimulus, whereas additional auditory information enhances the ability of an individual to accurately predict the rotational motion of stimulus.

  13. Magnetic resonance imaging of injuries to the ankle joint: can it predict clinical outcome?

    Science.gov (United States)

    Zanetti, M; De Simoni, C; Wetz, H H; Zollinger, H; Hodler, J

    1997-02-01

    To predict clinical outcome after ankle sprains on the basis of magnetic resonance (MR) findings. Twenty-nine consecutive patients (mean age 32.9 years, range 13-60 years) were examined clinically and with MR imaging both after trauma and following standardized conservative therapy. Various MR abnormalities were related to a clinical outcome score. There was a tendency for a better clinical outcome in partial, rather than complete, tears of the anterior talofibular ligament and when there was no fluid within the peroneal tendon sheath at the initial MR examination (P = 0.092 for either abnormality). A number of other MR features did not significantly influence clinical outcome, including the presence of a calcaneofibular ligament lesion and a bone bruise of the talar dome. Clinical outcome after ankle sprain cannot consistently be predicted by MR imaging, although MR imaging may be more accurate when the anterior talofibular ligament is only partially torn and there are no signs of injury to the peroneal tendon sheath.

  14. Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.

    Directory of Open Access Journals (Sweden)

    Iris I A Groen

    Full Text Available The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis. Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

  15. Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories

    Science.gov (United States)

    Groen, Iris I. A.; Ghebreab, Sennay; Lamme, Victor A. F.; Scholte, H. Steven

    2012-01-01

    The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task. PMID:23093921

  16. Efficient predictive algorithms for image compression

    CERN Document Server

    Rosário Lucas, Luís Filipe; Maciel de Faria, Sérgio Manuel; Morais Rodrigues, Nuno Miguel; Liberal Pagliari, Carla

    2017-01-01

    This book discusses efficient prediction techniques for the current state-of-the-art High Efficiency Video Coding (HEVC) standard, focusing on the compression of a wide range of video signals, such as 3D video, Light Fields and natural images. The authors begin with a review of the state-of-the-art predictive coding methods and compression technologies for both 2D and 3D multimedia contents, which provides a good starting point for new researchers in the field of image and video compression. New prediction techniques that go beyond the standardized compression technologies are then presented and discussed. In the context of 3D video, the authors describe a new predictive algorithm for the compression of depth maps, which combines intra-directional prediction, with flexible block partitioning and linear residue fitting. New approaches are described for the compression of Light Field and still images, which enforce sparsity constraints on linear models. The Locally Linear Embedding-based prediction method is in...

  17. OSM-Classic : An optical imaging technique for accurately determining strain

    Science.gov (United States)

    Aldrich, Daniel R.; Ayranci, Cagri; Nobes, David S.

    OSM-Classic is a program designed in MATLAB® to provide a method of accurately determining strain in a test sample using an optical imaging technique. Measuring strain for the mechanical characterization of materials is most commonly performed with extensometers, LVDT (linear variable differential transistors), and strain gauges; however, these strain measurement methods suffer from their fragile nature and it is not particularly easy to attach these devices to the material for testing. To alleviate these potential problems, an optical approach that does not require contact with the specimen can be implemented to measure the strain. OSM-Classic is a software that interrogates a series of images to determine elongation in a test sample and hence, strain of the specimen. It was designed to provide a graphical user interface that includes image processing with a dynamic region of interest. Additionally, the stain is calculated directly while providing active feedback during the processing.

  18. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding.

    Science.gov (United States)

    Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

    2017-01-01

    Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

  19. Contribution of diffusion-weighted MR imaging for predicting benignity of complex adnexal masses

    International Nuclear Information System (INIS)

    Thomassin-Naggara, Isabelle; Darai, Emile; Cuenod, Charles A.; Fournier, Laure; Toussaint, Irwin; Marsault, Claude; Bazot, Marc

    2009-01-01

    The purpose of this study was to prospectively assess the contribution of diffusion-weighted MR imaging (DWI) for characterizing complex adnexal masses. Seventy-seven women (22-87 years old) with complex adnexal masses (30 benign and 47 malignant) underwent MR imaging including DWI before surgery. Conventional morphological MR imaging criteria were recorded in addition to b 1,000 signal intensity and apparent diffusion coefficient (ADC) measurements of cystic and solid components. Positive likelihood ratios (PLR) were calculated for predicting benignity and malignancy. The most significant criteria for predicting benignity were low b 1,000 signal intensity within the solid component (PLR = 10.9), low T2 signal intensity within the solid component (PLR = 5.7), absence of solid portion (PLR = 3.1), absence of ascites or peritoneal implants (PLR = 2.3) and absence of papillary projections (PLR = 2.3). ADC measurements did not contribute to differentiating benign from malignant adnexal masses. All masses that displayed simultaneously low signal intensity within the solid component on T2-weighted and on b 1,000 diffusion-weighted images were benign. Alternatively, the presence of a solid component with intermediate T2 signal and high b 1,000 signal intensity was associated with a PLR of 4.5 for a malignant adnexal tumour. DWI signal intensity is an accurate tool for predicting benignity of complex adnexal masses. (orig.)

  20. Contribution of diffusion-weighted MR imaging for predicting benignity of complex adnexal masses

    Energy Technology Data Exchange (ETDEWEB)

    Thomassin-Naggara, Isabelle [Hopital Tenon, Assistance Publique Hopitaux de Pariss, Department of Radiology, Paris (France); Universite Rene Descartes, LRI-EA4062, Paris (France); Darai, Emile [Hopital Tenon, Assistance Publique Hopitaux de Pariss, Department of Gynecology-Obstetrics, Paris (France); Cuenod, Charles A.; Fournier, Laure [Universite Rene Descartes, LRI-EA4062, Paris (France); Hopital Europeen Georges Pompidou (HEGP), Assistance Publique Hopitaux de Paris, Department of Radiology, Paris (France); Toussaint, Irwin; Marsault, Claude; Bazot, Marc [Hopital Tenon, Assistance Publique Hopitaux de Pariss, Department of Radiology, Paris (France)

    2009-06-15

    The purpose of this study was to prospectively assess the contribution of diffusion-weighted MR imaging (DWI) for characterizing complex adnexal masses. Seventy-seven women (22-87 years old) with complex adnexal masses (30 benign and 47 malignant) underwent MR imaging including DWI before surgery. Conventional morphological MR imaging criteria were recorded in addition to b{sub 1,000} signal intensity and apparent diffusion coefficient (ADC) measurements of cystic and solid components. Positive likelihood ratios (PLR) were calculated for predicting benignity and malignancy. The most significant criteria for predicting benignity were low b{sub 1,000} signal intensity within the solid component (PLR = 10.9), low T2 signal intensity within the solid component (PLR = 5.7), absence of solid portion (PLR = 3.1), absence of ascites or peritoneal implants (PLR = 2.3) and absence of papillary projections (PLR = 2.3). ADC measurements did not contribute to differentiating benign from malignant adnexal masses. All masses that displayed simultaneously low signal intensity within the solid component on T2-weighted and on b{sub 1,000} diffusion-weighted images were benign. Alternatively, the presence of a solid component with intermediate T2 signal and high b{sub 1,000} signal intensity was associated with a PLR of 4.5 for a malignant adnexal tumour. DWI signal intensity is an accurate tool for predicting benignity of complex adnexal masses. (orig.)

  1. A Novel Fibrosis Index Comprising a Non-Cholesterol Sterol Accurately Predicts HCV-Related Liver Cirrhosis

    DEFF Research Database (Denmark)

    Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin

    2014-01-01

    of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive...

  2. LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs

    DEFF Research Database (Denmark)

    Will, Sebastian; Joshi, Tejal; Hofacker, Ivo L.

    2012-01-01

    Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing...... methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based...... on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used nc...

  3. Predictive images of postoperative levator resection outcome using image processing software

    Directory of Open Access Journals (Sweden)

    Mawatari Y

    2016-09-01

    Full Text Available Yuki Mawatari,1 Mikiko Fukushima2 1Igo Ophthalmic Clinic, Kagoshima, 2Department of Ophthalmology, Faculty of Life Science, Kumamoto University, Chuo-ku, Kumamoto, Japan Purpose: This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection.Methods: Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller’s muscle complex (levator resection. Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop®. Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery.Results: Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2% were satisfied with their postoperative appearances, and 55 patients (84.8% positively responded to the usefulness of processed images to predict postoperative appearance.Conclusion: Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery. Keywords: levator resection, blepharoptosis, image processing, Adobe Photoshop® 

  4. Do Dual-Route Models Accurately Predict Reading and Spelling Performance in Individuals with Acquired Alexia and Agraphia?

    OpenAIRE

    Rapcsak, Steven Z.; Henry, Maya L.; Teague, Sommer L.; Carnahan, Susan D.; Beeson, Pélagie M.

    2007-01-01

    Coltheart and colleagues (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Castles, Bates, & Coltheart, 2006) have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult...

  5. Towards accurate performance prediction of a vertical axis wind turbine operating at different tip speed ratios

    NARCIS (Netherlands)

    Rezaeiha, A.; Kalkman, I.; Blocken, B.J.E.

    2017-01-01

    Accurate prediction of the performance of a vertical-axis wind turbine (VAWT) using CFD simulation requires the employment of a sufficiently fine azimuthal increment (dθ) combined with a mesh size at which essential flow characteristics can be accurately resolved. Furthermore, the domain size needs

  6. Bayesian calibration of power plant models for accurate performance prediction

    International Nuclear Information System (INIS)

    Boksteen, Sowande Z.; Buijtenen, Jos P. van; Pecnik, Rene; Vecht, Dick van der

    2014-01-01

    Highlights: • Bayesian calibration is applied to power plant performance prediction. • Measurements from a plant in operation are used for model calibration. • A gas turbine performance model and steam cycle model are calibrated. • An integrated plant model is derived. • Part load efficiency is accurately predicted as a function of ambient conditions. - Abstract: Gas turbine combined cycles are expected to play an increasingly important role in the balancing of supply and demand in future energy markets. Thermodynamic modeling of these energy systems is frequently applied to assist in decision making processes related to the management of plant operation and maintenance. In most cases, model inputs, parameters and outputs are treated as deterministic quantities and plant operators make decisions with limited or no regard of uncertainties. As the steady integration of wind and solar energy into the energy market induces extra uncertainties, part load operation and reliability are becoming increasingly important. In the current study, methods are proposed to not only quantify various types of uncertainties in measurements and plant model parameters using measured data, but to also assess their effect on various aspects of performance prediction. The authors aim to account for model parameter and measurement uncertainty, and for systematic discrepancy of models with respect to reality. For this purpose, the Bayesian calibration framework of Kennedy and O’Hagan is used, which is especially suitable for high-dimensional industrial problems. The article derives a calibrated model of the plant efficiency as a function of ambient conditions and operational parameters, which is also accurate in part load. The article shows that complete statistical modeling of power plants not only enhances process models, but can also increases confidence in operational decisions

  7. Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging

    Science.gov (United States)

    Schwedt, Todd J.; Chong, Catherine D.; Wu, Teresa; Gaw, Nathan; Fu, Yinlin; Li, Jing

    2015-01-01

    Background The International Classification of Headache Disorders provides criteria for the diagnosis and subclassification of migraine. Since there is no objective gold standard by which to test these diagnostic criteria, the criteria are based on the consensus opinion of content experts. Accurate migraine classifiers consisting of brain structural measures could serve as an objective gold standard by which to test and revise diagnostic criteria. The objectives of this study were to utilize magnetic resonance imaging measures of brain structure for constructing classifiers: 1) that accurately identify individuals as having chronic vs. episodic migraine vs. being a healthy control; and 2) that test the currently used threshold of 15 headache days/month for differentiating chronic migraine from episodic migraine. Methods Study participants underwent magnetic resonance imaging for determination of regional cortical thickness, cortical surface area, and volume. Principal components analysis combined structural measurements into principal components accounting for 85% of variability in brain structure. Models consisting of these principal components were developed to achieve the classification objectives. Ten-fold cross validation assessed classification accuracy within each of the ten runs, with data from 90% of participants randomly selected for classifier development and data from the remaining 10% of participants used to test classification performance. Headache frequency thresholds ranging from 5–15 headache days/month were evaluated to determine the threshold allowing for the most accurate subclassification of individuals into lower and higher frequency subgroups. Results Participants were 66 migraineurs and 54 healthy controls, 75.8% female, with an average age of 36 +/− 11 years. Average classifier accuracies were: a) 68% for migraine (episodic + chronic) vs. healthy controls; b) 67.2% for episodic migraine vs. healthy controls; c) 86.3% for chronic

  8. Accurate Holdup Calculations with Predictive Modeling & Data Integration

    Energy Technology Data Exchange (ETDEWEB)

    Azmy, Yousry [North Carolina State Univ., Raleigh, NC (United States). Dept. of Nuclear Engineering; Cacuci, Dan [Univ. of South Carolina, Columbia, SC (United States). Dept. of Mechanical Engineering

    2017-04-03

    In facilities that process special nuclear material (SNM) it is important to account accurately for the fissile material that enters and leaves the plant. Although there are many stages and processes through which materials must be traced and measured, the focus of this project is material that is “held-up” in equipment, pipes, and ducts during normal operation and that can accumulate over time into significant quantities. Accurately estimating the holdup is essential for proper SNM accounting (vis-à-vis nuclear non-proliferation), criticality and radiation safety, waste management, and efficient plant operation. Usually it is not possible to directly measure the holdup quantity and location, so these must be inferred from measured radiation fields, primarily gamma and less frequently neutrons. Current methods to quantify holdup, i.e. Generalized Geometry Holdup (GGH), primarily rely on simple source configurations and crude radiation transport models aided by ad hoc correction factors. This project seeks an alternate method of performing measurement-based holdup calculations using a predictive model that employs state-of-the-art radiation transport codes capable of accurately simulating such situations. Inverse and data assimilation methods use the forward transport model to search for a source configuration that best matches the measured data and simultaneously provide an estimate of the level of confidence in the correctness of such configuration. In this work the holdup problem is re-interpreted as an inverse problem that is under-determined, hence may permit multiple solutions. A probabilistic approach is applied to solving the resulting inverse problem. This approach rates possible solutions according to their plausibility given the measurements and initial information. This is accomplished through the use of Bayes’ Theorem that resolves the issue of multiple solutions by giving an estimate of the probability of observing each possible solution. To use

  9. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    Science.gov (United States)

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2017-04-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  10. Learning-based prediction of gestational age from ultrasound images of the fetal brain.

    Science.gov (United States)

    Namburete, Ana I L; Stebbing, Richard V; Kemp, Bryn; Yaqub, Mohammad; Papageorghiou, Aris T; Alison Noble, J

    2015-04-01

    We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction with clinical measurements to develop, for the first time, a predictive age model which improves on the GA-prediction potential of US images. The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position. This allows for fast and efficient sampling of anatomically-corresponding brain regions to achieve like-for-like structural comparison of different developmental stages. We develop bespoke features which capture neurosonographic patterns in 3D images, and using a regression forest classifier, we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population (N=447) over an age range of active brain maturation (18-34weeks). On a routine clinical dataset (N=187) our age prediction results strongly correlate with true GA (r=0.98,accurate within±6.10days), confirming the link between maturational progression and neurosonographic activity observable across gestation. Our model also outperforms current clinical methods by ±4.57 days in the third trimester-a period complicated by biological variations in the fetal population. Through feature selection, the model successfully identified the most age-discriminating anatomies over this age range as being the Sylvian fissure, cingulate, and callosal sulci. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

  11. Accurate prediction of the enthalpies of formation for xanthophylls.

    Science.gov (United States)

    Lii, Jenn-Huei; Liao, Fu-Xing; Hu, Ching-Han

    2011-11-30

    This study investigates the applications of computational approaches in the prediction of enthalpies of formation (ΔH(f)) for C-, H-, and O-containing compounds. Molecular mechanics (MM4) molecular mechanics method, density functional theory (DFT) combined with the atomic equivalent (AE) and group equivalent (GE) schemes, and DFT-based correlation corrected atomization (CCAZ) were used. We emphasized on the application to xanthophylls, C-, H-, and O-containing carotenoids which consist of ∼ 100 atoms and extended π-delocaization systems. Within the training set, MM4 predictions are more accurate than those obtained using AE and GE; however a systematic underestimation was observed in the extended systems. ΔH(f) for the training set molecules predicted by CCAZ combined with DFT are in very good agreement with the G3 results. The average absolute deviations (AADs) of CCAZ combined with B3LYP and MPWB1K are 0.38 and 0.53 kcal/mol compared with the G3 data, and are 0.74 and 0.69 kcal/mol compared with the available experimental data, respectively. Consistency of the CCAZ approach for the selected xanthophylls is revealed by the AAD of 2.68 kcal/mol between B3LYP-CCAZ and MPWB1K-CCAZ. Copyright © 2011 Wiley Periodicals, Inc.

  12. Relative location prediction in CT scan images using convolutional neural networks.

    Science.gov (United States)

    Guo, Jiajia; Du, Hongwei; Zhu, Jianyue; Yan, Ting; Qiu, Bensheng

    2018-07-01

    Relative location prediction in computed tomography (CT) scan images is a challenging problem. Many traditional machine learning methods have been applied in attempts to alleviate this problem. However, the accuracy and speed of these methods cannot meet the requirement of medical scenario. In this paper, we propose a regression model based on one-dimensional convolutional neural networks (CNN) to determine the relative location of a CT scan image both quickly and precisely. In contrast to other common CNN models that use a two-dimensional image as an input, the input of this CNN model is a feature vector extracted by a shape context algorithm with spatial correlation. Normalization via z-score is first applied as a pre-processing step. Then, in order to prevent overfitting and improve model's performance, 20% of the elements of the feature vectors are randomly set to zero. This CNN model consists primarily of three one-dimensional convolutional layers, three dropout layers and two fully-connected layers with appropriate loss functions. A public dataset is employed to validate the performance of the proposed model using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with contemporary techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm. The time taken for each relative location prediction is approximately 2 ms. Results indicate that the proposed CNN method can contribute to a quick and accurate relative location prediction in CT scan images, which can improve efficiency of the medical picture archiving and communication system in the future. Copyright © 2018 Elsevier B.V. All rights reserved.

  13. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

    Directory of Open Access Journals (Sweden)

    Yong-Bi Fu

    2017-07-01

    Full Text Available Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

  14. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

    Science.gov (United States)

    Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

    2017-01-01

    Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

  15. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

    Science.gov (United States)

    Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

    2013-12-23

    Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

  16. Accurate reconstruction of hyperspectral images from compressive sensing measurements

    Science.gov (United States)

    Greer, John B.; Flake, J. C.

    2013-05-01

    The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.

  17. Iterative feature refinement for accurate undersampled MR image reconstruction

    Science.gov (United States)

    Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong

    2016-05-01

    Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.

  18. Iterative feature refinement for accurate undersampled MR image reconstruction

    International Nuclear Information System (INIS)

    Wang, Shanshan; Liu, Jianbo; Liu, Xin; Zheng, Hairong; Liang, Dong; Liu, Qiegen; Ying, Leslie

    2016-01-01

    Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches. (paper)

  19. Contributions to HEVC Prediction for Medical Image Compression

    OpenAIRE

    Guarda, André Filipe Rodrigues

    2016-01-01

    Medical imaging technology and applications are continuously evolving, dealing with images of increasing spatial and temporal resolutions, which allow easier and more accurate medical diagnosis. However, this increase in resolution demands a growing amount of data to be stored and transmitted. Despite the high coding efficiency achieved by the most recent image and video coding standards in lossy compression, they are not well suited for quality-critical medical image compressi...

  20. The prognostic and predictive value of sstr{sub 2}-immunohistochemistry and sstr{sub 2}-targeted imaging in neuroendocrine tumors

    Energy Technology Data Exchange (ETDEWEB)

    Brunner, Philippe [University Hospital Basel, Institute of Pathology (Switzerland); University Hospital Basel, Institute of Nuclear Medicine (Switzerland); Joerg, Ann-Catherine; Mueller-Brand, Jan [University Hospital Basel, Institute of Nuclear Medicine (Switzerland); Glatz, Katharina; Bubendorf, Lukas [University Hospital Basel, Institute of Pathology (Switzerland); Radojewski, Piotr; Umlauft, Maria; Spanjol, Petar-Marko; Krause, Thomas; Dumont, Rebecca A.; Walter, Martin A. [University Hospital Bern, Institute of Nuclear Medicine (Switzerland); Marincek, Nicolas [University Hospital Basel, Institute of Nuclear Medicine (Switzerland); University Hospital Bern, Institute of Nuclear Medicine (Switzerland); Maecke, Helmut R. [University Hospital Basel, Division of Radiological Chemistry (Switzerland); Briel, Matthias [University Hospital Basel, Basel Institute for Clinical Epidemiology and Biostatistics (Switzerland); McMaster University, Department of Clinical Epidemiology and Biostatistics, Hamilton (Canada); Schmitt, Anja; Perren, Aurel [University Bern, Institute of Pathology, Bern (Switzerland)

    2017-03-15

    Our aim was to assess the prognostic and predictive value of somatostatin receptor 2 (sstr{sub 2}) in neuroendocrine tumors (NETs). We established a tissue microarray and imaging database from NET patients that received sstr{sub 2}-targeted radiopeptide therapy with yttrium-90-DOTATOC, lutetium-177-DOTATOC or alternative treatment. We used univariate and multivariate analyses to identify prognostic and predictive markers for overall survival, including sstr{sub 2}-imaging and sstr{sub 2}-immunohistochemistry. We included a total of 279 patients. In these patients, sstr{sub 2}-immunohistochemistry was an independent prognostic marker for overall survival (HR: 0.82, 95 % CI: 0.67 - 0.99, n = 279, p = 0.037). In DOTATOC patients, sstr{sub 2}-expression on immunohistochemistry correlated with tumor uptake on sstr{sub 2}-imaging (n = 170, p < 0.001); however, sstr{sub 2}-imaging showed a higher prognostic accuracy (positive predictive value: +27 %, 95 % CI: 3 - 56 %, p = 0.025). Sstr{sub 2}-expression did not predict a benefit of DOTATOC over alternative treatment (p = 0.93). Our results suggest sstr{sub 2} as an independent prognostic marker in NETs. Sstr{sub 2}-immunohistochemistry correlates with sstr{sub 2}-imaging; however, sstr{sub 2}-imaging is more accurate for determining the individual prognosis. (orig.)

  1. Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models

    Directory of Open Access Journals (Sweden)

    Aeriel Belk

    2018-02-01

    Full Text Available Death investigations often include an effort to establish the postmortem interval (PMI in cases in which the time of death is uncertain. The postmortem interval can lead to the identification of the deceased and the validation of witness statements and suspect alibis. Recent research has demonstrated that microbes provide an accurate clock that starts at death and relies on ecological change in the microbial communities that normally inhabit a body and its surrounding environment. Here, we explore how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types (gravesoil, skin of the torso, skin of the head, gene markers (16S ribosomal RNA (rRNA, 18S rRNA, internal transcribed spacer regions (ITS, and taxonomic levels (sequence variants, species, genus, etc.. We also tested whether particular suites of indicator microbes were informative across different datasets. Generally, results indicate that the most accurate models for predicting PMI were built using gravesoil and skin data using the 16S rRNA genetic marker at the taxonomic level of phyla. Additionally, several phyla consistently contributed highly to model accuracy and may be candidate indicators of PMI.

  2. Accurate estimation of motion blur parameters in noisy remote sensing image

    Science.gov (United States)

    Shi, Xueyan; Wang, Lin; Shao, Xiaopeng; Wang, Huilin; Tao, Zhong

    2015-05-01

    The relative motion between remote sensing satellite sensor and objects is one of the most common reasons for remote sensing image degradation. It seriously weakens image data interpretation and information extraction. In practice, point spread function (PSF) should be estimated firstly for image restoration. Identifying motion blur direction and length accurately is very crucial for PSF and restoring image with precision. In general, the regular light-and-dark stripes in the spectrum can be employed to obtain the parameters by using Radon transform. However, serious noise existing in actual remote sensing images often causes the stripes unobvious. The parameters would be difficult to calculate and the error of the result relatively big. In this paper, an improved motion blur parameter identification method to noisy remote sensing image is proposed to solve this problem. The spectrum characteristic of noisy remote sensing image is analyzed firstly. An interactive image segmentation method based on graph theory called GrabCut is adopted to effectively extract the edge of the light center in the spectrum. Motion blur direction is estimated by applying Radon transform on the segmentation result. In order to reduce random error, a method based on whole column statistics is used during calculating blur length. Finally, Lucy-Richardson algorithm is applied to restore the remote sensing images of the moon after estimating blur parameters. The experimental results verify the effectiveness and robustness of our algorithm.

  3. Towards Accurate Prediction of Unbalance Response, Oil Whirl and Oil Whip of Flexible Rotors Supported by Hydrodynamic Bearings

    Directory of Open Access Journals (Sweden)

    Rob Eling

    2016-09-01

    Full Text Available Journal bearings are used to support rotors in a wide range of applications. In order to ensure reliable operation, accurate analyses of these rotor-bearing systems are crucial. Coupled analysis of the rotor and the journal bearing is essential in the case that the rotor is flexible. The accuracy of prediction of the model at hand depends on its comprehensiveness. In this study, we construct three bearing models of increasing modeling comprehensiveness and use these to predict the response of two different rotor-bearing systems. The main goal is to evaluate the correlation with measurement data as a function of modeling comprehensiveness: 1D versus 2D pressure prediction, distributed versus lumped thermal model, Newtonian versus non-Newtonian fluid description and non-mass-conservative versus mass-conservative cavitation description. We conclude that all three models predict the existence of critical speeds and whirl for both rotor-bearing systems. However, the two more comprehensive models in general show better correlation with measurement data in terms of frequency and amplitude. Furthermore, we conclude that a thermal network model comprising temperature predictions of the bearing surroundings is essential to obtain accurate predictions. The results of this study aid in developing accurate and computationally-efficient models of flexible rotors supported by plain journal bearings.

  4. Accurate localization of intracavitary brachytherapy applicators from 3D CT imaging studies

    International Nuclear Information System (INIS)

    Lerma, F.A.; Williamson, J.F.

    2002-01-01

    Purpose: To present an accurate method to identify the positions and orientations of intracavitary (ICT) brachytherapy applicators imaged in 3D CT scans, in support of Monte Carlo photon-transport simulations, enabling accurate dose modeling in the presence of applicator shielding and interapplicator attenuation. Materials and methods: The method consists of finding the transformation that maximizes the coincidence between the known 3D shapes of each applicator component (colpostats and tandem) with the volume defined by contours of the corresponding surface on each CT slice. We use this technique to localize Fletcher-Suit CT-compatible applicators for three cervix cancer patients using post-implant CT examinations (3 mm slice thickness and separation). Dose distributions in 1-to-1 registration with the underlying CT anatomy are derived from 3D Monte Carlo photon-transport simulations incorporating each applicator's internal geometry (source encapsulation, high-density shields, and applicator body) oriented in relation to the dose matrix according to the measured localization transformations. The precision and accuracy of our localization method are assessed using CT scans, in which the positions and orientations of dense rods and spheres (in a precision-machined phantom) were measured at various orientations relative to the gantry. Results: Using this method, we register 3D Monte Carlo dose calculations directly onto post insertion patient CT studies. Using CT studies of a precisely machined phantom, the absolute accuracy of the method was found to be ±0.2 mm in plane, and ±0.3 mm in the axial direction while its precision was ±0.2 mm in plane, and ±0.2 mm axially. Conclusion: We have developed a novel, and accurate technique to localize intracavitary brachytherapy applicators in 3D CT imaging studies, which supports 3D dose planning involving detailed 3D Monte Carlo dose calculations, modeling source positions, shielding and interapplicator shielding

  5. Accurate wavelength prediction of photonic crystal resonant reflection and applications in refractive index measurement

    DEFF Research Database (Denmark)

    Hermannsson, Pétur Gordon; Vannahme, Christoph; Smith, Cameron L. C.

    2014-01-01

    and superstrate materials. The importance of accounting for material dispersion in order to obtain accurate simulation results is highlighted, and a method for doing so using an iterative approach is demonstrated. Furthermore, an application for the model is demonstrated, in which the material dispersion......In the past decade, photonic crystal resonant reflectors have been increasingly used as the basis for label-free biochemical assays in lab-on-a-chip applications. In both designing and interpreting experimental results, an accurate model describing the optical behavior of such structures...... is essential. Here, an analytical method for precisely predicting the absolute positions of resonantly reflected wavelengths is presented. The model is experimentally verified to be highly accurate using nanoreplicated, polymer-based photonic crystal grating reflectors with varying grating periods...

  6. Predicted osteotomy planes are accurate when using patient-specific instrumentation for total knee arthroplasty in cadavers: a descriptive analysis.

    Science.gov (United States)

    Kievit, A J; Dobbe, J G G; Streekstra, G J; Blankevoort, L; Schafroth, M U

    2018-06-01

    Malalignment of implants is a major source of failure during total knee arthroplasty. To achieve more accurate 3D planning and execution of the osteotomy cuts during surgery, the Signature (Biomet, Warsaw) patient-specific instrumentation (PSI) was used to produce pin guides for the positioning of the osteotomy blocks by means of computer-aided manufacture based on CT scan images. The research question of this study is: what is the transfer accuracy of osteotomy planes predicted by the Signature PSI system for preoperative 3D planning and intraoperative block-guided pin placement to perform total knee arthroplasty procedures? The transfer accuracy achieved by using the Signature PSI system was evaluated by comparing the osteotomy planes predicted preoperatively with the osteotomy planes seen intraoperatively in human cadaveric legs. Outcomes were measured in terms of translational and rotational errors (varus, valgus, flexion, extension and axial rotation) for both tibia and femur osteotomies. Average translational errors between the osteotomy planes predicted using the Signature system and the actual osteotomy planes achieved was 0.8 mm (± 0.5 mm) for the tibia and 0.7 mm (± 4.0 mm) for the femur. Average rotational errors in relation to predicted and achieved osteotomy planes were 0.1° (± 1.2°) of varus and 0.4° (± 1.7°) of anterior slope (extension) for the tibia, and 2.8° (± 2.0°) of varus and 0.9° (± 2.7°) of flexion and 1.4° (± 2.2°) of external rotation for the femur. The similarity between osteotomy planes predicted using the Signature system and osteotomy planes actually achieved was excellent for the tibia although some discrepancies were seen for the femur. The use of 3D system techniques in TKA surgery can provide accurate intraoperative guidance, especially for patients with deformed bone, tailored to individual patients and ensure better placement of the implant.

  7. Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria

    Directory of Open Access Journals (Sweden)

    Sato Yoshiharu

    2011-11-01

    Full Text Available Abstract Background Many pathogens use a type III secretion system to translocate virulence proteins (called effectors in order to adapt to the host environment. To date, many prediction tools for effector identification have been developed. However, these tools are insufficiently accurate for producing a list of putative effectors that can be applied directly for labor-intensive experimental verification. This also suggests that important features of effectors have yet to be fully characterized. Results In this study, we have constructed an accurate approach to predicting secreted virulence effectors from Gram-negative bacteria. This consists of a support vector machine-based discriminant analysis followed by a simple criteria-based filtering. The accuracy was assessed by estimating the average number of true positives in the top-20 ranking in the genome-wide screening. In the validation, 10 sets of 20 training and 20 testing examples were randomly selected from 40 known effectors of Salmonella enterica serovar Typhimurium LT2. On average, the SVM portion of our system predicted 9.7 true positives from 20 testing examples in the top-20 of the prediction. Removal of the N-terminal instability, codon adaptation index and ProtParam indices decreased the score to 7.6, 8.9 and 7.9, respectively. These discrimination features suggested that the following characteristics of effectors had been uncovered: unstable N-terminus, non-optimal codon usage, hydrophilic, and less aliphathic. The secondary filtering process represented by coexpression analysis and domain distribution analysis further refined the average true positive counts to 12.3. We further confirmed that our system can correctly predict known effectors of P. syringae DC3000, strongly indicating its feasibility. Conclusions We have successfully developed an accurate prediction system for screening effectors on a genome-wide scale. We confirmed the accuracy of our system by external validation

  8. Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

    Science.gov (United States)

    Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

    2016-03-01

    Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.

  9. Artificial neural network prediction of ischemic tissue fate in acute stroke imaging

    Science.gov (United States)

    Huang, Shiliang; Shen, Qiang; Duong, Timothy Q

    2010-01-01

    Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin–spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBF+ADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis. PMID:20424631

  10. Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit.

    Science.gov (United States)

    Liu, Changhong; Liu, Wei; Chen, Wei; Yang, Jianbo; Zheng, Lei

    2015-04-15

    Tomato is an important health-stimulating fruit because of the antioxidant properties of its main bioactive compounds, dominantly lycopene and phenolic compounds. Nowadays, product differentiation in the fruit market requires an accurate evaluation of these value-added compounds. An experiment was conducted to simultaneously and non-destructively measure lycopene and phenolic compounds content in intact tomatoes using multispectral imaging combined with chemometric methods. Partial least squares (PLS), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) were applied to develop quantitative models. Compared with PLS and LS-SVM, BPNN model considerably improved the performance with coefficient of determination in prediction (RP(2))=0.938 and 0.965, residual predictive deviation (RPD)=4.590 and 9.335 for lycopene and total phenolics content prediction, respectively. It is concluded that multispectral imaging is an attractive alternative to the standard methods for determination of bioactive compounds content in intact tomatoes, providing a useful platform for infield fruit sorting/grading. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

    Energy Technology Data Exchange (ETDEWEB)

    Visel, Axel; Blow, Matthew J.; Li, Zirong; Zhang, Tao; Akiyama, Jennifer A.; Holt, Amy; Plajzer-Frick, Ingrid; Shoukry, Malak; Wright, Crystal; Chen, Feng; Afzal, Veena; Ren, Bing; Rubin, Edward M.; Pennacchio, Len A.

    2009-02-01

    A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.

  12. Color image lossy compression based on blind evaluation and prediction of noise characteristics

    Science.gov (United States)

    Ponomarenko, Nikolay N.; Lukin, Vladimir V.; Egiazarian, Karen O.; Lepisto, Leena

    2011-03-01

    The paper deals with JPEG adaptive lossy compression of color images formed by digital cameras. Adaptation to noise characteristics and blur estimated for each given image is carried out. The dominant factor degrading image quality is determined in a blind manner. Characteristics of this dominant factor are then estimated. Finally, a scaling factor that determines quantization steps for default JPEG table is adaptively set (selected). Within this general framework, two possible strategies are considered. A first one presumes blind estimation for an image after all operations in digital image processing chain just before compressing a given raster image. A second strategy is based on prediction of noise and blur parameters from analysis of RAW image under quite general assumptions concerning characteristics parameters of transformations an image will be subject to at further processing stages. The advantages of both strategies are discussed. The first strategy provides more accurate estimation and larger benefit in image compression ratio (CR) compared to super-high quality (SHQ) mode. However, it is more complicated and requires more resources. The second strategy is simpler but less beneficial. The proposed approaches are tested for quite many real life color images acquired by digital cameras and shown to provide more than two time increase of average CR compared to SHQ mode without introducing visible distortions with respect to SHQ compressed images.

  13. Accurate measurement of surface areas of anatomical structures by computer-assisted triangulation of computed tomography images

    Energy Technology Data Exchange (ETDEWEB)

    Allardice, J.T.; Jacomb-Hood, J.; Abulafi, A.M.; Williams, N.S. (Royal London Hospital (United Kingdom)); Cookson, J.; Dykes, E.; Holman, J. (London Hospital Medical College (United Kingdom))

    1993-05-01

    There is a need for accurate surface area measurement of internal anatomical structures in order to define light dosimetry in adjunctive intraoperative photodynamic therapy (AIOPDT). The authors investigated whether computer-assisted triangulation of serial sections generated by computed tomography (CT) scanning can give an accurate assessment of the surface area of the walls of the true pelvis after anterior resection and before colorectal anastomosis. They show that the technique of paper density tessellation is an acceptable method of measuring the surface areas of phantom objects, with a maximum error of 0.5%, and is used as the gold standard. Computer-assisted triangulation of CT images of standard geometric objects and accurately-constructed pelvic phantoms gives a surface area assessment with a maximum error of 2.5% compared with the gold standard. The CT images of 20 patients' pelves have been analysed by computer-assisted triangulation and this shows the surface area of the walls varies from 143 cm[sup 2] to 392 cm[sup 2]. (Author).

  14. Echocardiographic phase imaging to predict reverse remodeling after cardiac resynchronization therapy.

    Science.gov (United States)

    Buss, Sebastian J; Humpert, Per M; Bekeredjian, Raffi; Hardt, Stefan E; Zugck, Christian; Schellberg, Dieter; Bauer, Alexander; Filusch, Arthur; Kuecherer, Helmut; Katus, Hugo A; Korosoglou, Grigorios

    2009-05-01

    The aim of our study was to investigate whether echocardiographic phase imaging (EPI) can predict response in patients who are considered for cardiac resynchronization therapy (CRT). CRT improves quality of life, exercise capacity, and outcome in patients with bundle-branch block and advanced heart failure. Previous studies used QRS duration to select patients for CRT; the accuracy of this parameter to predict functional recovery, however, is controversial. We examined 42 patients with advanced heart failure (New York Heart Association [NYHA] functional class III to IV, QRS duration >130 ms, and ejection fraction or=15% at 6 to 8 months of follow-up were defined as responders. All others were classified as nonresponders. The Ts-SD and the mean EPI-Index were related to Delta ESV (r = 0.43 for Ts-SD and r = 0.67 for mean EPI-Index, p < 0.01 for both), and both parameters yielded similar accuracy for the prediction of LV remodeling (area under the curve of 0.87 for TDI vs. 0.90 for EPI, difference between areas = 0.03, p = NS) and ejection fraction (EF) improvement (area under the curve of 0.87 for TDI vs. 0.93 for EPI, difference between areas = 0.06, p = NS). Furthermore, patients classified as responders by EPI (mean EPI-Index imaging can predict functional recovery, reverse LV remodeling, and clinical outcomes in patients who undergo CRT. EPI is a method that objectively and accurately quantifies LV dyssynchrony and seems to be noninferior to TDI for the prediction of reverse LV remodeling and functional recovery.

  15. Accurate cut-offs for predicting endoscopic activity and mucosal healing in Crohn's disease with fecal calprotectin

    Directory of Open Access Journals (Sweden)

    Juan María Vázquez-Morón

    Full Text Available Background: Fecal biomarkers, especially fecal calprotectin, are useful for predicting endoscopic activity in Crohn's disease; however, the cut-off point remains unclear. The aim of this paper was to analyze whether faecal calprotectin and M2 pyruvate kinase are good tools for generating highly accurate scores for the prediction of the state of endoscopic activity and mucosal healing. Methods: The simple endoscopic score for Crohn's disease and the Crohn's disease activity index was calculated for 71 patients diagnosed with Crohn's. Fecal calprotectin and M2-PK were measured by the enzyme-linked immunosorbent assay test. Results: A fecal calprotectin cut-off concentration of ≥ 170 µg/g (sensitivity 77.6%, specificity 95.5% and likelihood ratio +17.06 predicts a high probability of endoscopic activity, and a fecal calprotectin cut-off of ≤ 71 µg/g (sensitivity 95.9%, specificity 52.3% and likelihood ratio -0.08 predicts a high probability of mucosal healing. Three clinical groups were identified according to the data obtained: endoscopic activity (calprotectin ≥ 170, mucosal healing (calprotectin ≤ 71 and uncertainty (71 > calprotectin < 170, with significant differences in endoscopic values (F = 26.407, p < 0.01. Clinical activity or remission modified the probabilities of presenting endoscopic activity (100% vs 89% or mucosal healing (75% vs 87% in the diagnostic scores generated. M2-PK was insufficiently accurate to determine scores. Conclusions: The highly accurate scores for fecal calprotectin provide a useful tool for interpreting the probabilities of presenting endoscopic activity or mucosal healing, and are valuable in the specific clinical context.

  16. Measuring and Predicting Tag Importance for Image Retrieval.

    Science.gov (United States)

    Li, Shangwen; Purushotham, Sanjay; Chen, Chen; Ren, Yuzhuo; Kuo, C-C Jay

    2017-12-01

    Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This will further lead to degenerated retrieval performance at query time. To address this issue, we investigate the problem of tag importance prediction, where the goal is to automatically predict the tag importance and use it in image retrieval. To achieve this, we first propose a method to measure the relative importance of object and scene tags from image sentence descriptions. Using this as the ground truth, we present a tag importance prediction model to jointly exploit visual, semantic and context cues. The Structural Support Vector Machine (SSVM) formulation is adopted to ensure efficient training of the prediction model. Then, the Canonical Correlation Analysis (CCA) is employed to learn the relation between the image visual feature and tag importance to obtain robust retrieval performance. Experimental results on three real-world datasets show a significant performance improvement of the proposed MIR with Tag Importance Prediction (MIR/TIP) system over other MIR systems.

  17. Flare Prediction Using Photospheric and Coronal Image Data

    Science.gov (United States)

    Jonas, E.; Shankar, V.; Bobra, M.; Recht, B.

    2016-12-01

    We attempt to forecast M-and X-class solar flares using a machine-learning algorithm and five years of image data from both the Helioseismic and Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) instruments aboard the Solar Dynamics Observatory. HMI is the first instrument to continuously map the full-disk photospheric vector magnetic field from space (Schou et al., 2012). The AIA instrument maps the transition region and corona using various ultraviolet wavelengths (Lemen et al., 2012). HMI and AIA data are taken nearly simultaneously, providing an opportunity to study the entire solar atmosphere at a rapid cadence. Most flare forecasting efforts described in the literature use some parameterization of solar data - typically of the photospheric magnetic field within active regions. These numbers are considered to capture the information in any given image relevant to predicting solar flares. In our approach, we use HMI and AIA images of solar active regions and a deep convolutional kernel network to predict solar flares. This is effectively a series of shallow-but-wide random convolutional neural networks stacked and then trained with a large-scale block-weighted least squares solver. This algorithm automatically determines which patterns in the image data are most correlated with flaring activity and then uses these patterns to predict solar flares. Using the recently-developed KeystoneML machine learning framework, we construct a pipeline to process millions of images in a few hours on commodity cloud computing infrastructure. This is the first time vector magnetic field images have been combined with coronal imagery to forecast solar flares. This is also the first time such a large dataset of solar images, some 8.5 terabytes of images that together capture over 3000 active regions, has been used to forecast solar flares. We evaluate our method using various flare prediction windows defined in the literature (e.g. Ahmed et al., 2013) and a novel per

  18. Burnout prediction using advance image analysis coal characterization techniques

    Energy Technology Data Exchange (ETDEWEB)

    Edward Lester; Dave Watts; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical Environmental and Mining Engineering

    2003-07-01

    The link between petrographic composition and burnout has been investigated previously by the authors. However, these predictions were based on 'bulk' properties of the coal, including the proportion of each maceral or the reflectance of the macerals in the whole sample. Combustion studies relating burnout with microlithotype analysis, or similar, remain less common partly because the technique is more complex than maceral analysis. Despite this, it is likely that any burnout prediction based on petrographic characteristics will become more accurate if it includes information about the maceral associations and the size of each particle. Chars from 13 coals, 106-125 micron size fractions, were prepared using a Drop Tube Furnace (DTF) at 1300{degree}C and 200 millisecond and 1% Oxygen. These chars were then refired in the DTF at 1300{degree}C 5% oxygen and residence times of 200, 400 and 600 milliseconds. The progressive burnout of each char was compared with the characteristics of the initial coals. This paper presents an extension of previous studies in that it relates combustion behaviour to coals that have been characterized on a particle by particle basis using advanced image analysis techniques. 13 refs., 7 figs.

  19. Improved image quality in pinhole SPECT by accurate modeling of the point spread function in low magnification systems

    International Nuclear Information System (INIS)

    Pino, Francisco; Roé, Nuria; Aguiar, Pablo; Falcon, Carles; Ros, Domènec; Pavía, Javier

    2015-01-01

    Purpose: Single photon emission computed tomography (SPECT) has become an important noninvasive imaging technique in small-animal research. Due to the high resolution required in small-animal SPECT systems, the spatially variant system response needs to be included in the reconstruction algorithm. Accurate modeling of the system response should result in a major improvement in the quality of reconstructed images. The aim of this study was to quantitatively assess the impact that an accurate modeling of spatially variant collimator/detector response has on image-quality parameters, using a low magnification SPECT system equipped with a pinhole collimator and a small gamma camera. Methods: Three methods were used to model the point spread function (PSF). For the first, only the geometrical pinhole aperture was included in the PSF. For the second, the septal penetration through the pinhole collimator was added. In the third method, the measured intrinsic detector response was incorporated. Tomographic spatial resolution was evaluated and contrast, recovery coefficients, contrast-to-noise ratio, and noise were quantified using a custom-built NEMA NU 4–2008 image-quality phantom. Results: A high correlation was found between the experimental data corresponding to intrinsic detector response and the fitted values obtained by means of an asymmetric Gaussian distribution. For all PSF models, resolution improved as the distance from the point source to the center of the field of view increased and when the acquisition radius diminished. An improvement of resolution was observed after a minimum of five iterations when the PSF modeling included more corrections. Contrast, recovery coefficients, and contrast-to-noise ratio were better for the same level of noise in the image when more accurate models were included. Ring-type artifacts were observed when the number of iterations exceeded 12. Conclusions: Accurate modeling of the PSF improves resolution, contrast, and recovery

  20. Improved image quality in pinhole SPECT by accurate modeling of the point spread function in low magnification systems

    Energy Technology Data Exchange (ETDEWEB)

    Pino, Francisco [Unitat de Biofísica, Facultat de Medicina, Universitat de Barcelona, Barcelona 08036, Spain and Servei de Física Mèdica i Protecció Radiològica, Institut Català d’Oncologia, L’Hospitalet de Llobregat 08907 (Spain); Roé, Nuria [Unitat de Biofísica, Facultat de Medicina, Universitat de Barcelona, Barcelona 08036 (Spain); Aguiar, Pablo, E-mail: pablo.aguiar.fernandez@sergas.es [Fundación Ramón Domínguez, Complexo Hospitalario Universitario de Santiago de Compostela 15706, Spain and Grupo de Imagen Molecular, Instituto de Investigacións Sanitarias de Santiago de Compostela (IDIS), Galicia 15782 (Spain); Falcon, Carles; Ros, Domènec [Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain and CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona 08036 (Spain); Pavía, Javier [Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 080836 (Spain); CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona 08036 (Spain); and Servei de Medicina Nuclear, Hospital Clínic, Barcelona 08036 (Spain)

    2015-02-15

    Purpose: Single photon emission computed tomography (SPECT) has become an important noninvasive imaging technique in small-animal research. Due to the high resolution required in small-animal SPECT systems, the spatially variant system response needs to be included in the reconstruction algorithm. Accurate modeling of the system response should result in a major improvement in the quality of reconstructed images. The aim of this study was to quantitatively assess the impact that an accurate modeling of spatially variant collimator/detector response has on image-quality parameters, using a low magnification SPECT system equipped with a pinhole collimator and a small gamma camera. Methods: Three methods were used to model the point spread function (PSF). For the first, only the geometrical pinhole aperture was included in the PSF. For the second, the septal penetration through the pinhole collimator was added. In the third method, the measured intrinsic detector response was incorporated. Tomographic spatial resolution was evaluated and contrast, recovery coefficients, contrast-to-noise ratio, and noise were quantified using a custom-built NEMA NU 4–2008 image-quality phantom. Results: A high correlation was found between the experimental data corresponding to intrinsic detector response and the fitted values obtained by means of an asymmetric Gaussian distribution. For all PSF models, resolution improved as the distance from the point source to the center of the field of view increased and when the acquisition radius diminished. An improvement of resolution was observed after a minimum of five iterations when the PSF modeling included more corrections. Contrast, recovery coefficients, and contrast-to-noise ratio were better for the same level of noise in the image when more accurate models were included. Ring-type artifacts were observed when the number of iterations exceeded 12. Conclusions: Accurate modeling of the PSF improves resolution, contrast, and recovery

  1. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

    International Nuclear Information System (INIS)

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; Lilienfeld, O. Anatole von; Müller, Klaus-Robert; Tkatchenko, Alexandre

    2015-01-01

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the 'holy grail' of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies

  2. Preoperative 3D FSE T1-Weighted MR Plaque Imaging for Severely Stenotic Cervical ICA: Accuracy of Predicting Emboli during Carotid Endarterectomy

    Directory of Open Access Journals (Sweden)

    Yasushi Ogasawara

    2016-10-01

    Full Text Available The aim of the present study was to determine whether preoperative three-dimensional (3D fast spin-echo (FSE T1-weighted magnetic resonance (MR plaque imaging for severely stenotic cervical carotid arteries could accurately predict the development of artery-to-artery emboli during exposure of the carotid arteries in carotid endarterectomy (CEA. Seventy-five patients underwent preoperative MR plaque imaging and CEA under transcranial Doppler ultrasonography of the ipsilateral middle cerebral artery. On reformatted axial MR image slices showing the maximum plaque occupation rate (POR and maximum plaque intensity for each patient, the contrast ratio (CR was calculated by dividing the internal carotid artery plaque signal intensity by the sternocleidomastoid muscle signal intensity. For all patients, the area under the receiver operating characteristic curve (AUC—used to discriminate between the presence and absence of microembolic signals—was significantly greater for the CR on the axial image with maximum plaque intensity (CRmax intensity (0.941 than for that with the maximum POR (0.885 (p < 0.05. For 32 patients in whom both the maximum POR and the maximum plaque density were identified, the AUCs for the CR were 1.000. Preoperative 3D FSE T1-weighted MR plaque imaging accurately predicts the development of artery-to-artery emboli during exposure of the carotid arteries in CEA.

  3. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

    Directory of Open Access Journals (Sweden)

    Zhiheng Wang

    Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

  4. Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction.

    Science.gov (United States)

    Wu, Yao; Yang, Wei; Lu, Lijun; Lu, Zhentai; Zhong, Liming; Huang, Meiyan; Feng, Yanqiu; Feng, Qianjin; Chen, Wufan

    2016-10-01

    image segmentation or accurate registration is required. Our method demonstrates superior performance in CT prediction and PET reconstruction compared with competing methods. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

  5. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

    Directory of Open Access Journals (Sweden)

    Cecilia Noecker

    2015-03-01

    Full Text Available Upon infection of a new host, human immunodeficiency virus (HIV replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV. First, we found that the mode of virus production by infected cells (budding vs. bursting has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral

  6. Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

    Science.gov (United States)

    Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

    2018-02-01

    Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

  7. Improving medical decisions for incapacitated persons: does focusing on "accurate predictions" lead to an inaccurate picture?

    Science.gov (United States)

    Kim, Scott Y H

    2014-04-01

    The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%-80% reliability of people's preferences for future medical decisions--a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed.

  8. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

    Science.gov (United States)

    Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

    2018-01-01

    The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

  9. The MIDAS touch for Accurately Predicting the Stress-Strain Behavior of Tantalum

    Energy Technology Data Exchange (ETDEWEB)

    Jorgensen, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2016-03-02

    Testing the behavior of metals in extreme environments is not always feasible, so material scientists use models to try and predict the behavior. To achieve accurate results it is necessary to use the appropriate model and material-specific parameters. This research evaluated the performance of six material models available in the MIDAS database [1] to determine at which temperatures and strain-rates they perform best, and to determine to which experimental data their parameters were optimized. Additionally, parameters were optimized for the Johnson-Cook model using experimental data from Lassila et al [2].

  10. Accurate inference of shoot biomass from high-throughput images of cereal plants

    Directory of Open Access Journals (Sweden)

    Tester Mark

    2011-02-01

    Full Text Available Abstract With the establishment of advanced technology facilities for high throughput plant phenotyping, the problem of estimating plant biomass of individual plants from their two dimensional images is becoming increasingly important. The approach predominantly cited in literature is to estimate the biomass of a plant as a linear function of the projected shoot area of plants in the images. However, the estimation error from this model, which is solely a function of projected shoot area, is large, prohibiting accurate estimation of the biomass of plants, particularly for the salt-stressed plants. In this paper, we propose a method based on plant specific weight for improving the accuracy of the linear model and reducing the estimation bias (the difference between actual shoot dry weight and the value of the shoot dry weight estimated with a predictive model. For the proposed method in this study, we modeled the plant shoot dry weight as a function of plant area and plant age. The data used for developing our model and comparing the results with the linear model were collected from a completely randomized block design experiment. A total of 320 plants from two bread wheat varieties were grown in a supported hydroponics system in a greenhouse. The plants were exposed to two levels of hydroponic salt treatments (NaCl at 0 and 100 mM for 6 weeks. Five harvests were carried out. Each time 64 randomly selected plants were imaged and then harvested to measure the shoot fresh weight and shoot dry weight. The results of statistical analysis showed that with our proposed method, most of the observed variance can be explained, and moreover only a small difference between actual and estimated shoot dry weight was obtained. The low estimation bias indicates that our proposed method can be used to estimate biomass of individual plants regardless of what variety the plant is and what salt treatment has been applied. We validated this model on an independent

  11. Cone beam computed tomography: An accurate imaging technique in comparison with orthogonal portal imaging in intensity-modulated radiotherapy for prostate cancer

    Directory of Open Access Journals (Sweden)

    Om Prakash Gurjar

    2016-03-01

    Full Text Available Purpose: Various factors cause geometric uncertainties during prostate radiotherapy, including interfractional and intrafractional patient motions, organ motion, and daily setup errors. This may lead to increased normal tissue complications when a high dose to the prostate is administered. More-accurate treatment delivery is possible with daily imaging and localization of the prostate. This study aims to measure the shift of the prostate by using kilovoltage (kV cone beam computed tomography (CBCT after position verification by kV orthogonal portal imaging (OPI.Methods: Position verification in 10 patients with prostate cancer was performed by using OPI followed by CBCT before treatment delivery in 25 sessions per patient. In each session, OPI was performed by using an on-board imaging (OBI system and pelvic bone-to-pelvic bone matching was performed. After applying the noted shift by using OPI, CBCT was performed by using the OBI system and prostate-to-prostate matching was performed. The isocenter shifts along all three translational directions in both techniques were combined into a three-dimensional (3-D iso-displacement vector (IDV.Results: The mean (SD IDV (in centimeters calculated during the 250 imaging sessions was 0.931 (0.598, median 0.825 for OPI and 0.515 (336, median 0.43 for CBCT, p-value was less than 0.0001 which shows extremely statistical significant difference.Conclusion: Even after bone-to-bone matching by using OPI, a significant shift in prostate was observed on CBCT. This study concludes that imaging with CBCT provides a more accurate prostate localization than the OPI technique. Hence, CBCT should be chosen as the preferred imaging technique.

  12. Dynamic and accurate assessment of acetaminophen-induced hepatotoxicity by integrated photoacoustic imaging and mechanistic biomarkers in vivo.

    Science.gov (United States)

    Brillant, Nathalie; Elmasry, Mohamed; Burton, Neal C; Rodriguez, Josep Monne; Sharkey, Jack W; Fenwick, Stephen; Poptani, Harish; Kitteringham, Neil R; Goldring, Christopher E; Kipar, Anja; Park, B Kevin; Antoine, Daniel J

    2017-10-01

    The prediction and understanding of acetaminophen (APAP)-induced liver injury (APAP-ILI) and the response to therapeutic interventions is complex. This is due in part to sensitivity and specificity limitations of currently used assessment techniques. Here we sought to determine the utility of integrating translational non-invasive photoacoustic imaging of liver function with mechanistic circulating biomarkers of hepatotoxicity with histological assessment to facilitate the more accurate and precise characterization of APAP-ILI and the efficacy of therapeutic intervention. Perturbation of liver function and cellular viability was assessed in C57BL/6J male mice by Indocyanine green (ICG) clearance (Multispectral Optoacoustic Tomography (MSOT)) and by measurement of mechanistic (miR-122, HMGB1) and established (ALT, bilirubin) circulating biomarkers in response to the acetaminophen and its treatment with acetylcysteine (NAC) in vivo. We utilised a 60% partial hepatectomy model as a situation of defined hepatic functional mass loss to compared acetaminophen-induced changes to. Integration of these mechanistic markers correlated with histological features of APAP hepatotoxicity in a time-dependent manner. They accurately reflected the onset and recovery from hepatotoxicity compared to traditional biomarkers and also reported the efficacy of NAC with high sensitivity. ICG clearance kinetics correlated with histological scores for acute liver damage for APAP (i.e. 3h timepoint; r=0.90, P<0.0001) and elevations in both of the mechanistic biomarkers, miR-122 (e.g. 6h timepoint; r=0.70, P=0.005) and HMGB1 (e.g. 6h timepoint; r=0.56, P=0.04). For the first time we report the utility of this non-invasive longitudinal imaging approach to provide direct visualisation of the liver function coupled with mechanistic biomarkers, in the same animal, allowing the investigation of the toxicological and pharmacological aspects of APAP-ILI and hepatic regeneration. Copyright © 2017

  13. A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

    International Nuclear Information System (INIS)

    Liu, Guangming; Ouyang, Minggao; Lu, Languang; Li, Jianqiu; Hua, Jianfeng

    2015-01-01

    Highlights: • An energy prediction (EP) method is introduced for battery E RDE determination. • EP determines E RDE through coupled prediction of future states, parameters, and output. • The PAEP combines parameter adaptation and prediction to update model parameters. • The PAEP provides improved E RDE accuracy compared with DC and other EP methods. - Abstract: In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (E RDE ) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge (SOC). To enhance the E RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E RDE prediction. The correlation of SOC estimation and E RDE calculation is then discussed to illustrate the

  14. Easy Leaf Area: Automated digital image analysis for rapid and accurate measurement of leaf area.

    Science.gov (United States)

    Easlon, Hsien Ming; Bloom, Arnold J

    2014-07-01

    Measurement of leaf areas from digital photographs has traditionally required significant user input unless backgrounds are carefully masked. Easy Leaf Area was developed to batch process hundreds of Arabidopsis rosette images in minutes, removing background artifacts and saving results to a spreadsheet-ready CSV file. • Easy Leaf Area uses the color ratios of each pixel to distinguish leaves and calibration areas from their background and compares leaf pixel counts to a red calibration area to eliminate the need for camera distance calculations or manual ruler scale measurement that other software methods typically require. Leaf areas estimated by this software from images taken with a camera phone were more accurate than ImageJ estimates from flatbed scanner images. • Easy Leaf Area provides an easy-to-use method for rapid measurement of leaf area and nondestructive estimation of canopy area from digital images.

  15. Development of motion image prediction method using principal component analysis

    International Nuclear Information System (INIS)

    Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

    2012-01-01

    Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

  16. Do dual-route models accurately predict reading and spelling performance in individuals with acquired alexia and agraphia?

    Science.gov (United States)

    Rapcsak, Steven Z; Henry, Maya L; Teague, Sommer L; Carnahan, Susan D; Beeson, Pélagie M

    2007-06-18

    Coltheart and co-workers [Castles, A., Bates, T. C., & Coltheart, M. (2006). John Marshall and the developmental dyslexias. Aphasiology, 20, 871-892; Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256] have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper, we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult neurological patients with acquired alexia and agraphia. These findings provide empirical support for dual-route theories of written language processing.

  17. Imaging biomarkers to predict response to anti-HER2 (ErbB2) therapy in preclinical models of breast cancer

    Science.gov (United States)

    Shah, Chirayu; Miller, Todd W.; Wyatt, Shelby K.; McKinley, Eliot T.; Olivares, Maria Graciela; Sanchez, Violeta; Nolting, Donald D.; Buck, Jason R.; Zhao, Ping; Ansari, M. Sib; Baldwin, Ronald M.; Gore, John C.; Schiff, Rachel; Arteaga, Carlos L.; Manning, H. Charles

    2010-01-01

    Purpose To evaluate non-invasive imaging methods as predictive biomarkers of response to trastuzumab in mouse models of HER2-overexpressing breast cancer. The correlation between tumor regression and molecular imaging of apoptosis, glucose metabolism, and cellular proliferation was evaluated longitudinally in responding and non-responding tumor-bearing cohorts. Experimental Design Mammary tumors from MMTV/HER2 transgenic female mice were transplanted into syngeneic female mice. BT474 human breast carcinoma cell line xenografts were grown in athymic nude mice. Tumor cell apoptosis (NIR700-Annexin-V accumulation), glucose metabolism ([18F]FDG-PET), and proliferation ([18F]FLT-PET) were evaluated throughout a bi-weekly trastuzumab regimen. Imaging metrics were validated by direct measurement of tumor size and immunohistochemical (IHC) analysis of cleaved caspase-3, phosphorylated AKT (p-AKT) and Ki67. Results NIR700-Annexin-V accumulated significantly in trastuzumab-treated MMTV/HER2 and BT474 tumors that ultimately regressed, but not in non-responding or vehicle-treated tumors. Uptake of [18F]FDG was not affected by trastuzumab treatment in MMTV/HER2 or BT474 tumors. [18F]FLT PET imaging predicted trastuzumab response in BT474 tumors but not in MMTV/HER2 tumors, which exhibited modest uptake of [18F]FLT. Close agreement was observed between imaging metrics and IHC analysis. Conclusions Molecular imaging of apoptosis accurately predicts trastuzumab-induced regression of HER2(+) tumors and may warrant clinical exploration to predict early response to neoadjuvant trastuzumab. Trastuzumab does not appear to alter glucose metabolism substantially enough to afford [18F]FDG-PET significant predictive value in this setting. Although promising in one preclinical model, further studies are required to determine the overall value of [18F]FLT-PET as a biomarker of response to trastuzumab in HER2+ breast cancer. PMID:19584166

  18. Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

    International Nuclear Information System (INIS)

    Wang, Yan; Zhou, Jiliu; Zhang, Pei; An, Le; Ma, Guangkai; Kang, Jiayin; Shi, Feng; Shen, Dinggang; Wu, Xi; Lalush, David S; Lin, Weili

    2016-01-01

    Positron emission tomography (PET) has been widely used in clinical diagnosis for diseases and disorders. To obtain high-quality PET images requires a standard-dose radionuclide (tracer) injection into the human body, which inevitably increases risk of radiation exposure. One possible solution to this problem is to predict the standard-dose PET image from its low-dose counterpart and its corresponding multimodal magnetic resonance (MR) images. Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, we propose a mapping-based SR (m-SR) framework for standard-dose PET image prediction. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients, estimated from the multimodal MR images and low-dose PET image, can be applied directly to the prediction of standard-dose PET image. As the mapping between multimodal MR images (or low-dose PET image) and standard-dose PET images can be particularly complex, one step of mapping is often insufficient. To this end, an incremental refinement framework is therefore proposed. Specifically, the predicted standard-dose PET image is further mapped to the target standard-dose PET image, and then the SR is performed again to predict a new standard-dose PET image. This procedure can be repeated for prediction refinement of the iterations. Also, a patch selection based dictionary construction method is further used to speed up the prediction process. The proposed method is validated on a human brain dataset. The experimental results show that our method can outperform benchmark methods in both qualitative and quantitative measures. (paper)

  19. Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

    International Nuclear Information System (INIS)

    Ernst, Floris; Schweikard, Achim

    2008-01-01

    Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

  20. Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

    Energy Technology Data Exchange (ETDEWEB)

    Ernst, Floris; Schweikard, Achim [University of Luebeck, Institute for Robotics and Cognitive Systems, Luebeck (Germany)

    2008-06-15

    Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

  1. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

  2. Magnetic Resonance Imaging: An accurate diagnostic tool in the precise localization of penile fracture

    Directory of Open Access Journals (Sweden)

    Mujeeb M Rahiman

    2013-01-01

    Full Text Available An 18-year-old male presented with history and clinical findings suggestive of penile fracture. An MRI demonstrated disruption of the tunica albuginea and corpora cavernosa on the left dorso-lateral aspect, mid-shaft of penis with adjacent hematoma, and subcutaneous edema. At surgery, imaging findings were found to be accurate, and the penis was successfully repaired with minimal postoperative morbidity.

  3. Easy Leaf Area: Automated Digital Image Analysis for Rapid and Accurate Measurement of Leaf Area

    Directory of Open Access Journals (Sweden)

    Hsien Ming Easlon

    2014-07-01

    Full Text Available Premise of the study: Measurement of leaf areas from digital photographs has traditionally required significant user input unless backgrounds are carefully masked. Easy Leaf Area was developed to batch process hundreds of Arabidopsis rosette images in minutes, removing background artifacts and saving results to a spreadsheet-ready CSV file. Methods and Results: Easy Leaf Area uses the color ratios of each pixel to distinguish leaves and calibration areas from their background and compares leaf pixel counts to a red calibration area to eliminate the need for camera distance calculations or manual ruler scale measurement that other software methods typically require. Leaf areas estimated by this software from images taken with a camera phone were more accurate than ImageJ estimates from flatbed scanner images. Conclusions: Easy Leaf Area provides an easy-to-use method for rapid measurement of leaf area and nondestructive estimation of canopy area from digital images.

  4. Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

    Directory of Open Access Journals (Sweden)

    Jesse S. Jin

    2010-10-01

    Full Text Available Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

  5. Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients.

    Directory of Open Access Journals (Sweden)

    Jayasree Chakraborty

    Full Text Available Pancreatic ductal adenocarcinoma (PDAC is one of the most lethal cancers in the United States with a five-year survival rate of 7.2% for all stages. Although surgical resection is the only curative treatment, currently we are unable to differentiate between resectable patients with occult metastatic disease from those with potentially curable disease. Identification of patients with poor prognosis via early classification would help in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant therapy. PDAC ranges in appearance from homogeneously isoattenuating masses to heterogeneously hypovascular tumors on CT images; hence, we hypothesize that heterogeneity reflects underlying differences at the histologic or genetic level and will therefore correlate with patient outcome. We quantify heterogeneity of PDAC with texture analysis to predict 2-year survival. Using fuzzy minimum-redundancy maximum-relevance feature selection and a naive Bayes classifier, the proposed features achieve an area under receiver operating characteristic curve (AUC of 0.90 and accuracy (Ac of 82.86% with the leave-one-image-out technique and an AUC of 0.80 and Ac of 75.0% with three-fold cross-validation. We conclude that texture analysis can be used to quantify heterogeneity in CT images to accurately predict 2-year survival in patients with pancreatic cancer. From these data, we infer differences in the biological evolution of pancreatic cancer subtypes measurable in imaging and identify opportunities for optimized patient selection for therapy.

  6. Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients.

    Science.gov (United States)

    Chakraborty, Jayasree; Langdon-Embry, Liana; Cunanan, Kristen M; Escalon, Joanna G; Allen, Peter J; Lowery, Maeve A; O'Reilly, Eileen M; Gönen, Mithat; Do, Richard G; Simpson, Amber L

    2017-01-01

    Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the United States with a five-year survival rate of 7.2% for all stages. Although surgical resection is the only curative treatment, currently we are unable to differentiate between resectable patients with occult metastatic disease from those with potentially curable disease. Identification of patients with poor prognosis via early classification would help in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant therapy. PDAC ranges in appearance from homogeneously isoattenuating masses to heterogeneously hypovascular tumors on CT images; hence, we hypothesize that heterogeneity reflects underlying differences at the histologic or genetic level and will therefore correlate with patient outcome. We quantify heterogeneity of PDAC with texture analysis to predict 2-year survival. Using fuzzy minimum-redundancy maximum-relevance feature selection and a naive Bayes classifier, the proposed features achieve an area under receiver operating characteristic curve (AUC) of 0.90 and accuracy (Ac) of 82.86% with the leave-one-image-out technique and an AUC of 0.80 and Ac of 75.0% with three-fold cross-validation. We conclude that texture analysis can be used to quantify heterogeneity in CT images to accurately predict 2-year survival in patients with pancreatic cancer. From these data, we infer differences in the biological evolution of pancreatic cancer subtypes measurable in imaging and identify opportunities for optimized patient selection for therapy.

  7. Prediction of tumor-brain adhesion in intracranial meningiomas by MR imaging and DSA

    International Nuclear Information System (INIS)

    Takeguchi, Takashi; Miki, Hitoshi; Shimizu, Teruhiko; Kikuchi, Keiichi; Mochizuki, Teruhito; Ohue, Shiro; Ohnishi, Takanori

    2003-01-01

    The purpose of this study was to evaluate the usefulness of MRI (magnetic resonance imaging) and DSA (digital subtraction angiography) by using preoperative MRI and DSA findings in the examination of meningiomas before excision. In particular, we focused on their usefulness in predicting tumor-brain adhesion during surgery. The subjects were 36 patients with intracranial meningioma who underwent tumor excision at which time neurosurgeons examined the tumor-brain adhesion. Two neurosurgeons evaluated the degree of tumor-brain adhesion from operation records and videotapes recorded during surgery. Two neuroradiologists retrospectively evaluated the preoperative MRI findings including tumor diameter, signal intensity of the tumor parenchyma obtained with T 2 -weighted imaging (T 2 WI), characteristics of the tumor-brain interface, and degree of peritumoral brain edema. The vascular supply was also evaluated from the preoperative DSA findings. The relationship between these MRI and DSA findings and the degree of tumor-brain adhesion during surgery as classified by the neurosurgeons was statistically analyzed. The degree of peritumoral brain edema and the shapes and characteristics of the tumor-brain interface, including the findings of FLAIR (fluid-attenuated inversion recovery) imaging and vascular supply observed by DSA, were significantly correlated with tumor-brain adhesion. In particular, the shapes and characteristics of the tumor-brain interface as observed by T 1 -weighted imaging (T 1 WI), T2WI, and FLAIR, respectively, as well as the vascular supply observed by DSA, were closely correlated with the degree of tumor-brain adhesion encountered during surgery. According to these results, we developed a method of predicting tumor-brain adhesion that considers the shape of the tumor-brain interface revealed by MRI and the vascular supply revealed by DSA. We retrospectively examined the findings of MRI and DSA performed before excision of meningioma and clarified

  8. Accurate diffraction data integration by the EVAL15 profile prediction method : Application in chemical and biological crystallography

    NARCIS (Netherlands)

    Xian, X.

    2009-01-01

    Accurate integration of reflection intensities plays an essential role in structure determination of the crystallized compound. A new diffraction data integration method, EVAL15, is presented in this thesis. This method uses the principle of general impacts to predict ab inito three-dimensional

  9. Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.

    Directory of Open Access Journals (Sweden)

    Niklas Berliner

    Full Text Available Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.

  10. Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning

    DEFF Research Database (Denmark)

    Nielsen, Anne; Hansen, Mikkel Bo; Tietze, Anna

    2018-01-01

    of automatically identifying and combining acute imaging features to accurately predict final lesion volume. METHODS: Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNNdeep) to predict final imaging outcome. A total of 222 patients were included, of which 187...

  11. Fast and accurate denoising method applied to very high resolution optical remote sensing images

    Science.gov (United States)

    Masse, Antoine; Lefèvre, Sébastien; Binet, Renaud; Artigues, Stéphanie; Lassalle, Pierre; Blanchet, Gwendoline; Baillarin, Simon

    2017-10-01

    Restoration of Very High Resolution (VHR) optical Remote Sensing Image (RSI) is critical and leads to the problem of removing instrumental noise while keeping integrity of relevant information. Improving denoising in an image processing chain implies increasing image quality and improving performance of all following tasks operated by experts (photo-interpretation, cartography, etc.) or by algorithms (land cover mapping, change detection, 3D reconstruction, etc.). In a context of large industrial VHR image production, the selected denoising method should optimized accuracy and robustness with relevant information and saliency conservation, and rapidity due to the huge amount of data acquired and/or archived. Very recent research in image processing leads to a fast and accurate algorithm called Non Local Bayes (NLB) that we propose to adapt and optimize for VHR RSIs. This method is well suited for mass production thanks to its best trade-off between accuracy and computational complexity compared to other state-of-the-art methods. NLB is based on a simple principle: similar structures in an image have similar noise distribution and thus can be denoised with the same noise estimation. In this paper, we describe in details algorithm operations and performances, and analyze parameter sensibilities on various typical real areas observed in VHR RSIs.

  12. Comprehensive fluence model for absolute portal dose image prediction

    International Nuclear Information System (INIS)

    Chytyk, K.; McCurdy, B. M. C.

    2009-01-01

    Amorphous silicon (a-Si) electronic portal imaging devices (EPIDs) continue to be investigated as treatment verification tools, with a particular focus on intensity modulated radiation therapy (IMRT). This verification could be accomplished through a comparison of measured portal images to predicted portal dose images. A general fluence determination tailored to portal dose image prediction would be a great asset in order to model the complex modulation of IMRT. A proposed physics-based parameter fluence model was commissioned by matching predicted EPID images to corresponding measured EPID images of multileaf collimator (MLC) defined fields. The two-source fluence model was composed of a focal Gaussian and an extrafocal Gaussian-like source. Specific aspects of the MLC and secondary collimators were also modeled (e.g., jaw and MLC transmission factors, MLC rounded leaf tips, tongue and groove effect, interleaf leakage, and leaf offsets). Several unique aspects of the model were developed based on the results of detailed Monte Carlo simulations of the linear accelerator including (1) use of a non-Gaussian extrafocal fluence source function, (2) separate energy spectra used for focal and extrafocal fluence, and (3) different off-axis energy spectra softening used for focal and extrafocal fluences. The predicted energy fluence was then convolved with Monte Carlo generated, EPID-specific dose kernels to convert incident fluence to dose delivered to the EPID. Measured EPID data were obtained with an a-Si EPID for various MLC-defined fields (from 1x1 to 20x20 cm 2 ) over a range of source-to-detector distances. These measured profiles were used to determine the fluence model parameters in a process analogous to the commissioning of a treatment planning system. The resulting model was tested on 20 clinical IMRT plans, including ten prostate and ten oropharyngeal cases. The model predicted the open-field profiles within 2%, 2 mm, while a mean of 96.6% of pixels over all

  13. Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

    Science.gov (United States)

    Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

    2017-09-01

    Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

  14. Fast, accurate, and robust automatic marker detection for motion correction based on oblique kV or MV projection image pairs

    International Nuclear Information System (INIS)

    Slagmolen, Pieter; Hermans, Jeroen; Maes, Frederik; Budiharto, Tom; Haustermans, Karin; Heuvel, Frank van den

    2010-01-01

    Purpose: A robust and accurate method that allows the automatic detection of fiducial markers in MV and kV projection image pairs is proposed. The method allows to automatically correct for inter or intrafraction motion. Methods: Intratreatment MV projection images are acquired during each of five treatment beams of prostate cancer patients with four implanted fiducial markers. The projection images are first preprocessed using a series of marker enhancing filters. 2D candidate marker locations are generated for each of the filtered projection images and 3D candidate marker locations are reconstructed by pairing candidates in subsequent projection images. The correct marker positions are retrieved in 3D by the minimization of a cost function that combines 2D image intensity and 3D geometric or shape information for the entire marker configuration simultaneously. This optimization problem is solved using dynamic programming such that the globally optimal configuration for all markers is always found. Translational interfraction and intrafraction prostate motion and the required patient repositioning is assessed from the position of the centroid of the detected markers in different MV image pairs. The method was validated on a phantom using CT as ground-truth and on clinical data sets of 16 patients using manual marker annotations as ground-truth. Results: The entire setup was confirmed to be accurate to around 1 mm by the phantom measurements. The reproducibility of the manual marker selection was less than 3.5 pixels in the MV images. In patient images, markers were correctly identified in at least 99% of the cases for anterior projection images and 96% of the cases for oblique projection images. The average marker detection accuracy was 1.4±1.8 pixels in the projection images. The centroid of all four reconstructed marker positions in 3D was positioned within 2 mm of the ground-truth position in 99.73% of all cases. Detecting four markers in a pair of MV images

  15. Proposal for future diagnosis and management of vascular tumors by using automatic software for image processing and statistic prediction.

    Science.gov (United States)

    Popescu, M D; Draghici, L; Secheli, I; Secheli, M; Codrescu, M; Draghici, I

    2015-01-01

    Infantile Hemangiomas (IH) are the most frequent tumors of vascular origin, and the differential diagnosis from vascular malformations is difficult to establish. Specific types of IH due to the location, dimensions and fast evolution, can determine important functional and esthetic sequels. To avoid these unfortunate consequences it is necessary to establish the exact appropriate moment to begin the treatment and decide which the most adequate therapeutic procedure is. Based on clinical data collected by a serial clinical observations correlated with imaging data, and processed by a computer-aided diagnosis system (CAD), the study intended to develop a treatment algorithm to accurately predict the best final results, from the esthetical and functional point of view, for a certain type of lesion. The preliminary database was composed of 75 patients divided into 4 groups according to the treatment management they received: medical therapy, sclerotherapy, surgical excision and no treatment. The serial clinical observation was performed each month and all the data was processed by using CAD. The project goal was to create a software that incorporated advanced methods to accurately measure the specific IH lesions, integrated medical information, statistical methods and computational methods to correlate this information with that obtained from the processing of images. Based on these correlations, a prediction mechanism of the evolution of hemangioma, which helped determine the best method of therapeutic intervention to minimize further complications, was established.

  16. An accurate model for numerical prediction of piezoelectric energy harvesting from fluid structure interaction problems

    International Nuclear Information System (INIS)

    Amini, Y; Emdad, H; Farid, M

    2014-01-01

    Piezoelectric energy harvesting (PEH) from ambient energy sources, particularly vibrations, has attracted considerable interest throughout the last decade. Since fluid flow has a high energy density, it is one of the best candidates for PEH. Indeed, a piezoelectric energy harvesting process from the fluid flow takes the form of natural three-way coupling of the turbulent fluid flow, the electromechanical effect of the piezoelectric material and the electrical circuit. There are some experimental and numerical studies about piezoelectric energy harvesting from fluid flow in literatures. Nevertheless, accurate modeling for predicting characteristics of this three-way coupling has not yet been developed. In the present study, accurate modeling for this triple coupling is developed and validated by experimental results. A new code based on this modeling in an openFOAM platform is developed. (paper)

  17. Improved Patient Size Estimates for Accurate Dose Calculations in Abdomen Computed Tomography

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Chang-Lae [Yonsei University, Wonju (Korea, Republic of)

    2017-07-15

    The radiation dose of CT (computed tomography) is generally represented by the CTDI (CT dose index). CTDI, however, does not accurately predict the actual patient doses for different human body sizes because it relies on a cylinder-shaped head (diameter : 16 cm) and body (diameter : 32 cm) phantom. The purpose of this study was to eliminate the drawbacks of the conventional CTDI and to provide more accurate radiation dose information. Projection radiographs were obtained from water cylinder phantoms of various sizes, and the sizes of the water cylinder phantoms were calculated and verified using attenuation profiles. The effective diameter was also calculated using the attenuation of the abdominal projection radiographs of 10 patients. When the results of the attenuation-based method and the geometry-based method shown were compared with the results of the reconstructed-axial-CT-image-based method, the effective diameter of the attenuation-based method was found to be similar to the effective diameter of the reconstructed-axial-CT-image-based method, with a difference of less than 3.8%, but the geometry-based method showed a difference of less than 11.4%. This paper proposes a new method of accurately computing the radiation dose of CT based on the patient sizes. This method computes and provides the exact patient dose before the CT scan, and can therefore be effectively used for imaging and dose control.

  18. Large arterial occlusive strokes as a medical emergency: need to accurately predict clot location.

    Science.gov (United States)

    Vanacker, Peter; Faouzi, Mohamed; Eskandari, Ashraf; Maeder, Philippe; Meuli, Reto; Michel, Patrik

    2017-10-01

    Endovascular treatment for acute ischemic stroke with a large intracranial occlusion was recently shown to be effective. Timely knowledge of the presence, site, and extent of arterial occlusions in the ischemic territory has the potential to influence patient selection for endovascular treatment. We aimed to find predictors of large vessel occlusive strokes, on the basis of available demographic, clinical, radiological, and laboratory data in the emergency setting. Patients enrolled in ASTRAL registry with acute ischemic stroke and computed tomography (CT)-angiography within 12 h of stroke onset were selected and categorized according to occlusion site. Easily accessible variables were used in a multivariate analysis. Of 1645 patients enrolled, a significant proportion (46.2%) had a large vessel occlusion in the ischemic territory. The main clinical predictors of any arterial occlusion were in-hospital stroke [odd ratios (OR) 2.1, 95% confidence interval 1.4-3.1], higher initial National Institute of Health Stroke Scale (OR 1.1, 1.1-1.2), presence of visual field defects (OR 1.9, 1.3-2.6), dysarthria (OR 1.4, 1.0-1.9), or hemineglect (OR 2.0, 1.4-2.8) at admission and atrial fibrillation (OR 1.7, 1.2-2.3). Further, the following radiological predictors were identified: time-to-imaging (OR 0.9, 0.9-1.0), early ischemic changes (OR 2.3, 1.7-3.2), and silent lesions on CT (OR 0.7, 0.5-1.0). The area under curve for this analysis was 0.85. Looking at different occlusion sites, National Institute of Health Stroke Scale and early ischemic changes on CT were independent predictors in all subgroups. Neurological deficits, stroke risk factors, and CT findings accurately identify acute ischemic stroke patients at risk of symptomatic vessel occlusion. Predicting the presence of these occlusions may impact emergency stroke care in regions with limited access to noninvasive vascular imaging.

  19. The lucky image-motion prediction for simple scene observation based soft-sensor technology

    Science.gov (United States)

    Li, Yan; Su, Yun; Hu, Bin

    2015-08-01

    High resolution is important to earth remote sensors, while the vibration of the platforms of the remote sensors is a major factor restricting high resolution imaging. The image-motion prediction and real-time compensation are key technologies to solve this problem. For the reason that the traditional autocorrelation image algorithm cannot meet the demand for the simple scene image stabilization, this paper proposes to utilize soft-sensor technology in image-motion prediction, and focus on the research of algorithm optimization in imaging image-motion prediction. Simulations results indicate that the improving lucky image-motion stabilization algorithm combining the Back Propagation Network (BP NN) and support vector machine (SVM) is the most suitable for the simple scene image stabilization. The relative error of the image-motion prediction based the soft-sensor technology is below 5%, the training computing speed of the mathematical predication model is as fast as the real-time image stabilization in aerial photography.

  20. 3D FaceCam: a fast and accurate 3D facial imaging device for biometrics applications

    Science.gov (United States)

    Geng, Jason; Zhuang, Ping; May, Patrick; Yi, Steven; Tunnell, David

    2004-08-01

    Human faces are fundamentally three-dimensional (3D) objects, and each face has its unique 3D geometric profile. The 3D geometric features of a human face can be used, together with its 2D texture, for rapid and accurate face recognition purposes. Due to the lack of low-cost and robust 3D sensors and effective 3D facial recognition (FR) algorithms, almost all existing FR systems use 2D face images. Genex has developed 3D solutions that overcome the inherent problems in 2D while also addressing limitations in other 3D alternatives. One important aspect of our solution is a unique 3D camera (the 3D FaceCam) that combines multiple imaging sensors within a single compact device to provide instantaneous, ear-to-ear coverage of a human face. This 3D camera uses three high-resolution CCD sensors and a color encoded pattern projection system. The RGB color information from each pixel is used to compute the range data and generate an accurate 3D surface map. The imaging system uses no moving parts and combines multiple 3D views to provide detailed and complete 3D coverage of the entire face. Images are captured within a fraction of a second and full-frame 3D data is produced within a few seconds. This described method provides much better data coverage and accuracy in feature areas with sharp features or details (such as the nose and eyes). Using this 3D data, we have been able to demonstrate that a 3D approach can significantly improve the performance of facial recognition. We have conducted tests in which we have varied the lighting conditions and angle of image acquisition in the "field." These tests have shown that the matching results are significantly improved when enrolling a 3D image rather than a single 2D image. With its 3D solutions, Genex is working toward unlocking the promise of powerful 3D FR and transferring FR from a lab technology into a real-world biometric solution.

  1. Imaging of fetal chest masses

    Energy Technology Data Exchange (ETDEWEB)

    Barth, Richard A. [Lucile Packard Children' s Hospital, Stanford University School of Medicine, Department of Radiology, Stanford, CA (United States)

    2012-01-15

    Prenatal imaging with high-resolution US and rapid acquisition MRI plays a key role in the accurate diagnosis of congenital chest masses. Imaging has enhanced our understanding of the natural history of fetal lung masses, allowing for accurate prediction of outcome, parental counseling, and planning of pregnancy and newborn management. This paper will focus on congenital bronchopulmonary malformations, which account for the vast majority of primary lung masses in the fetus. In addition, anomalies that mimic masses and less common causes of lung masses will be discussed. (orig.)

  2. Variational mode decomposition based approach for accurate classification of color fundus images with hemorrhages

    Science.gov (United States)

    Lahmiri, Salim; Shmuel, Amir

    2017-11-01

    Diabetic retinopathy is a disease that can cause a loss of vision. An early and accurate diagnosis helps to improve treatment of the disease and prognosis. One of the earliest characteristics of diabetic retinopathy is the appearance of retinal hemorrhages. The purpose of this study is to design a fully automated system for the detection of hemorrhages in a retinal image. In the first stage of our proposed system, a retinal image is processed with variational mode decomposition (VMD) to obtain the first variational mode, which captures the high frequency components of the original image. In the second stage, four texture descriptors are extracted from the first variational mode. Finally, a classifier trained with all computed texture descriptors is used to distinguish between images of healthy and unhealthy retinas with hemorrhages. Experimental results showed evidence of the effectiveness of the proposed system for detection of hemorrhages in the retina, since a perfect detection rate was achieved. Our proposed system for detecting diabetic retinopathy is simple and easy to implement. It requires only short processing time, and it yields higher accuracy in comparison with previously proposed methods for detecting diabetic retinopathy.

  3. Hybrid wavefront sensing and image correction algorithm for imaging through turbulent media

    Science.gov (United States)

    Wu, Chensheng; Robertson Rzasa, John; Ko, Jonathan; Davis, Christopher C.

    2017-09-01

    It is well known that passive image correction of turbulence distortions often involves using geometry-dependent deconvolution algorithms. On the other hand, active imaging techniques using adaptive optic correction should use the distorted wavefront information for guidance. Our work shows that a hybrid hardware-software approach is possible to obtain accurate and highly detailed images through turbulent media. The processing algorithm also takes much fewer iteration steps in comparison with conventional image processing algorithms. In our proposed approach, a plenoptic sensor is used as a wavefront sensor to guide post-stage image correction on a high-definition zoomable camera. Conversely, we show that given the ground truth of the highly detailed image and the plenoptic imaging result, we can generate an accurate prediction of the blurred image on a traditional zoomable camera. Similarly, the ground truth combined with the blurred image from the zoomable camera would provide the wavefront conditions. In application, our hybrid approach can be used as an effective way to conduct object recognition in a turbulent environment where the target has been significantly distorted or is even unrecognizable.

  4. Highly accurate prediction of food challenge outcome using routinely available clinical data.

    Science.gov (United States)

    DunnGalvin, Audrey; Daly, Deirdre; Cullinane, Claire; Stenke, Emily; Keeton, Diane; Erlewyn-Lajeunesse, Mich; Roberts, Graham C; Lucas, Jane; Hourihane, Jonathan O'B

    2011-03-01

    Serum specific IgE or skin prick tests are less useful at levels below accepted decision points. We sought to develop and validate a model to predict food challenge outcome by using routinely collected data in a diverse sample of children considered suitable for food challenge. The proto-algorithm was generated by using a limited data set from 1 service (phase 1). We retrospectively applied, evaluated, and modified the initial model by using an extended data set in another center (phase 2). Finally, we prospectively validated the model in a blind study in a further group of children undergoing food challenge for peanut, milk, or egg in the second center (phase 3). Allergen-specific models were developed for peanut, egg, and milk. Phase 1 (N = 429) identified 5 clinical factors associated with diagnosis of food allergy by food challenge. In phase 2 (N = 289), we examined the predictive ability of 6 clinical factors: skin prick test, serum specific IgE, total IgE minus serum specific IgE, symptoms, sex, and age. In phase 3 (N = 70), 97% of cases were accurately predicted as positive and 94% as negative. Our model showed an advantage in clinical prediction compared with serum specific IgE only, skin prick test only, and serum specific IgE and skin prick test (92% accuracy vs 57%, and 81%, respectively). Our findings have implications for the improved delivery of food allergy-related health care, enhanced food allergy-related quality of life, and economized use of health service resources by decreasing the number of food challenges performed. Copyright © 2011 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

  5. Development and validation of a new virtual source model for portal image prediction and treatment quality control

    International Nuclear Information System (INIS)

    Chabert, Isabelle

    2015-01-01

    Intensity-Modulated Radiation Therapy (IMRT), require extensive verification procedures to ensure the correct dose delivery. Electronic Portal Imaging Devices (EPIDs) are widely used for quality assurance in radiotherapy, and also for dosimetric verifications. For this latter application, the images obtained during the treatment session can be compared to a pre-calculated reference image in order to highlight dose delivery errors. The quality control performance depends (1) on the accuracy of the pre-calculated reference image (2) on the ability of the tool used to compare images to detect errors. These two key points were studied during this PhD work. We chose to use a Monte Carlo (MC)-based method developed in the laboratory and based on the DPGLM (Dirichlet process generalized linear model) de-noising technique to predict high-resolution reference images. A model of the studied linear accelerator (linac Synergy, Elekta, Crawley, UK) was first developed using the PENELOPE MC codes, and then commissioned using measurements acquired in the Hopital Nord of Marseille. A 71 Go phase space file (PSF) stored under the flattening filter was then analyzed to build a new kind of virtual source model based on correlated histograms (200 Mo). This new and compact VSM is as much accurate as the PSF to calculate dose distributions in water if histogram sampling is based on adaptive method. The associated EPID modelling in PENELOPE suggests that hypothesis about linac primary source were too simple and should be reconsidered. The use of the VSM to predict high-resolution portal images however led to excellent results. The VSM associated to the linac and EPID MC models were used to detect errors in IMRT treatment plans. A preliminary study was conducted introducing on purpose treatment errors in portal image calculations (primary source parameters, phantom position and morphology changes). The γ-index commonly used in clinical routine appears to be less effective than the

  6. Calibrated Multi-Temporal Edge Images for City Infrastructure Growth Assessment and Prediction

    Science.gov (United States)

    Al-Ruzouq, R.; Shanableh, A.; Boharoon, Z.; Khalil, M.

    2018-03-01

    Urban Growth or urbanization can be defined as the gradual process of city's population growth and infrastructure development. It is typically demonstrated by the expansion of a city's infrastructure, mainly development of its roads and buildings. Uncontrolled urban Growth in cities has been responsible for several problems that include living environment, drinking water, noise and air pollution, waste management, traffic congestion and hydraulic processes. Accurate identification of urban growth is of great importance for urban planning and water/land management. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, allows for efficient identification of change patterns and the prediction of built-up areas. In this study, two approaches were adapted to quantify and assess the pattern of urbanization, in Ajman City at UAE, during the last three decades. The first approach relies on image processing techniques and multi-temporal Landsat satellite images with ground resolution varying between 15 to 60 meters. In this approach, the derived edge images (roads and buildings) were used as the basis of change detection. The second approach relies on digitizing features from high-resolution images captured at different years. The latest approach was adopted, as a reference and ground truth, to calibrate extracted edges from Landsat images. It has been found that urbanized area almost increased by 12 folds during the period 1975-2015 where the growth of buildings and roads were almost parallel until 2005 when the roads spatial expansion witnessed a steep increase due to the vertical expansion of the City. Extracted Edges features, were successfully used for change detection and quantification in term of buildings and roads.

  7. Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

    Directory of Open Access Journals (Sweden)

    Hong Cheng

    2017-01-01

    Pinova” apple trees were used as another variety in 2012 to develop the BPNN prediction model for the early period after June drop. The model was used in 2013, which gave similar results as those found with cv. “Gala”; (4 Conclusion: Overall, the results showed in this research that the proposed estimation models performed accurately using canopy and fruit features using image analysis algorithms.

  8. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

    Science.gov (United States)

    Nemati, Shamim; Holder, Andre; Razmi, Fereshteh; Stanley, Matthew D; Clifford, Gari D; Buchman, Timothy G

    2018-04-01

    Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January 2013 to December 2015. Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. None. High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the

  9. Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

    Science.gov (United States)

    Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

    2011-01-01

    A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

  10. Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Jiayin [School of Electronics Engineering, Huaihai Institute of Technology, Lianyungang, Jiangsu 222005, China and IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong [IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Feng [IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Lalush, David S. [Joint UNC-NCSU Department of Biomedical Engineering, North Carolina State University, Raleigh, North Carolina 27695 (United States); Lin, Weili [MRI Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2015-09-15

    Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET

  11. Prediction of standard-dose brain PET image by using MRI and low-dose brain ["1"8F]FDG PET images

    International Nuclear Information System (INIS)

    Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

    2015-01-01

    Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain ["1"8F]FDG PET image by using a low-dose brain ["1"8F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain ["1"8F]FDG PET image by low-dose brain ["1"8F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain ["1"8F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain ["1"8F]FDG PET image and substantially

  12. [Prediction of Encapsulation Temperatures of Copolymer Films in Photovoltaic Cells Using Hyperspectral Imaging Techniques and Chemometrics].

    Science.gov (United States)

    Lin, Ping; Chen, Yong-ming; Yao, Zhi-lei

    2015-11-01

    introduced to eliminate the impact of nonlinear hyperspectral data to some extent through mapping the original nonlinear hyperspectral data to the high dimensional linear feature space, so the relationship between the nonlinear hyperspectral data and the encapsulation temperatures of EVA films was fully disclosed finally. Compared with the prediction results of three proposed models, the prediction performance of LMNN was superior to the other two, whose final recognition accuracy achieved 100%. The results indicated that the methods of combination of LMNN model with the hyperspectral imaging techniques was the best one for accurately and rapidly determining the encapsulation temperatures of EVA films of photovoltaic cells. In addition, this paper had created the ideal conditions for automatically monitoring and effectively controlling the encapsulation temperatures of EVA films in the photovoltaic cells production process.

  13. An accurate algorithm to match imperfectly matched images for lung tumor detection without markers.

    Science.gov (United States)

    Rozario, Timothy; Bereg, Sergey; Yan, Yulong; Chiu, Tsuicheng; Liu, Honghuan; Kearney, Vasant; Jiang, Lan; Mao, Weihua

    2015-05-08

    In order to locate lung tumors on kV projection images without internal markers, digitally reconstructed radiographs (DRRs) are created and compared with projection images. However, lung tumors always move due to respiration and their locations change on projection images while they are static on DRRs. In addition, global image intensity discrepancies exist between DRRs and projections due to their different image orientations, scattering, and noises. This adversely affects comparison accuracy. A simple but efficient comparison algorithm is reported to match imperfectly matched projection images and DRRs. The kV projection images were matched with different DRRs in two steps. Preprocessing was performed in advance to generate two sets of DRRs. The tumors were removed from the planning 3D CT for a single phase of planning 4D CT images using planning contours of tumors. DRRs of background and DRRs of tumors were generated separately for every projection angle. The first step was to match projection images with DRRs of background signals. This method divided global images into a matrix of small tiles and similarities were evaluated by calculating normalized cross-correlation (NCC) between corresponding tiles on projections and DRRs. The tile configuration (tile locations) was automatically optimized to keep the tumor within a single projection tile that had a bad matching with the corresponding DRR tile. A pixel-based linear transformation was determined by linear interpolations of tile transformation results obtained during tile matching. The background DRRs were transformed to the projection image level and subtracted from it. The resulting subtracted image now contained only the tumor. The second step was to register DRRs of tumors to the subtracted image to locate the tumor. This method was successfully applied to kV fluoro images (about 1000 images) acquired on a Vero (BrainLAB) for dynamic tumor tracking on phantom studies. Radiation opaque markers were

  14. SU-E-J-08: A Hybrid Three Dimensional Registration Framework for Image-Guided Accurate Radiotherapy System ARTS-IGRT

    International Nuclear Information System (INIS)

    Wu, Q; Pei, X; Cao, R; Hu, L; Wu, Y

    2014-01-01

    Purpose: The purpose of this work was to develop a registration framework and method based on the software platform of ARTS-IGRT and implement in C++ based on ITK libraries to register CT images and CBCT images. ARTS-IGRT was a part of our self-developed accurate radiation planning system ARTS. Methods: Mutual information (MI) registration treated each voxel equally. Actually, different voxels even having same intensity should be treated differently in the registration procedure. According to their importance values calculated from self-information, a similarity measure was proposed which combined the spatial importance of a voxel with MI (S-MI). For lung registration, Firstly, a global alignment method was adopted to minimize the margin error and achieve the alignment of these two images on the whole. The result obtained at the low resolution level was then interpolated to become the initial conditions for the higher resolution computation. Secondly, a new similarity measurement S-MI was established to quantify how close the two input image volumes were to each other. Finally, Demons model was applied to compute the deformable map. Results: Registration tools were tested for head-neck and lung images and the average region was 128*128*49. The rigid registration took approximately 2 min and converged 10% faster than traditional MI algorithm, the accuracy reached 1mm for head-neck images. For lung images, the improved symmetric Demons registration process was completed in an average of 5 min using a 2.4GHz dual core CPU. Conclusion: A registration framework was developed to correct patient's setup according to register the planning CT volume data and the daily reconstructed 3D CBCT data. The experiments showed that the spatial MI algorithm can be adopted for head-neck images. The improved Demons deformable registration was more suitable to lung images, and rigid alignment should be applied before deformable registration to get more accurate result. Supported by

  15. Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

    Science.gov (United States)

    Rey, M.; Nikitin, A. V.; Tyuterev, V.

    2014-06-01

    Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

  16. Serial Diffusion Tensor Imaging In Vivo Predicts Long-Term Functional Recovery and Histopathology in Rats following Different Severities of Spinal Cord Injury

    Science.gov (United States)

    Patel, Samir P.; Smith, Taylor D.; VanRooyen, Jenna L.; Powell, David; Cox, David H.; Sullivan, Patrick G.

    2016-01-01

    Abstract The current study demonstrates the feasibility of using serial magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) in vivo to quantify temporally spinal cord injury (SCI) pathology in adult female Sprague-Dawley rats that were scanned prior to a moderate or severe upper lumbar contusion SCI. Injured rats were behaviorally tested for hind limb locomotion (Basso, Beattie, Bresnahan [BBB] scores) weekly for 4 weeks and scanned immediately after each session, ending with terminal gait analyses prior to euthanasia. As a measure of tissue integrity, fractional anisotropy (FA) values were significantly lower throughout the spinal cord in both injury cohorts at all time-points examined versus pre-injury. Moreover, FA values were significantly lower following severe versus moderate SCI at all time-points, and FA values at the injury epicenters at all time-points were significantly correlated with both spared white and gray matter volumes, as well as lesion volumes. Critically, quantified FA values at subacute (24 h) and all subsequent time-points were highly predictive of terminal behavior, reflected in significant correlations with both weekly BBB scores and terminal gait parameters. Critically, the finding that clinically relevant subacute (24 h) FA values accurately predict long-term functional recovery may obviate long-term studies to assess the efficacy of therapeutics tested experimentally or clinically. In summary, this study demonstrates a reproducible serial MRI procedure to predict the long-term impact of contusion SCI on both behavior and histopathology using subacute DTI metrics obtained in vivo to accurately predict multiple terminal outcome measures, which can be particularly valuable when comparing experimental interventions. PMID:26650623

  17. Prediction of Motion Induced Image Degradation Using a Markerless Motion Tracker

    DEFF Research Database (Denmark)

    Olsen, Rasmus Munch; Johannesen, Helle Hjorth; Henriksen, Otto Mølby

    In this work a markerless motion tracker, TCL2, is used to predict image quality in 3D T1 weighted MPRAGE MRI brain scans. An experienced radiologist scored the image quality for 172 scans as being usable or not usable, i.e. if a repeated scan was required. Based on five motion parameters......, a classification algorithm was trained and an accuracy for identifying not usable images of 95.9% was obtained with a sensitivity of 91.7% and specificity of 96.3%. This work shows the feasibility of the markerless motion tracker for predicting image quality with a high accuracy....

  18. Novel prediction- and subblock-based algorithm for fractal image compression

    International Nuclear Information System (INIS)

    Chung, K.-L.; Hsu, C.-H.

    2006-01-01

    Fractal encoding is the most consuming part in fractal image compression. In this paper, a novel two-phase prediction- and subblock-based fractal encoding algorithm is presented. Initially the original gray image is partitioned into a set of variable-size blocks according to the S-tree- and interpolation-based decomposition principle. In the first phase, each current block of variable-size range block tries to find the best matched domain block based on the proposed prediction-based search strategy which utilizes the relevant neighboring variable-size domain blocks. The first phase leads to a significant computation-saving effect. If the domain block found within the predicted search space is unacceptable, in the second phase, a subblock strategy is employed to partition the current variable-size range block into smaller blocks to improve the image quality. Experimental results show that our proposed prediction- and subblock-based fractal encoding algorithm outperforms the conventional full search algorithm and the recently published spatial-correlation-based algorithm by Truong et al. in terms of encoding time and image quality. In addition, the performance comparison among our proposed algorithm and the other two algorithms, the no search-based algorithm and the quadtree-based algorithm, are also investigated

  19. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

    Energy Technology Data Exchange (ETDEWEB)

    Gan, Yangzhou; Zhao, Qunfei [Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240 (China); Xia, Zeyang, E-mail: zy.xia@siat.ac.cn, E-mail: jing.xiong@siat.ac.cn; Hu, Ying [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and The Chinese University of Hong Kong, Shenzhen 518055 (China); Xiong, Jing, E-mail: zy.xia@siat.ac.cn, E-mail: jing.xiong@siat.ac.cn [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 510855 (China); Zhang, Jianwei [TAMS, Department of Informatics, University of Hamburg, Hamburg 22527 (Germany)

    2015-01-15

    Purpose: A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images. Methods: The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slice and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm{sup 3}) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods. Results: For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm{sup 3}, 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm{sup 3}, 91.57 ± 0.82%, 0.27 ± 0.02 mm, 0

  20. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

    International Nuclear Information System (INIS)

    Gan, Yangzhou; Zhao, Qunfei; Xia, Zeyang; Hu, Ying; Xiong, Jing; Zhang, Jianwei

    2015-01-01

    Purpose: A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images. Methods: The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slice and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm 3 ) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods. Results: For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm 3 , 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm 3 , 91.57 ± 0.82%, 0.27 ± 0.02 mm, 0.28 ± 0.03 mm

  1. A New Approach for Accurate Prediction of Liquid Loading of Directional Gas Wells in Transition Flow or Turbulent Flow

    Directory of Open Access Journals (Sweden)

    Ruiqing Ming

    2017-01-01

    Full Text Available Current common models for calculating continuous liquid-carrying critical gas velocity are established based on vertical wells and laminar flow without considering the influence of deviation angle and Reynolds number on liquid-carrying. With the increase of the directional well in transition flow or turbulent flow, the current common models cannot accurately predict the critical gas velocity of these wells. So we built a new model to predict continuous liquid-carrying critical gas velocity for directional well in transition flow or turbulent flow. It is shown from sensitivity analysis that the correction coefficient is mainly influenced by Reynolds number and deviation angle. With the increase of Reynolds number, the critical liquid-carrying gas velocity increases first and then decreases. And with the increase of deviation angle, the critical liquid-carrying gas velocity gradually decreases. It is indicated from the case calculation analysis that the calculation error of this new model is less than 10%, where accuracy is much higher than those of current common models. It is demonstrated that the continuous liquid-carrying critical gas velocity of directional well in transition flow or turbulent flow can be predicted accurately by using this new model.

  2. Partial volume correction and image segmentation for accurate measurement of standardized uptake value of grey matter in the brain.

    Science.gov (United States)

    Bural, Gonca; Torigian, Drew; Basu, Sandip; Houseni, Mohamed; Zhuge, Ying; Rubello, Domenico; Udupa, Jayaram; Alavi, Abass

    2015-12-01

    Our aim was to explore a novel quantitative method [based upon an MRI-based image segmentation that allows actual calculation of grey matter, white matter and cerebrospinal fluid (CSF) volumes] for overcoming the difficulties associated with conventional techniques for measuring actual metabolic activity of the grey matter. We included four patients with normal brain MRI and fluorine-18 fluorodeoxyglucose (F-FDG)-PET scans (two women and two men; mean age 46±14 years) in this analysis. The time interval between the two scans was 0-180 days. We calculated the volumes of grey matter, white matter and CSF by using a novel segmentation technique applied to the MRI images. We measured the mean standardized uptake value (SUV) representing the whole metabolic activity of the brain from the F-FDG-PET images. We also calculated the white matter SUV from the upper transaxial slices (centrum semiovale) of the F-FDG-PET images. The whole brain volume was calculated by summing up the volumes of the white matter, grey matter and CSF. The global cerebral metabolic activity was calculated by multiplying the mean SUV with total brain volume. The whole brain white matter metabolic activity was calculated by multiplying the mean SUV for the white matter by the white matter volume. The global cerebral metabolic activity only reflects those of the grey matter and the white matter, whereas that of the CSF is zero. We subtracted the global white matter metabolic activity from that of the whole brain, resulting in the global grey matter metabolism alone. We then divided the grey matter global metabolic activity by grey matter volume to accurately calculate the SUV for the grey matter alone. The brain volumes ranged between 1546 and 1924 ml. The mean SUV for total brain was 4.8-7. Total metabolic burden of the brain ranged from 5565 to 9617. The mean SUV for white matter was 2.8-4.1. On the basis of these measurements we generated the grey matter SUV, which ranged from 8.1 to 11.3. The

  3. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    Science.gov (United States)

    Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

    2018-05-01

    Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

  4. Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

    Science.gov (United States)

    Notas, George; Bariotakis, Michail; Kalogrias, Vaios; Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

  5. A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

    Science.gov (United States)

    Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

    2013-01-01

    Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

  6. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

    Science.gov (United States)

    Ben Ali, Jaouher; Chebel-Morello, Brigitte; Saidi, Lotfi; Malinowski, Simon; Fnaiech, Farhat

    2015-05-01

    Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques. In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets.

  7. The value of diffusion-weighted imaging for prediction of lasting deficit in acute stroke: an analysis of 134 patients with acute neurologic deficits

    International Nuclear Information System (INIS)

    Wiener, J.I.; King, J.T. Jr.; Moore, J.R.; Lewin, J.S.

    2001-01-01

    Acute stroke is one of the three major causes of death and disability in the United States. Now that new, and possibly effective therapy is becoming available, accurate, rapid diagnosis is important to provide timely treatment, while avoiding the risk of complications from unnecessary intervention. Our objective was to test the hypothesis that use of echo-planar (EPI) diffusion-weighted imaging (DWI) is more accurate than conventional T 2 weighted MRI in predicting progression to stroke in patients with acute ischemic neurologic deficits. We studied 134 patients presenting with acute neurologic deficits to a community hospital emergency room with both conventional MRI and DWI within 72 h of the onset of the acute deficit. We found DWI significantly more sensitive to permanent neurologic deficit at discharge (sensitivity 0.81) than conventional MRI (sensitivity 0.41). When available, DWI should be considered for routine use in patients being imaged for acute stroke. (orig.)

  8. Applying a new mammographic imaging marker to predict breast cancer risk

    Science.gov (United States)

    Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

    2018-02-01

    Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

  9. Large-Scale Multi-Resolution Representations for Accurate Interactive Image and Volume Operations

    KAUST Repository

    Sicat, Ronell B.

    2015-11-25

    and voxel footprints in input images and volumes. We show that the continuous pdfs encoded in the sparse pdf map representation enable accurate multi-resolution non-linear image operations on gigapixel images. Similarly, we show that sparse pdf volumes enable more consistent multi-resolution volume rendering compared to standard approaches, on both artificial and real world large-scale volumes. The supplementary videos demonstrate our results. In the standard approach, users heavily rely on panning and zooming interactions to navigate the data within the limits of their display devices. However, panning across the whole spatial domain and zooming across all resolution levels of large-scale images to search for interesting regions is not practical. Assisted exploration techniques allow users to quickly narrow down millions to billions of possible regions to a more manageable number for further inspection. However, existing approaches are not fully user-driven because they typically already prescribe what being of interest means. To address this, we introduce the patch sets representation for large-scale images. Patches inside a patch set are grouped and encoded according to similarity via a permutohedral lattice (p-lattice) in a user-defined feature space. Fast set operations on p-lattices facilitate patch set queries that enable users to describe what is interesting. In addition, we introduce an exploration framework—GigaPatchExplorer—for patch set-based image exploration. We show that patch sets in our framework are useful for a variety of user-driven exploration tasks in gigapixel images and whole collections thereof.

  10. Déjà vu: Motion Prediction in Static Images

    NARCIS (Netherlands)

    Pintea, S.L.; van Gemert, J.C.; Smeulders, A.W.M.; Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T.

    2014-01-01

    This paper proposes motion prediction in single still images by learning it from a set of videos. The building assumption is that similar motion is characterized by similar appearance. The proposed method learns local motion patterns given a specific appearance and adds the predicted motion in a

  11. Measuring solar reflectance - Part I: Defining a metric that accurately predicts solar heat gain

    Energy Technology Data Exchange (ETDEWEB)

    Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul [Heat Island Group, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 (United States)

    2010-09-15

    Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective ''cool colored'' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland US latitudes, this metric R{sub E891BN} can underestimate the annual peak solar heat gain of a typical roof or pavement (slope {<=} 5:12 [23 ]) by as much as 89 W m{sup -2}, and underestimate its peak surface temperature by up to 5 K. Using R{sub E891BN} to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool roof net energy savings by as much as 23%. We define clear sky air mass one global horizontal (''AM1GH'') solar reflectance R{sub g,0}, a simple and easily measured property that more accurately predicts solar heat gain. R{sub g,0} predicts the annual peak solar heat gain of a roof or pavement to within 2 W m{sup -2}, and overestimates N by no more than 3%. R{sub g,0} is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R{sub g,0} can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer. (author)

  12. Measuring solar reflectance Part I: Defining a metric that accurately predicts solar heat gain

    Energy Technology Data Exchange (ETDEWEB)

    Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul

    2010-05-14

    Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective 'cool colored' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland U.S. latitudes, this metric RE891BN can underestimate the annual peak solar heat gain of a typical roof or pavement (slope {le} 5:12 [23{sup o}]) by as much as 89 W m{sup -2}, and underestimate its peak surface temperature by up to 5 K. Using R{sub E891BN} to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool-roof net energy savings by as much as 23%. We define clear-sky air mass one global horizontal ('AM1GH') solar reflectance R{sub g,0}, a simple and easily measured property that more accurately predicts solar heat gain. R{sub g,0} predicts the annual peak solar heat gain of a roof or pavement to within 2 W m{sup -2}, and overestimates N by no more than 3%. R{sub g,0} is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R{sub g,0} can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer.

  13. Embodied memory allows accurate and stable perception of hidden objects despite orientation change.

    Science.gov (United States)

    Pan, Jing Samantha; Bingham, Ned; Bingham, Geoffrey P

    2017-07-01

    Rotating a scene in a frontoparallel plane (rolling) yields a change in orientation of constituent images. When using only information provided by static images to perceive a scene after orientation change, identification performance typically decreases (Rock & Heimer, 1957). However, rolling generates optic flow information that relates the discrete, static images (before and after the change) and forms an embodied memory that aids recognition. The embodied memory hypothesis predicts that upon detecting a continuous spatial transformation of image structure, or in other words, seeing the continuous rolling process and objects undergoing rolling observers should accurately perceive objects during and after motion. Thus, in this case, orientation change should not affect performance. We tested this hypothesis in three experiments and found that (a) using combined optic flow and image structure, participants identified locations of previously perceived but currently occluded targets with great accuracy and stability (Experiment 1); (b) using combined optic flow and image structure information, participants identified hidden targets equally well with or without 30° orientation changes (Experiment 2); and (c) when the rolling was unseen, identification of hidden targets after orientation change became worse (Experiment 3). Furthermore, when rolling was unseen, although target identification was better when participants were told about the orientation change than when they were not told, performance was still worse than when there was no orientation change. Therefore, combined optic flow and image structure information, not mere knowledge about the rolling, enables accurate and stable perception despite orientation change. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  14. Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation

    Energy Technology Data Exchange (ETDEWEB)

    Ulvestad, A. [Materials Science Division, Argonne National Laboratory, Lemont, IL 60439, USA; Menickelly, M. [Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA; Wild, S. M. [Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA

    2018-01-01

    Defects such as dislocations impact materials properties and their response during external stimuli. Imaging these defects in their native operating conditions to establish the structure-function relationship and, ultimately, to improve performance via defect engineering has remained a considerable challenge for both electron-based and x-ray-based imaging techniques. While Bragg coherent x-ray diffractive imaging (BCDI) is successful in many cases, nuances in identifying the dislocations has left manual identification as the preferred method. Derivative-based methods are also used, but they can be inaccurate and are computationally inefficient. Here we demonstrate a derivative-free method that is both more accurate and more computationally efficient than either derivative-or human-based methods for identifying 3D dislocation lines in nanocrystal images produced by BCDI. We formulate the problem as a min-max optimization problem and show exceptional accuracy for experimental images. We demonstrate a 227x speedup for a typical experimental dataset with higher accuracy over current methods. We discuss the possibility of using this algorithm as part of a sparsity-based phase retrieval process. We also provide MATLAB code for use by other researchers.

  15. Prediction suppression in monkey inferotemporal cortex depends on the conditional probability between images.

    Science.gov (United States)

    Ramachandran, Suchitra; Meyer, Travis; Olson, Carl R

    2016-01-01

    When monkeys view two images in fixed sequence repeatedly over days and weeks, neurons in area TE of the inferotemporal cortex come to exhibit prediction suppression. The trailing image elicits only a weak response when presented following the leading image that preceded it during training. Induction of prediction suppression might depend either on the contiguity of the images, as determined by their co-occurrence and captured in the measure of joint probability P(A,B), or on their contingency, as determined by their correlation and as captured in the measures of conditional probability P(A|B) and P(B|A). To distinguish between these possibilities, we measured prediction suppression after imposing training regimens that held P(A,B) constant but varied P(A|B) and P(B|A). We found that reducing either P(A|B) or P(B|A) during training attenuated prediction suppression as measured during subsequent testing. We conclude that prediction suppression depends on contingency, as embodied in the predictive relations between the images, and not just on contiguity, as embodied in their co-occurrence. Copyright © 2016 the American Physiological Society.

  16. Size matters. The width and location of a ureteral stone accurately predict the chance of spontaneous passage

    Energy Technology Data Exchange (ETDEWEB)

    Jendeberg, Johan; Geijer, Haakan; Alshamari, Muhammed; Liden, Mats [Oerebro University Hospital, Department of Radiology, Faculty of Medicine and Health, Oerebro (Sweden); Cierzniak, Bartosz [Oerebro University, Department of Surgery, Faculty of Medicine and Health, Oerebro (Sweden)

    2017-11-15

    To determine how to most accurately predict the chance of spontaneous passage of a ureteral stone using information in the diagnostic non-enhanced computed tomography (NECT) and to create predictive models with smaller stone size intervals than previously possible. Retrospectively 392 consecutive patients with ureteric stone on NECT were included. Three radiologists independently measured the stone size. Stone location, side, hydronephrosis, CRP, medical expulsion therapy (MET) and all follow-up radiology until stone expulsion or 26 weeks were recorded. Logistic regressions were performed with spontaneous stone passage in 4 weeks and 20 weeks as the dependent variable. The spontaneous passage rate in 20 weeks was 312 out of 392 stones, 98% in 0-2 mm, 98% in 3 mm, 81% in 4 mm, 65% in 5 mm, 33% in 6 mm and 9% in ≥6.5 mm wide stones. The stone size and location predicted spontaneous ureteric stone passage. The side and the grade of hydronephrosis only predicted stone passage in specific subgroups. Spontaneous passage of a ureteral stone can be predicted with high accuracy with the information available in the NECT. We present a prediction method based on stone size and location. (orig.)

  17. Slip Interface Imaging Predicts Tumor-Brain Adhesion in Vestibular Schwannomas.

    Science.gov (United States)

    Yin, Ziying; Glaser, Kevin J; Manduca, Armando; Van Gompel, Jamie J; Link, Michael J; Hughes, Joshua D; Romano, Anthony; Ehman, Richard L; Huston, John

    2015-11-01

    To test the clinical feasibility and usefulness of slip interface imaging (SII) to identify and quantify the degree of tumor-brain adhesion in patients with vestibular schwannomas. S With institutional review board approval and after obtaining written informed consent, SII examinations were performed in nine patients with vestibular schwannomas. During the SII acquisition, a low-amplitude mechanical vibration is applied to the head with a pillow-like device placed in the head coil and the resulting shear waves are imaged by using a phase-contrast pulse sequence with motion-encoding gradients synchronized with the applied vibration. Imaging was performed with a 3-T magnetic resonance (MR) system in less than 7 minutes. The acquired shear motion data were processed with two different algorithms (shear line analysis and calculation of octahedral shear strain [OSS]) to identify the degree of tumor-brain adhesion. Blinded to the SII results, neurosurgeons qualitatively assessed tumor adhesion at the time of tumor resection. Standard T2-weighted, fast imaging employing steady-state acquisition (FIESTA), and T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging were reviewed to identify the presence of cerebral spinal fluid (CSF) clefts around the tumors. The performance of the use of the CSF cleft and SII to predict the degree of tumor adhesion was evaluated by using the κ coefficient and McNemar test. Among the nine patients, SII agreed with the intraoperative assessment of the degree of tumor adhesion in eight patients (88.9%; 95% confidence interval [CI]: 57%, 98%), with four of four, three of three, and one of two cases correctly predicted as no adhesion, partial adhesion, and complete adhesion, respectively. However, the T2-weighted, FIESTA, and T2-weighted FLAIR images that used the CSF cleft sign to predict adhesion agreed with surgical findings in only four cases (44.4% [four of nine]; 95% CI: 19%, 73%). The κ coefficients indicate good agreement (0

  18. Exploring a new quantitative image marker to assess benefit of chemotherapy to ovarian cancer patients

    Science.gov (United States)

    Mirniaharikandehei, Seyedehnafiseh; Patil, Omkar; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

    2017-03-01

    Accurately assessing the potential benefit of chemotherapy to cancer patients is an important prerequisite to developing precision medicine in cancer treatment. The previous study has shown that total psoas area (TPA) measured on preoperative cross-section CT image might be a good image marker to predict long-term outcome of pancreatic cancer patients after surgery. However, accurate and automated segmentation of TPA from the CT image is difficult due to the fuzzy boundary or connection of TPA to other muscle areas. In this study, we developed a new interactive computer-aided detection (ICAD) scheme aiming to segment TPA from the abdominal CT images more accurately and assess the feasibility of using this new quantitative image marker to predict the benefit of ovarian cancer patients receiving Bevacizumab-based chemotherapy. ICAD scheme was applied to identify a CT image slice of interest, which is located at the level of L3 (vertebral spines). The cross-sections of the right and left TPA are segmented using a set of adaptively adjusted boundary conditions. TPA is then quantitatively measured. In addition, recent studies have investigated that muscle radiation attenuation which reflects fat deposition in the tissue might be a good image feature for predicting the survival rate of cancer patients. The scheme and TPA measurement task were applied to a large national clinical trial database involving 1,247 ovarian cancer patients. By comparing with manual segmentation results, we found that ICAD scheme could yield higher accuracy and consistency for this task. Using a new ICAD scheme can provide clinical researchers a useful tool to more efficiently and accurately extract TPA as well as muscle radiation attenuation as new image makers, and allow them to investigate the discriminatory power of it to predict progression-free survival and/or overall survival of the cancer patients before and after taking chemotherapy.

  19. Chlorophyll fluorescence imaging accurately quantifies freezing damage and cold acclimation responses in Arabidopsis leaves

    Directory of Open Access Journals (Sweden)

    Hincha Dirk K

    2008-05-01

    Full Text Available Abstract Background Freezing tolerance is an important factor in the geographical distribution of plants and strongly influences crop yield. Many plants increase their freezing tolerance during exposure to low, nonfreezing temperatures in a process termed cold acclimation. There is considerable natural variation in the cold acclimation capacity of Arabidopsis that has been used to study the molecular basis of this trait. Accurate methods for the quantitation of freezing damage in leaves that include spatial information about the distribution of damage and the possibility to screen large populations of plants are necessary, but currently not available. In addition, currently used standard methods such as electrolyte leakage assays are very laborious and therefore not easily applicable for large-scale screening purposes. Results We have performed freezing experiments with the Arabidopsis accessions C24 and Tenela, which differ strongly in their freezing tolerance, both before and after cold acclimation. Freezing tolerance of detached leaves was investigated using the well established electrolyte leakage assay as a reference. Chlorophyll fluorescence imaging was used as an alternative method that provides spatial resolution of freezing damage over the leaf area. With both methods, LT50 values (i.e. temperature where 50% damage occurred could be derived as quantitative measures of leaf freezing tolerance. Both methods revealed the expected differences between acclimated and nonacclimated plants and between the two accessions and LT50 values were tightly correlated. However, electrolyte leakage assays consistently yielded higher LT50 values than chlorophyll fluorescence imaging. This was to a large part due to the incubation of leaves for electrolyte leakage measurements in distilled water, which apparently led to secondary damage, while this pre-incubation was not necessary for the chlorophyll fluorescence measurements. Conclusion Chlorophyll

  20. Moment-ration imaging of seismic regions for earthquake prediction

    Science.gov (United States)

    Lomnitz, Cinna

    1993-10-01

    An algorithm for predicting large earthquakes is proposed. The reciprocal ratio (mri) of the residual seismic moment to the total moment release in a region is used for imaging seismic moment precursors. Peaks in mri predict recent major earthquakes, including the 1985 Michoacan, 1985 central Chile, and 1992 Eureka, California earthquakes.

  1. Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning

    Directory of Open Access Journals (Sweden)

    Tanel Pärnamaa

    2017-05-01

    Full Text Available High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy.

  2. Accurate Analysis of Target Characteristic in Bistatic SAR Images: A Dihedral Corner Reflectors Case.

    Science.gov (United States)

    Ao, Dongyang; Li, Yuanhao; Hu, Cheng; Tian, Weiming

    2017-12-22

    The dihedral corner reflectors are the basic geometric structure of many targets and are the main contributions of radar cross section (RCS) in the synthetic aperture radar (SAR) images. In stealth technologies, the elaborate design of the dihedral corners with different opening angles is a useful approach to reduce the high RCS generated by multiple reflections. As bistatic synthetic aperture sensors have flexible geometric configurations and are sensitive to the dihedral corners with different opening angles, they specially fit for the stealth target detections. In this paper, the scattering characteristic of dihedral corner reflectors is accurately analyzed in bistatic synthetic aperture images. The variation of RCS with the changing opening angle is formulated and the method to design a proper bistatic radar for maximizing the detection capability is provided. Both the results of the theoretical analysis and the experiments show the bistatic SAR could detect the dihedral corners, under a certain bistatic angle which is related to the geometry of target structures.

  3. Accurate Analysis of Target Characteristic in Bistatic SAR Images: A Dihedral Corner Reflectors Case

    Directory of Open Access Journals (Sweden)

    Dongyang Ao

    2017-12-01

    Full Text Available The dihedral corner reflectors are the basic geometric structure of many targets and are the main contributions of radar cross section (RCS in the synthetic aperture radar (SAR images. In stealth technologies, the elaborate design of the dihedral corners with different opening angles is a useful approach to reduce the high RCS generated by multiple reflections. As bistatic synthetic aperture sensors have flexible geometric configurations and are sensitive to the dihedral corners with different opening angles, they specially fit for the stealth target detections. In this paper, the scattering characteristic of dihedral corner reflectors is accurately analyzed in bistatic synthetic aperture images. The variation of RCS with the changing opening angle is formulated and the method to design a proper bistatic radar for maximizing the detection capability is provided. Both the results of the theoretical analysis and the experiments show the bistatic SAR could detect the dihedral corners, under a certain bistatic angle which is related to the geometry of target structures.

  4. Accurate Analysis of Target Characteristic in Bistatic SAR Images: A Dihedral Corner Reflectors Case

    Science.gov (United States)

    Ao, Dongyang; Hu, Cheng; Tian, Weiming

    2017-01-01

    The dihedral corner reflectors are the basic geometric structure of many targets and are the main contributions of radar cross section (RCS) in the synthetic aperture radar (SAR) images. In stealth technologies, the elaborate design of the dihedral corners with different opening angles is a useful approach to reduce the high RCS generated by multiple reflections. As bistatic synthetic aperture sensors have flexible geometric configurations and are sensitive to the dihedral corners with different opening angles, they specially fit for the stealth target detections. In this paper, the scattering characteristic of dihedral corner reflectors is accurately analyzed in bistatic synthetic aperture images. The variation of RCS with the changing opening angle is formulated and the method to design a proper bistatic radar for maximizing the detection capability is provided. Both the results of the theoretical analysis and the experiments show the bistatic SAR could detect the dihedral corners, under a certain bistatic angle which is related to the geometry of target structures. PMID:29271917

  5. Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.

    Science.gov (United States)

    Pärnamaa, Tanel; Parts, Leopold

    2017-05-05

    High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy. Copyright © 2017 Parnamaa and Parts.

  6. An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

    Science.gov (United States)

    De Vore, Karl W; Fatahi, Nadia M; Sass, John E

    2016-08-01

    Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.

  7. Does the emergency surgery score accurately predict outcomes in emergent laparotomies?

    Science.gov (United States)

    Peponis, Thomas; Bohnen, Jordan D; Sangji, Naveen F; Nandan, Anirudh R; Han, Kelsey; Lee, Jarone; Yeh, D Dante; de Moya, Marc A; Velmahos, George C; Chang, David C; Kaafarani, Haytham M A

    2017-08-01

    The emergency surgery score is a mortality-risk calculator for emergency general operation patients. We sought to examine whether the emergency surgery score predicts 30-day morbidity and mortality in a high-risk group of patients undergoing emergent laparotomy. Using the 2011-2012 American College of Surgeons National Surgical Quality Improvement Program database, we identified all patients who underwent emergent laparotomy using (1) the American College of Surgeons National Surgical Quality Improvement Program definition of "emergent," and (2) all Current Procedural Terminology codes denoting a laparotomy, excluding aortic aneurysm rupture. Multivariable logistic regression analyses were performed to measure the correlation (c-statistic) between the emergency surgery score and (1) 30-day mortality, and (2) 30-day morbidity after emergent laparotomy. As sensitivity analyses, the correlation between the emergency surgery score and 30-day mortality was also evaluated in prespecified subgroups based on Current Procedural Terminology codes. A total of 26,410 emergent laparotomy patients were included. Thirty-day mortality and morbidity were 10.2% and 43.8%, respectively. The emergency surgery score correlated well with mortality (c-statistic = 0.84); scores of 1, 11, and 22 correlated with mortalities of 0.4%, 39%, and 100%, respectively. Similarly, the emergency surgery score correlated well with morbidity (c-statistic = 0.74); scores of 0, 7, and 11 correlated with complication rates of 13%, 58%, and 79%, respectively. The morbidity rates plateaued for scores higher than 11. Sensitivity analyses demonstrated that the emergency surgery score effectively predicts mortality in patients undergoing emergent (1) splenic, (2) gastroduodenal, (3) intestinal, (4) hepatobiliary, or (5) incarcerated ventral hernia operation. The emergency surgery score accurately predicts outcomes in all types of emergent laparotomy patients and may prove valuable as a bedside decision

  8. A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status.

    Directory of Open Access Journals (Sweden)

    Meysam Bastani

    Full Text Available BACKGROUND: Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. METHODS: To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. RESULTS: This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. CONCLUSIONS: Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions.

  9. In vitro transcription accurately predicts lac repressor phenotype in vivo in Escherichia coli

    Directory of Open Access Journals (Sweden)

    Matthew Almond Sochor

    2014-07-01

    Full Text Available A multitude of studies have looked at the in vivo and in vitro behavior of the lac repressor binding to DNA and effector molecules in order to study transcriptional repression, however these studies are not always reconcilable. Here we use in vitro transcription to directly mimic the in vivo system in order to build a self consistent set of experiments to directly compare in vivo and in vitro genetic repression. A thermodynamic model of the lac repressor binding to operator DNA and effector is used to link DNA occupancy to either normalized in vitro mRNA product or normalized in vivo fluorescence of a regulated gene, YFP. An accurate measurement of repressor, DNA and effector concentrations were made both in vivo and in vitro allowing for direct modeling of the entire thermodynamic equilibrium. In vivo repression profiles are accurately predicted from the given in vitro parameters when molecular crowding is considered. Interestingly, our measured repressor–operator DNA affinity differs significantly from previous in vitro measurements. The literature values are unable to replicate in vivo binding data. We therefore conclude that the repressor-DNA affinity is much weaker than previously thought. This finding would suggest that in vitro techniques that are specifically designed to mimic the in vivo process may be necessary to replicate the native system.

  10. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    Science.gov (United States)

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

    Science.gov (United States)

    Lee, Jia-Ying Joey; Miller, James Alastair; Basu, Sreetama; Kee, Ting-Zhen Vanessa; Loo, Lit-Hsin

    2018-06-01

    Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide variety of chemicals is not currently available. Here, we report a study that uses high-throughput imaging and artificial intelligence to build an in vitro pulmonotoxicity assay by automatically comparing and selecting human lung-cell lines and their associated quantitative phenotypic features most predictive of in vivo pulmonotoxicity. This approach is called "High-throughput In vitro Phenotypic Profiling for Toxicity Prediction" (HIPPTox). We found that the resulting assay based on two phenotypic features of a human bronchial epithelial cell line, BEAS-2B, can accurately classify 33 reference chemicals with human pulmonotoxicity information (88.8% balance accuracy, 84.6% sensitivity, and 93.0% specificity). In comparison, the predictivity of a standard cell-viability assay on the same set of chemicals is much lower (77.1% balanced accuracy, 84.6% sensitivity, and 69.5% specificity). We also used the assay to evaluate 17 additional test chemicals with unknown/unclear human pulmonotoxicity, and experimentally confirmed that many of the pulmonotoxic reference and predicted-positive test chemicals induce DNA strand breaks and/or activation of the DNA-damage response (DDR) pathway. Therefore, HIPPTox helps us to uncover these common modes-of-action of pulmonotoxic chemicals. HIPPTox may also be applied to other cell types or models, and accelerate the development of predictive in vitro assays for other cell-type- or organ-specific toxicities.

  12. A model for predicting lung cancer response to therapy

    International Nuclear Information System (INIS)

    Seibert, Rebecca M.; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

    2007-01-01

    Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during

  13. Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content

    Energy Technology Data Exchange (ETDEWEB)

    Tourassi, Georgia [ORNL; Voisin, Sophie [ORNL; Paquit, Vincent C [ORNL; Krupinski, Elizabeth [University of Arizona

    2013-01-01

    Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By pooling the data from all radiologists machine learning produced highly accurate predictive models linking image content, gaze, cognition, and error. Merging radiologists gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the radiologists diagnostic errors while confirming 96.2% of their correct diagnoses. The radiologists individual errors could be adequately predicted by modeling the behavior of their peers. However, personalized tuning appears to be beneficial in many cases to capture more accurately individual behavior. Conclusions: Machine learning algorithms combining image features with radiologists gaze data and diagnostic decisions can be effectively developed to recognize cognitive and perceptual errors associated with the diagnostic interpretation of mammograms.

  14. A No Reference Image Quality Assessment Metric Based on Visual Perception

    Directory of Open Access Journals (Sweden)

    Yan Fu

    2016-12-01

    Full Text Available Nowadays, how to evaluate image quality reasonably is a basic and challenging problem. In view of the present no reference evaluation methods, they cannot reflect the human visual perception of image quality accurately. In this paper, we propose an efficient general-purpose no reference image quality assessment (NRIQA method based on visual perception, and effectively integrates human visual characteristics into the NRIQA fields. First, a novel algorithm for salient region extraction is presented. Two characteristics graphs of texture and edging of the original image are added to the Itti model. Due to the normalized luminance coefficients of natural images obey the generalized Gauss probability distribution, we utilize this characteristic to extract statistical features in the regions of interest (ROI and regions of non-interest respectively. Then, the extracted features are fused to be an input to establish the support vector regression (SVR model. Finally, the IQA model obtained by training is used to predict the quality of the image. Experimental results show that this method has good predictive ability, and the evaluation effect is better than existing classical algorithms. Moreover, the predicted results are more consistent with human subjective perception, which can accurately reflect the human visual perception to image quality.

  15. Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves

    Directory of Open Access Journals (Sweden)

    M.S. Borhan

    2017-12-01

    Full Text Available Facilitating non-contact measurement, a computer-imaging system was devised and evaluated to predict the chlorophyll content in potato leaves. A charge-coupled device (CCD camera paired with two optical filters and light chamber was used to acquire green (550 ± 40 nm and red band (700 ± 40 nm images from the same leaf. Potato leaves from 15 plants differing in coloration (green to yellow and age were selected for this study. Histogram based image features, such as mean and variances of green and red band images, were extracted from the histogram. Regression analyses demonstrated that the variations in SPAD meter reading could be explained by the mean gray and variances of gray scale values. The fitted least square models based on the mean gray scale levels were inversely related to the chlorophyll content of the potato leaf with a R2 of 0.87 using a green band image and with an R2 of 0.79 using a red band image. With the extracted four image features, the developed multiple linear regression model predicted the chlorophyll content with a high R2 of 0.88. The multiple regression model (using all features provided an average prediction accuracy of 85.08% and a maximum accuracy of 99.8%. The prediction model using only mean gray value of red band showed an average accuracy of 81.6% with a maximum accuracy of 99.14%. Keywords: Computer imaging, Chlorophyll, SPAD meter, Regression, Prediction accuracy

  16. Image-preprocessing method for near-wall particle image velocimetry (PIV) image interrogation with very large in-plane displacement

    International Nuclear Information System (INIS)

    Zhu, Yiding; Yuan, Huijing; Zhang, Chuanhong; Lee, Cunbiao

    2013-01-01

    Accurate particle image velocimetry (PIV) measurements very near the wall are still a great challenge. The problem is compounded by the very large in-plane displacement on PIV images commonly encountered in measurements in hypersonic boundary layers. An improved image-preprocessing method is presented in this paper which expands the traditional window deformation iterative multigrid scheme to PIV images with very large displacement. Before the interrogation, stationary artificial particles of uniform size are added homogeneously in the wall region. The mean squares of the intensities of signals in the flow and in the wall region are postulated to be equal when half the initial interrogation window overlaps the wall region. The initial estimation near the wall is then smoothed by data from both sides of the shear layer to reduce the large random uncertainties. Interrogations in the following iterative steps then converge to the correct results to provide accurate predictions for particle tracking velocimetries. Significant improvement is seen in Monte Carlo simulations and experimental tests. The algorithm successfully extracted the small flow structures of the second-mode wave in the hypersonic boundary layer from PIV images with low signal-noise-ratios when the traditional method was not successful. (paper)

  17. Investigating the link between radiologists’ gaze, diagnostic decision, and image content

    Science.gov (United States)

    Tourassi, Georgia; Voisin, Sophie; Paquit, Vincent; Krupinski, Elizabeth

    2013-01-01

    Objective To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods Gaze data and diagnostic decisions were collected from three breast imaging radiologists and three radiology residents who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Image analysis was performed in mammographic regions that attracted radiologists’ attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results By pooling the data from all readers, machine learning produced highly accurate predictive models linking image content, gaze, and cognition. Potential linking of those with diagnostic error was also supported to some extent. Merging readers’ gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the readers’ diagnostic errors while confirming 97.3% of their correct diagnoses. The readers’ individual perceptual and cognitive behaviors could be adequately predicted by modeling the behavior of others. However, personalized tuning was in many cases beneficial for capturing more accurately individual behavior. Conclusions There is clearly an interaction between radiologists’ gaze, diagnostic decision, and image content which can be modeled with machine learning algorithms. PMID:23788627

  18. IMAGE CAPTURE WITH SYNCHRONIZED MULTIPLE-CAMERAS FOR EXTRACTION OF ACCURATE GEOMETRIES

    Directory of Open Access Journals (Sweden)

    M. Koehl

    2016-06-01

    Full Text Available This paper presents a project of recording and modelling tunnels, traffic circles and roads from multiple sensors. The aim is the representation and the accurate 3D modelling of a selection of road infrastructures as dense point clouds in order to extract profiles and metrics from it. Indeed, these models will be used for the sizing of infrastructures in order to simulate exceptional convoy truck routes. The objective is to extract directly from the point clouds the heights, widths and lengths of bridges and tunnels, the diameter of gyrating and to highlight potential obstacles for a convoy. Light, mobile and fast acquisition approaches based on images and videos from a set of synchronized sensors have been tested in order to obtain useable point clouds. The presented solution is based on a combination of multiple low-cost cameras designed on an on-boarded device allowing dynamic captures. The experimental device containing GoPro Hero4 cameras has been set up and used for tests in static or mobile acquisitions. That way, various configurations have been tested by using multiple synchronized cameras. These configurations are discussed in order to highlight the best operational configuration according to the shape of the acquired objects. As the precise calibration of each sensor and its optics are major factors in the process of creation of accurate dense point clouds, and in order to reach the best quality available from such cameras, the estimation of the internal parameters of fisheye lenses of the cameras has been processed. Reference measures were also realized by using a 3D TLS (Faro Focus 3D to allow the accuracy assessment.

  19. Image Capture with Synchronized Multiple-Cameras for Extraction of Accurate Geometries

    Science.gov (United States)

    Koehl, M.; Delacourt, T.; Boutry, C.

    2016-06-01

    This paper presents a project of recording and modelling tunnels, traffic circles and roads from multiple sensors. The aim is the representation and the accurate 3D modelling of a selection of road infrastructures as dense point clouds in order to extract profiles and metrics from it. Indeed, these models will be used for the sizing of infrastructures in order to simulate exceptional convoy truck routes. The objective is to extract directly from the point clouds the heights, widths and lengths of bridges and tunnels, the diameter of gyrating and to highlight potential obstacles for a convoy. Light, mobile and fast acquisition approaches based on images and videos from a set of synchronized sensors have been tested in order to obtain useable point clouds. The presented solution is based on a combination of multiple low-cost cameras designed on an on-boarded device allowing dynamic captures. The experimental device containing GoPro Hero4 cameras has been set up and used for tests in static or mobile acquisitions. That way, various configurations have been tested by using multiple synchronized cameras. These configurations are discussed in order to highlight the best operational configuration according to the shape of the acquired objects. As the precise calibration of each sensor and its optics are major factors in the process of creation of accurate dense point clouds, and in order to reach the best quality available from such cameras, the estimation of the internal parameters of fisheye lenses of the cameras has been processed. Reference measures were also realized by using a 3D TLS (Faro Focus 3D) to allow the accuracy assessment.

  20. Flare Prediction Using Photospheric and Coronal Image Data

    Science.gov (United States)

    Jonas, Eric; Bobra, Monica; Shankar, Vaishaal; Todd Hoeksema, J.; Recht, Benjamin

    2018-03-01

    The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar-image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that i) automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and May 2014, ii) combines these features with other features based on flaring history and a physical understanding of putative flaring processes, and iii) classifies these features to predict whether a solar active region will flare within a time period of T hours, where T = 2 and 24. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We find that when optimizing for the True Skill Score (TSS), photospheric vector-magnetic-field data combined with flaring history yields the best performance, and when optimizing for the area under the precision-recall curve, all of the data are helpful. Our model performance yields a TSS of 0.84 ±0.03 and 0.81 ±0.03 in the T = 2- and 24-hour cases, respectively, and a value of 0.13 ±0.07 and 0.43 ±0.08 for the area under the precision-recall curve in the T=2- and 24-hour cases, respectively. These relatively high scores are competitive with previous attempts at solar prediction, but our different methodology and extreme care in task design and experimental setup provide an independent confirmation of these results. Given the similar values of algorithm performance across various types of models reported in the literature, we conclude that we can expect a certain baseline predictive capacity using these data. We believe that this is the first attempt to predict solar flares using photospheric vector-magnetic field data as well as multiple wavelengths of image

  1. Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model

    International Nuclear Information System (INIS)

    ZareNezhad, Bahman; Aminian, Ali

    2011-01-01

    This paper presents a new approach based on using an artificial neural network (ANN) model for predicting the acid dew points of the combustion gases in process and power plants. The most important acidic combustion gases namely, SO 3 , SO 2 , NO 2 , HCl and HBr are considered in this investigation. Proposed Network is trained using the Levenberg-Marquardt back propagation algorithm and the hyperbolic tangent sigmoid activation function is applied to calculate the output values of the neurons of the hidden layer. According to the network's training, validation and testing results, a three layer neural network with nine neurons in the hidden layer is selected as the best architecture for accurate prediction of the acidic combustion gases dew points over wide ranges of acid and moisture concentrations. The proposed neural network model can have significant application in predicting the condensation temperatures of different acid gases to mitigate the corrosion problems in stacks, pollution control devices and energy recovery systems.

  2. MR Imaging in Monitoring and Predicting Treatment Response in Multiple Sclerosis.

    Science.gov (United States)

    Río, Jordi; Auger, Cristina; Rovira, Àlex

    2017-05-01

    MR imaging is the most sensitive tool for identifying lesions in patients with multiple sclerosis (MS). MR imaging has also acquired an essential role in the detection of complications arising from these treatments and in the assessment and prediction of efficacy. In the future, other radiological measures that have shown prognostic value may be incorporated within the models for predicting treatment response. This article examines the role of MR imaging as a prognostic tool in patients with MS and the recommendations that have been proposed in recent years to monitor patients who are treated with disease-modifying drugs. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Predicting Falls in People with Multiple Sclerosis: Fall History Is as Accurate as More Complex Measures

    Directory of Open Access Journals (Sweden)

    Michelle H. Cameron

    2013-01-01

    Full Text Available Background. Many people with MS fall, but the best method for identifying those at increased fall risk is not known. Objective. To compare how accurately fall history, questionnaires, and physical tests predict future falls and injurious falls in people with MS. Methods. 52 people with MS were asked if they had fallen in the past 2 months and the past year. Subjects were also assessed with the Activities-specific Balance Confidence, Falls Efficacy Scale-International, and Multiple Sclerosis Walking Scale-12 questionnaires, the Expanded Disability Status Scale, Timed 25-Foot Walk, and computerized dynamic posturography and recorded their falls daily for the following 6 months with calendars. The ability of baseline assessments to predict future falls was compared using receiver operator curves and logistic regression. Results. All tests individually provided similar fall prediction (area under the curve (AUC 0.60–0.75. A fall in the past year was the best predictor of falls (AUC 0.75, sensitivity 0.89, specificity 0.56 or injurious falls (AUC 0.69, sensitivity 0.96, specificity 0.41 in the following 6 months. Conclusion. Simply asking people with MS if they have fallen in the past year predicts future falls and injurious falls as well as more complex, expensive, or time-consuming approaches.

  4. Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan.

    Science.gov (United States)

    Siri, Sangeeta K; Latte, Mrityunjaya V

    2017-11-01

    Many different diseases can occur in the liver, including infections such as hepatitis, cirrhosis, cancer and over effect of medication or toxins. The foremost stage for computer-aided diagnosis of liver is the identification of liver region. Liver segmentation algorithms extract liver image from scan images which helps in virtual surgery simulation, speedup the diagnosis, accurate investigation and surgery planning. The existing liver segmentation algorithms try to extort exact liver image from abdominal Computed Tomography (CT) scan images. It is an open problem because of ambiguous boundaries, large variation in intensity distribution, variability of liver geometry from patient to patient and presence of noise. A novel approach is proposed to meet challenges in extracting the exact liver image from abdominal CT scan images. The proposed approach consists of three phases: (1) Pre-processing (2) CT scan image transformation to Neutrosophic Set (NS) and (3) Post-processing. In pre-processing, the noise is removed by median filter. The "new structure" is designed to transform a CT scan image into neutrosophic domain which is expressed using three membership subset: True subset (T), False subset (F) and Indeterminacy subset (I). This transform approximately extracts the liver image structure. In post processing phase, morphological operation is performed on indeterminacy subset (I) and apply Chan-Vese (C-V) model with detection of initial contour within liver without user intervention. This resulted in liver boundary identification with high accuracy. Experiments show that, the proposed method is effective, robust and comparable with existing algorithm for liver segmentation of CT scan images. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. A methodology to accurately quantify patellofemoral cartilage contact kinematics by combining 3D image shape registration and cine-PC MRI velocity data.

    Science.gov (United States)

    Borotikar, Bhushan S; Sipprell, William H; Wible, Emily E; Sheehan, Frances T

    2012-04-05

    Patellofemoral osteoarthritis and its potential precursor patellofemoral pain syndrome (PFPS) are common, costly, and debilitating diseases. PFPS has been shown to be associated with altered patellofemoral joint mechanics; however, an actual variation in joint contact stresses has not been established due to challenges in accurately quantifying in vivo contact kinematics (area and location). This study developed and validated a method for tracking dynamic, in vivo cartilage contact kinematics by combining three magnetic resonance imaging (MRI) techniques, cine-phase contrast (CPC), multi-plane cine (MPC), and 3D high-resolution static imaging. CPC and MPC data were acquired from 12 healthy volunteers while they actively extended/flexed their knee within the MRI scanner. Since no gold standard exists for the quantification of in vivo dynamic cartilage contact kinematics, the accuracy of tracking a single point (patellar origin relative to the femur) represented the accuracy of tracking the kinematics of an entire surface. The accuracy was determined by the average absolute error between the PF kinematics derived through registration of MPC images to a static model and those derived through integration of the CPC velocity data. The accuracy ranged from 0.47 mm to 0.77 mm for the patella and femur and from 0.68 mm to 0.86 mm for the patellofemoral joint. For purely quantifying joint kinematics, CPC remains an analytically simpler and more accurate (accuracy <0.33 mm) technique. However, for application requiring the tracking of an entire surface, such as quantifying cartilage contact kinematics, this combined imaging approach produces accurate results with minimal operator intervention. Published by Elsevier Ltd.

  6. A statistical model for radar images of agricultural scenes

    Science.gov (United States)

    Frost, V. S.; Shanmugan, K. S.; Holtzman, J. C.; Stiles, J. A.

    1982-01-01

    The presently derived and validated statistical model for radar images containing many different homogeneous fields predicts the probability density functions of radar images of entire agricultural scenes, thereby allowing histograms of large scenes composed of a variety of crops to be described. Seasat-A SAR images of agricultural scenes are accurately predicted by the model on the basis of three assumptions: each field has the same SNR, all target classes cover approximately the same area, and the true reflectivity characterizing each individual target class is a uniformly distributed random variable. The model is expected to be useful in the design of data processing algorithms and for scene analysis using radar images.

  7. Implicit Beliefs about Ideal Body Image Predict Body Image Dissatisfaction

    Directory of Open Access Journals (Sweden)

    Niclas eHeider

    2015-10-01

    Full Text Available We examined whether implicit measures of actual and ideal body image can be used to predict body dissatisfaction in young female adults. Participants completed two Implicit Relational Assessment Procedures (IRAPs to examine their implicit beliefs concerning actual (e.g., I am thin and desired ideal body image (e.g., I want to be thin. Body dissatisfaction was examined via self-report questionnaires and rating scales. As expected, differences in body dissatisfaction exerted a differential influence on the two IRAP scores. Specifically, the implicit belief that one is thin was lower in participants who exhibited a high degree of body dissatisfaction than in participants who exhibited a low degree of body dissatisfaction. In contrast, the implicit desire to be thin (i.e., thin ideal body image was stronger in participants who exhibited a high level of body dissatisfaction than in participants who were less dissatisfied with their body. Adding further weight to the idea that both IRAP measures captured different underlying constructs, we also observed that they correlated differently with body mass index, explicit body dissatisfaction, and explicit measures of actual and ideal body image. More generally, these findings underscore the advantage of using implicit measures that incorporate relational information relative to implicit measures that allow for an assessment of associative relations only.

  8. A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images

    Directory of Open Access Journals (Sweden)

    Vineet Kumar

    2016-01-01

    Full Text Available Iris segmentation in the iris recognition systems is a challenging task under noncooperative environments. The iris segmentation is a process of detecting the pupil, iris’s outer boundary, and eyelids in the iris image. In this paper, we propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared (NIR illuminations and contain the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and occlusions by the eyelids, eyelashes, and eyebrow hair. In the proposed method, first, a novel edge-map is created from the iris image, which is based on combining the conventional thresholding and edge detection based segmentation techniques, and then, the general circular Hough transform (CHT is used to find the pupil circle parameters in the edge-map. Our main contribution in this research is a novel edge-map creation technique, which reduces the false edges drastically in the edge-map of the iris image and makes the pupil localization in the noisy NIR images more accurate, fast, robust, and simple. The proposed method was tested with three iris databases: CASIA-Iris-Thousand (version 4.0, CASIA-Iris-Lamp (version 3.0, and MMU (version 2.0. The average accuracy of the proposed method is 99.72% and average time cost per image is 0.727 sec.

  9. Editorial Commentary: Single-Image Slice Magnetic Resonance Imaging Assessments Do Not Predict 3-Dimensional Muscle Volume.

    Science.gov (United States)

    Brand, Jefferson C

    2016-01-01

    No single-image magnetic resonance imaging (MRI) assessment-Goutallier classification, Fuchs classification, or cross-sectional area-is predictive of whole-muscle volume or fatty atrophy of the supraspinatus or infraspinatus. Rather, 3-dimensional MRI measurement of whole-muscle volume and fat-free muscle volume is required and is associated with shoulder strength, which is clinically relevant. Three-dimensional MRI may represent a new gold standard for assessment of the rotator cuff musculature using imaging and may help to predict the feasibility of repair of a rotator cuff tear as well as the postoperative outcome. Unfortunately, 3-dimensional MRI assessment of muscle volume is labor intensive and is not widely available for clinical use. Copyright © 2016 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

  10. Predicting patterns of glioma recurrence using diffusion tensor imaging

    International Nuclear Information System (INIS)

    Price, Stephen J.; Pickard, John D.; Jena, Rajesh; Burnet, Neil G.; Carpenter, T.A.; Gillard, Jonathan H.

    2007-01-01

    Although multimodality therapy for high-grade gliomas is making some improvement in outcome, most patients will still die from their disease within a short time. We need tools that allow treatments to be tailored to an individual. In this study we used diffusion tensor imaging (DTI), a technique sensitive to subtle disruption of white-matter tracts due to tumour infiltration, to see if it can be used to predict patterns of glioma recurrence. In this study we imaged 26 patients with gliomas using DTI. Patients were imaged after 2 years or on symptomatic tumour recurrence. The diffusion tensor was split into its isotropic (p) and anisotropic (q) components, and these were plotted on T 2 -weighted images to show the pattern of DTI abnormality. This was compared to the pattern of recurrence. Three DTI patterns could be identified: (a) a diffuse pattern of abnormality where p exceeded q in all directions and was associated with diffuse increase in tumour size; (b) a localised pattern of abnormality where the tumour recurred in one particular direction; and (c) a pattern of minimal abnormality seen in some patients with or without evidence of recurrence. Diffusion tensor imaging is able to predict patterns of tumour recurrence and may allow better individualisation of tumour management and stratification for randomised controlled trials. (orig.)

  11. Predicting patterns of glioma recurrence using diffusion tensor imaging

    Energy Technology Data Exchange (ETDEWEB)

    Price, Stephen J.; Pickard, John D. [University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Addenbrooke' s Hospital, Academic Neurosurgery Unit (United Kingdom); University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Addenbrooke' s Hospital, Wolfson Brain Imaging Centre, Department of Clinical Neurosciences (United Kingdom); Jena, Rajesh; Burnet, Neil G. [University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Addenbrooke' s Hospital, University Department of Oncology (United Kingdom); Carpenter, T.A. [University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Addenbrooke' s Hospital, Wolfson Brain Imaging Centre, Department of Clinical Neurosciences (United Kingdom); Gillard, Jonathan H. [University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Addenbrooke' s Hospital, University Department of Radiology (United Kingdom)

    2007-07-15

    Although multimodality therapy for high-grade gliomas is making some improvement in outcome, most patients will still die from their disease within a short time. We need tools that allow treatments to be tailored to an individual. In this study we used diffusion tensor imaging (DTI), a technique sensitive to subtle disruption of white-matter tracts due to tumour infiltration, to see if it can be used to predict patterns of glioma recurrence. In this study we imaged 26 patients with gliomas using DTI. Patients were imaged after 2 years or on symptomatic tumour recurrence. The diffusion tensor was split into its isotropic (p) and anisotropic (q) components, and these were plotted on T{sub 2}-weighted images to show the pattern of DTI abnormality. This was compared to the pattern of recurrence. Three DTI patterns could be identified: (a) a diffuse pattern of abnormality where p exceeded q in all directions and was associated with diffuse increase in tumour size; (b) a localised pattern of abnormality where the tumour recurred in one particular direction; and (c) a pattern of minimal abnormality seen in some patients with or without evidence of recurrence. Diffusion tensor imaging is able to predict patterns of tumour recurrence and may allow better individualisation of tumour management and stratification for randomised controlled trials. (orig.)

  12. Noninvasive Multimodal Imaging to Predict Recovery of Locomotion after Extended Limb Ischemia.

    Directory of Open Access Journals (Sweden)

    Jason S Radowsky

    Full Text Available Acute limb ischemia is a common cause of morbidity and mortality following trauma both in civilian centers and in combat related injuries. Rapid determination of tissue viability and surgical restoration of blood flow are desirable, but not always possible. We sought to characterize the response to increasing periods of hind limb ischemia in a porcine model such that we could define a period of critical ischemia (the point after which irreversible neuromuscular injury occurs, evaluate non-invasive methods for characterizing that ischemia, and establish a model by which we could predict whether or not the animal's locomotion would return to baselines levels post-operatively. Ischemia was induced by either application of a pneumatic tourniquet or vessel occlusion (performed by clamping the proximal iliac artery and vein at the level of the inguinal ligament. The limb was monitored for the duration of the procedure with both 3-charge coupled device (3CCD and infrared (IR imaging for tissue oxygenation and perfusion, respectively. The experimental arms of this model are effective at inducing histologically evident muscle injury with some evidence of expected secondary organ damage, particularly in animals with longer ischemia times. Noninvasive imaging data shows excellent correlation with post-operative functional outcomes, validating its use as a non-invasive means of viability assessment, and directly monitors post-occlusive reactive hyperemia. A classification model, based on partial-least squares discriminant analysis (PLSDA of imaging variables only, successfully classified animals as "returned to normal locomotion" or "did not return to normal locomotion" with 87.5% sensitivity and 66.7% specificity after cross-validation. PLSDA models generated from non-imaging data were not as accurate (AUC of 0.53 compared the PLSDA model generated from only imaging data (AUC of 0.76. With some modification, this limb ischemia model could also serve as a

  13. Does 99mTc MAA study accurately predict the Hepatopulmonary shunt fraction of 90Y theraspheres?

    International Nuclear Information System (INIS)

    Jha, Ashish; Zade, A.; Monteiro, P.; Shah, S.; Purandare, N.C.; Rangarajan, V.; Kulkarni, S.; Kulkarni, A.; Shetty, Nitin

    2010-01-01

    Full text: Transarterial-radioembolisation (TARE) is FDA approved therapeutic option for primary and metastatic liver malignancy when patient is inoperable; which in addition to the embolic effect (as seen with Transarterial- chemoembolisation-TACE) also gives the benefit of selectively irradiation to the target lesions with minimal toxicity to adjacent normal hepatocytes. However there is a risk of shunting of radioactive spheres to pulmonary circulation and subsequent pulmonary toxicity if the hepatopulmonary shunt fraction is high. The estimated lung dose becomes the limiting factor for the dose that can be delivered trans-arterially for radioembolisation of hepatic neoplasms.This is achieved by a pretreatment 99m Tc MAA study. Aim: The accuracy of 99m Tc-MAA Scintigraphy to predict the hepatopulmonary shunt fraction of 90 Y Theraspheres was evaluated by comparing it with that obtained by post therapeutic Bremsstrahlung imaging. Materials and Methods: Patients: 13 patients who underwent 90 Y Theraspheres radioembolisation of hepatic malignancies (both primary and secondary) underwent pre therapeutic 99m Tc- MAA Scintigraphy and post therapeutic 90 Y Bremsstrahlung Scintigraphy. 10-12 mCi of freshly prepared 99m Tc MAA was administered by selective hepatic artery cauterization. Planar and tomographic images were acquired within 1hr of radiopharmaceutical administration. IMAGE ACQUISITION 99m Tc MAA static images were acquired in 256 x 256 matrix (1000 KCnts) and SPECT were a 128 x 128 matrix with 64 frames (20 s/frame). The scan parameters for CT were 140 kV, 2.5 mAs, and 1-cm slices. SPECT images were corrected for attenuation and scatter. Post therapeutic 90 Y Bremsstrahlung imaging was done with HEGP collimator with photo peak centered at 140 KeV - 64.29% and +56% window width. SPECT/CT images were obtained using a dual-detector gamma-camera with a mounted 1-row CT scanner (Infinia Hawkeye; GE medical systems) to evaluate hepatic and extra hepatic tracer

  14. Prediction of optical properties of paints

    Science.gov (United States)

    Ďurikovič, Roman; Ágošton, Tomaš

    2007-09-01

    The field of predictive rendering concerns itself with those methods of image synthesis which yield results that do not only look real, but are also radiometrically correct renditions of nature, i.e., which are accurate predictions of what a real scene would look like under given lighting conditions. A real coating consists of pigments, effect pigments, clear lacquer and glaze. A novel and unique combination of real parameters that are commonly measured in the industry and a theoretical reflectance model consisting of measurable parameters is required. Here, the authors design perception parameters and put them into well known surface reflection functions such as He and Torrance. The original contributions are the study of the sub-surface scattering of real paint and the prediction of its appearance in rendered images by the proposed model of light reflection beneath the paint surface.

  15. A Cost-Effective Transparency-Based Digital Imaging for Efficient and Accurate Wound Area Measurement

    Science.gov (United States)

    Li, Pei-Nan; Li, Hong; Wu, Mo-Li; Wang, Shou-Yu; Kong, Qing-You; Zhang, Zhen; Sun, Yuan; Liu, Jia; Lv, De-Cheng

    2012-01-01

    Wound measurement is an objective and direct way to trace the course of wound healing and to evaluate therapeutic efficacy. Nevertheless, the accuracy and efficiency of the current measurement methods need to be improved. Taking the advantages of reliability of transparency tracing and the accuracy of computer-aided digital imaging, a transparency-based digital imaging approach is established, by which data from 340 wound tracing were collected from 6 experimental groups (8 rats/group) at 8 experimental time points (Day 1, 3, 5, 7, 10, 12, 14 and 16) and orderly archived onto a transparency model sheet. This sheet was scanned and its image was saved in JPG form. Since a set of standard area units from 1 mm2 to 1 cm2 was integrated into the sheet, the tracing areas in JPG image were measured directly, using the “Magnetic lasso tool” in Adobe Photoshop program. The pixel values/PVs of individual outlined regions were obtained and recorded in an average speed of 27 second/region. All PV data were saved in an excel form and their corresponding areas were calculated simultaneously by the formula of Y (PV of the outlined region)/X (PV of standard area unit) × Z (area of standard unit). It took a researcher less than 3 hours to finish area calculation of 340 regions. In contrast, over 3 hours were expended by three skillful researchers to accomplish the above work with traditional transparency-based method. Moreover, unlike the results obtained traditionally, little variation was found among the data calculated by different persons and the standard area units in different sizes and shapes. Given its accurate, reproductive and efficient properties, this transparency-based digital imaging approach would be of significant values in basic wound healing research and clinical practice. PMID:22666449

  16. Dosimetric verification of radiation therapy including intensity modulated treatments, using an amorphous-silicon electronic portal imaging device

    Science.gov (United States)

    Chytyk-Praznik, Krista Joy

    Radiation therapy is continuously increasing in complexity due to technological innovation in delivery techniques, necessitating thorough dosimetric verification. Comparing accurately predicted portal dose images to measured images obtained during patient treatment can determine if a particular treatment was delivered correctly. The goal of this thesis was to create a method to predict portal dose images that was versatile and accurate enough to use in a clinical setting. All measured images in this work were obtained with an amorphous silicon electronic portal imaging device (a-Si EPID), but the technique is applicable to any planar imager. A detailed, physics-motivated fluence model was developed to characterize fluence exiting the linear accelerator head. The model was further refined using results from Monte Carlo simulations and schematics of the linear accelerator. The fluence incident on the EPID was converted to a portal dose image through a superposition of Monte Carlo-generated, monoenergetic dose kernels specific to the a-Si EPID. Predictions of clinical IMRT fields with no patient present agreed with measured portal dose images within 3% and 3 mm. The dose kernels were applied ignoring the geometrically divergent nature of incident fluence on the EPID. A computational investigation into this parallel dose kernel assumption determined its validity under clinically relevant situations. Introducing a patient or phantom into the beam required the portal image prediction algorithm to account for patient scatter and attenuation. Primary fluence was calculated by attenuating raylines cast through the patient CT dataset, while scatter fluence was determined through the superposition of pre-calculated scatter fluence kernels. Total dose in the EPID was calculated by convolving the total predicted incident fluence with the EPID-specific dose kernels. The algorithm was tested on water slabs with square fields, agreeing with measurement within 3% and 3 mm. The

  17. A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery

    International Nuclear Information System (INIS)

    Fu Dongshan; Kuduvalli, Gopinath

    2008-01-01

    The authors developed a fast and accurate two-dimensional (2D)-three-dimensional (3D) image registration method to perform precise initial patient setup and frequent detection and correction for patient movement during image-guided cranial radiosurgery treatment. In this method, an approximate geometric relationship is first established to decompose a 3D rigid transformation in the 3D patient coordinate into in-plane transformations and out-of-plane rotations in two orthogonal 2D projections. Digitally reconstructed radiographs are generated offline from a preoperative computed tomography volume prior to treatment and used as the reference for patient position. A multiphase framework is designed to register the digitally reconstructed radiographs with the x-ray images periodically acquired during patient setup and treatment. The registration in each projection is performed independently; the results in the two projections are then combined and converted to a 3D rigid transformation by 2D-3D geometric backprojection. The in-plane transformation and the out-of-plane rotation are estimated using different search methods, including multiresolution matching, steepest descent minimization, and one-dimensional search. Two similarity measures, optimized pattern intensity and sum of squared difference, are applied at different registration phases to optimize accuracy and computation speed. Various experiments on an anthropomorphic head-and-neck phantom showed that, using fiducial registration as a gold standard, the registration errors were 0.33±0.16 mm (s.d.) in overall translation and 0.29 deg. ±0.11 deg. (s.d.) in overall rotation. The total targeting errors were 0.34±0.16 mm (s.d.), 0.40±0.2 mm (s.d.), and 0.51±0.26 mm (s.d.) for the targets at the distances of 2, 6, and 10 cm from the rotation center, respectively. The computation time was less than 3 s on a computer with an Intel Pentium 3.0 GHz dual processor

  18. Accurate 3D kinematic measurement of temporomandibular joint using X-ray fluoroscopic images

    Science.gov (United States)

    Yamazaki, Takaharu; Matsumoto, Akiko; Sugamoto, Kazuomi; Matsumoto, Ken; Kakimoto, Naoya; Yura, Yoshiaki

    2014-04-01

    Accurate measurement and analysis of 3D kinematics of temporomandibular joint (TMJ) is very important for assisting clinical diagnosis and treatment of prosthodontics and orthodontics, and oral surgery. This study presents a new 3D kinematic measurement technique of the TMJ using X-ray fluoroscopic images, which can easily obtain the TMJ kinematic data in natural motion. In vivo kinematics of the TMJ (maxilla and mandibular bone) is determined using a feature-based 2D/3D registration, which uses beads silhouette on fluoroscopic images and 3D surface bone models with beads. The 3D surface models of maxilla and mandibular bone with beads were created from CT scans data of the subject using the mouthpiece with the seven strategically placed beads. In order to validate the accuracy of pose estimation for the maxilla and mandibular bone, computer simulation test was performed using five patterns of synthetic tantalum beads silhouette images. In the clinical applications, dynamic movement during jaw opening and closing was conducted, and the relative pose of the mandibular bone with respect to the maxilla bone was determined. The results of computer simulation test showed that the root mean square errors were sufficiently smaller than 1.0 mm and 1.0 degree. In the results of clinical application, during jaw opening from 0.0 to 36.8 degree of rotation, mandibular condyle exhibited 19.8 mm of anterior sliding relative to maxillary articular fossa, and these measurement values were clinically similar to the previous reports. Consequently, present technique was thought to be suitable for the 3D TMJ kinematic analysis.

  19. Medical image diagnosis of liver cancer using artificial intelligence

    International Nuclear Information System (INIS)

    Kondo, Tadashi; Ueno, Junji; Takao, Shoichiro

    2010-01-01

    A revised Group Method of Data Handling (GMDH)-type neural network algorithm using artificial intelligence technology for medical image diagnosis is proposed and is applied to medical image diagnosis of liver cancer. In this algorithm, the knowledge base for medical image diagnosis are used for organizing the neural network architecture for medical image diagnosis. Furthermore, the revised GMDH-type neural network algorithm has a feedback loop and can identify the characteristics of the medical images accurately using feedback loop calculations. The optimum neural network architecture fitting the complexity of the medical images is automatically organized so as to minimize the prediction error criterion defined as Prediction Sum of Squares (PSS). It is shown that the revised GMDH-type neural network can be easily applied to the medical image diagnosis. (author)

  20. Can preoperative MR imaging predict optic nerve invasion of retinoblastoma?

    International Nuclear Information System (INIS)

    Song, Kyoung Doo; Eo, Hong; Kim, Ji Hye; Yoo, So-Young; Jeon, Tae Yeon

    2012-01-01

    Purpose: To evaluate the accuracy of pre-operative MRI for the detection of optic nerve invasion in retinoblastoma. Materials and methods: Institutional review board approval and informed consent were waived for this retrospective study. A total of 41 patients were included. Inclusion criteria were histologically proven retinoblastoma, availability of diagnostic-quality preoperative MR images acquired during the 4 weeks before surgery, unilateral retinoblastoma, and normal-sized optic nerve. Two radiologists retrospectively reviewed the MR images independently. Five imaging findings (diffuse mild optic nerve enhancement, focal strong optic nerve enhancement, optic sheath enhancement, tumor location, and tumor size) were evaluated against optic nerve invasion of retinoblastoma. The predictive performance of all MR imaging findings for optic nerve invasion was also evaluated by the receiver operating characteristic curve analysis. Results: Optic nerve invasion was histopathologically confirmed in 24% of study population (10/41). The differences in diffuse mild enhancement, focal strong enhancement, optic sheath enhancement, and tumor location between patients with optic nerve invasion and patients without optic nerve invasion were not significant. Tumor sizes were 16.1 mm (SD: 2.2 mm) and 14.9 mm (SD: 3.6 mm) in patients with and without optic nerve involvement, respectively (P = 0.444). P-Values from binary logistic regression indicated that all five imaging findings were not significant predictors of tumor invasion of optic nerve. The AUC values of all MR imaging findings for the prediction of optic nerve invasion were 0.689 (95% confidence interval: 0.499–0.879) and 0.653 (95% confidence interval: 0.445–0.861) for observer 1 and observer 2, respectively. Conclusion: Findings of MRI in patients with normal-sized optic nerves have limited usefulness in preoperatively predicting the presence of optic nerve invasion in retinoblastoma.

  1. Can preoperative MR imaging predict optic nerve invasion of retinoblastoma?

    Energy Technology Data Exchange (ETDEWEB)

    Song, Kyoung Doo, E-mail: kdsong0308@gmail.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Eo, Hong, E-mail: rtombow@gmail.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Kim, Ji Hye, E-mail: jhkate.kim@samsung.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Yoo, So-Young, E-mail: sy1131.yoo@samsung.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Jeon, Tae Yeon, E-mail: hathor97.jeon@samsung.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of)

    2012-12-15

    Purpose: To evaluate the accuracy of pre-operative MRI for the detection of optic nerve invasion in retinoblastoma. Materials and methods: Institutional review board approval and informed consent were waived for this retrospective study. A total of 41 patients were included. Inclusion criteria were histologically proven retinoblastoma, availability of diagnostic-quality preoperative MR images acquired during the 4 weeks before surgery, unilateral retinoblastoma, and normal-sized optic nerve. Two radiologists retrospectively reviewed the MR images independently. Five imaging findings (diffuse mild optic nerve enhancement, focal strong optic nerve enhancement, optic sheath enhancement, tumor location, and tumor size) were evaluated against optic nerve invasion of retinoblastoma. The predictive performance of all MR imaging findings for optic nerve invasion was also evaluated by the receiver operating characteristic curve analysis. Results: Optic nerve invasion was histopathologically confirmed in 24% of study population (10/41). The differences in diffuse mild enhancement, focal strong enhancement, optic sheath enhancement, and tumor location between patients with optic nerve invasion and patients without optic nerve invasion were not significant. Tumor sizes were 16.1 mm (SD: 2.2 mm) and 14.9 mm (SD: 3.6 mm) in patients with and without optic nerve involvement, respectively (P = 0.444). P-Values from binary logistic regression indicated that all five imaging findings were not significant predictors of tumor invasion of optic nerve. The AUC values of all MR imaging findings for the prediction of optic nerve invasion were 0.689 (95% confidence interval: 0.499–0.879) and 0.653 (95% confidence interval: 0.445–0.861) for observer 1 and observer 2, respectively. Conclusion: Findings of MRI in patients with normal-sized optic nerves have limited usefulness in preoperatively predicting the presence of optic nerve invasion in retinoblastoma.

  2. Semiquantitative dynamic contrast-enhanced MRI for accurate classification of complex adnexal masses.

    Science.gov (United States)

    Kazerooni, Anahita Fathi; Malek, Mahrooz; Haghighatkhah, Hamidreza; Parviz, Sara; Nabil, Mahnaz; Torbati, Leila; Assili, Sanam; Saligheh Rad, Hamidreza; Gity, Masoumeh

    2017-02-01

    To identify the best dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) descriptive parameters in predicting malignancy of complex ovarian masses, and develop an optimal decision tree for accurate classification of benign and malignant complex ovarian masses. Preoperative DCE-MR images of 55 sonographically indeterminate ovarian masses (27 benign and 28 malignant) were analyzed prospectively. Four descriptive parameters of the dynamic curve, namely, time-to-peak (TTP), wash-in-rate (WIR), relative signal intensity (SI rel ), and the initial area under the curve (IAUC 60 ) were calculated on the normalized curves of specified regions-of-interest (ROIs). A two-tailed Student's t-test and two automated classifiers, linear discriminant analysis (LDA) and support vector machines (SVMs), were used to compare the performance of the mentioned parameters individually and in combination with each other. TTP (P = 6.15E-8) and WIR (P = 5.65E-5) parameters induced the highest sensitivity (89% for LDA, and 97% for SVM) and specificity (93% for LDA, and 100% for SVM), respectively. Regarding the high sensitivity of TTP and high specificity of WIR and through their combination, an accurate and simple decision-tree classifier was designed using the line equation obtained by LDA classification model. The proposed classifier achieved an accuracy of 89% and area under the ROC curve of 93%. In this study an accurate decision-tree classifier based on a combination of TTP and WIR parameters was proposed, which provides a clinically flexible framework to aid radiologists/clinicians to reach a conclusive preoperative diagnosis and patient-specific therapy plan for distinguishing malignant from benign complex ovarian masses. 2 J. Magn. Reson. Imaging 2017;45:418-427. © 2016 International Society for Magnetic Resonance in Medicine.

  3. Quantitative imaging features: extension of the oncology medical image database

    Science.gov (United States)

    Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

    2015-03-01

    Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

  4. Predictive images of postoperative levator resection outcome using image processing software

    OpenAIRE

    Mawatari, Yuki; Fukushima, Mikiko

    2016-01-01

    Yuki Mawatari,1 Mikiko Fukushima2 1Igo Ophthalmic Clinic, Kagoshima, 2Department of Ophthalmology, Faculty of Life Science, Kumamoto University, Chuo-ku, Kumamoto, Japan Purpose: This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection.Methods: Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller’s muscle complex (levator resection). P...

  5. Predicting suitable optoelectronic properties of monoclinic VON semiconductor crystals for photovoltaics using accurate first-principles computations

    KAUST Repository

    Harb, Moussab

    2015-01-01

    Using accurate first-principles quantum calculations based on DFT (including the perturbation theory DFPT) with the range-separated hybrid HSE06 exchange-correlation functional, we predict essential fundamental properties (such as bandgap, optical absorption coefficient, dielectric constant, charge carrier effective masses and exciton binding energy) of two stable monoclinic vanadium oxynitride (VON) semiconductor crystals for solar energy conversion applications. In addition to the predicted band gaps in the optimal range for making single-junction solar cells, both polymorphs exhibit relatively high absorption efficiencies in the visible range, high dielectric constants, high charge carrier mobilities and much lower exciton binding energies than the thermal energy at room temperature. Moreover, their optical absorption, dielectric and exciton dissociation properties are found to be better than those obtained for semiconductors frequently utilized in photovoltaic devices like Si, CdTe and GaAs. These novel results offer a great opportunity for this stoichiometric VON material to be properly synthesized and considered as a new good candidate for photovoltaic applications.

  6. Predicting suitable optoelectronic properties of monoclinic VON semiconductor crystals for photovoltaics using accurate first-principles computations

    KAUST Repository

    Harb, Moussab

    2015-08-26

    Using accurate first-principles quantum calculations based on DFT (including the perturbation theory DFPT) with the range-separated hybrid HSE06 exchange-correlation functional, we predict essential fundamental properties (such as bandgap, optical absorption coefficient, dielectric constant, charge carrier effective masses and exciton binding energy) of two stable monoclinic vanadium oxynitride (VON) semiconductor crystals for solar energy conversion applications. In addition to the predicted band gaps in the optimal range for making single-junction solar cells, both polymorphs exhibit relatively high absorption efficiencies in the visible range, high dielectric constants, high charge carrier mobilities and much lower exciton binding energies than the thermal energy at room temperature. Moreover, their optical absorption, dielectric and exciton dissociation properties are found to be better than those obtained for semiconductors frequently utilized in photovoltaic devices like Si, CdTe and GaAs. These novel results offer a great opportunity for this stoichiometric VON material to be properly synthesized and considered as a new good candidate for photovoltaic applications.

  7. A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

    Directory of Open Access Journals (Sweden)

    Magdalena Ydreborg

    Full Text Available Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI in the paper, was based on the model: Log-odds (predicting cirrhosis = -12.17+ (age × 0.11 + (BMI (kg/m(2 × 0.23 + (D7-lathosterol (μg/100 mg cholesterol×(-0.013 + (Platelet count (x10(9/L × (-0.018 + (Prothrombin-INR × 3.69. The area under the ROC curve (AUROC for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96. The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98. In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

  8. Accurate corresponding point search using sphere-attribute-image for statistical bone model generation

    International Nuclear Information System (INIS)

    Saito, Toki; Nakajima, Yoshikazu; Sugita, Naohiko; Mitsuishi, Mamoru; Hashizume, Hiroyuki; Kuramoto, Kouichi; Nakashima, Yosio

    2011-01-01

    Statistical deformable model based two-dimensional/three-dimensional (2-D/3-D) registration is a promising method for estimating the position and shape of patient bone in the surgical space. Since its accuracy depends on the statistical model capacity, we propose a method for accurately generating a statistical bone model from a CT volume. Our method employs the Sphere-Attribute-Image (SAI) and has improved the accuracy of corresponding point search in statistical model generation. At first, target bone surfaces are extracted as SAIs from the CT volume. Then the textures of SAIs are classified to some regions using Maximally-stable-extremal-regions methods. Next, corresponding regions are determined using Normalized cross-correlation (NCC). Finally, corresponding points in each corresponding region are determined using NCC. The application of our method to femur bone models was performed, and worked well in the experiments. (author)

  9. Online prediction of organileptic data for snack food using color images

    Science.gov (United States)

    Yu, Honglu; MacGregor, John F.

    2004-11-01

    In this paper, a study for the prediction of organileptic properties of snack food in real-time using RGB color images is presented. The so-called organileptic properties, which are properties based on texture, taste and sight, are generally measured either by human sensory response or by mechanical devices. Neither of these two methods can be used for on-line feedback control in high-speed production. In this situation, a vision-based soft sensor is very attractive. By taking images of the products, the samples remain untouched and the product properties can be predicted in real time from image data. Four types of organileptic properties are considered in this study: blister level, toast points, taste and peak break force. Wavelet transform are applied on the color images and the averaged absolute value for each filtered image is used as texture feature variable. In order to handle the high correlation among the feature variables, Partial Least Squares (PLS) is used to regress the extracted feature variables against the four response variables.

  10. Predicting the prognosis of non-small cell lung cancer patient treated with conservative therapy using contrast-enhanced MR imaging

    International Nuclear Information System (INIS)

    Ohno, Y.; Adachi, S.; Motoyama, A.; Sugimura, K.; Kono, M.; Kusumoto, M.

    2000-01-01

    The aim of this study was to evaluate the therapeutic effect more accurately and predict the prognosis of treated non-small cell lung cancer by using contrast-enhanced magnetic resonance imaging (CE-MRI). Contrast-enhanced computed tomography (CE-CT) and CE-MRI were examined 90 non-small cell lung cancer patients treated with conservative therapies. Enhancement patterns of CE-MRI were classified into three types: peripheral; mottled; and homogeneous. Reduction ratio of tumor size (RRT) based on the World Health Organization response criteria and a new response rate; reduction ratio of viable tumor size (RRVT) which evaluates not only the reduction of tumor size but also changes in necrosis and/or cavity size, were evaluated. Changes of enhancement pattern were compared and correlated with pathological diagnosis. The RRTs, RRVTs, and interobserver agreements evaluated by all modalities were compared. The RRTs and RRVTs in each subgroup were correlated and compared with prognoses. Change of enhancement pattern depended on therapy had no tendency (p = 0.06). Enhancement pattern had significant correlation with pathological diagnosis (p < 0.0001). Partial response (PR) case of RRVT had significant difference between imaging techniques (p = 0.04). The RRVT of other cases and RRT had no significant difference. Interobserver agreements of RRT and RRVT were almost perfect (κ≥ 0.93). Prognosis had better correlation with RRVT than with RRT. Differences of relapse-free survival and survival between patients considered as having no change (NC) by RRT and PR by RRVT (NC-PR) and patients considered as having NC by RRT and RRVT were significant (p = 0.03, p = 0.01). There were no significant differences of relapse-free survival and survival between NC-PR patients and patients considered as having PR by RRT and RRVT. The CE-MRI technique could accurately evaluate the therapeutic effect and predict the prognosis of treated non-small cell lung cancer. (orig.)

  11. Debris-flows scale predictions based on basin spatial parameters calculated from Remote Sensing images in Wenchuan earthquake area

    International Nuclear Information System (INIS)

    Zhang, Huaizhen; Chi, Tianhe; Liu, Tianyue; Wang, Wei; Yang, Lina; Zhao, Yuan; Shao, Jing; Yao, Xiaojing; Fan, Jianrong

    2014-01-01

    Debris flow is a common hazard in the Wenchuan earthquake area. Collapse and Landslide Regions (CLR), caused by earthquakes, could be located from Remote Sensing images. CLR are the direct material source regions for debris flow. The Spatial Distribution of Collapse and Landslide Regions (SDCLR) strongly impact debris-flow formation. In order to depict SDCLR, we referred to Strahler's Hypsometric analysis method and developed 3 functional models to depict SDCLR quantitatively. These models mainly depict SDCLR relative to altitude, basin mouth and main gullies of debris flow. We used the integral of functions as the spatial parameters of SDCLR and these parameters were employed during the process of debris-flows scale predictions. Grouping-occurring debris-flows triggered by the rainstorm, which occurred on September 24th 2008 in Beichuan County, Sichuan province China, were selected to build the empirical equations for debris-flows scale predictions. Given the existing data, only debris-flows runout zone parameters (Max. runout distance L and Lateral width B) were estimated in this paper. The results indicate that the predicted results were more accurate when the spatial parameters were used. Accordingly, we suggest spatial parameters of SDCLR should be considered in the process of debris-flows scale prediction and proposed several strategies to prevent debris flow in the future

  12. Quantifying the effect of microstructure variability on the yield strength predictions of Ni-base superalloys

    Energy Technology Data Exchange (ETDEWEB)

    Tiley, J.S. [Air Force Research Laboratory, Wright Patterson AFB, OH 45433 (United States); Kim, S.L.; Parthasarathy, T.A. [UES, Inc., Wright Patterson AFB, OH 45433 (United States); Loughnane, G.T. [Wright State University, Dayton, OH 45435 (United States); Kublik, R.; Salem, A.A. [Materials Resources LLC, Dayton, OH 45402 (United States)

    2017-02-08

    Physics-based models for predicting the mechanical behavior of Ni-based superalloys as a function of microstructure features require the use of microstructure data for calibration and verification. Accurate representation of the heterogeneity of microstructure features requires accurate selection of the representative microstructure data size (i.e. image size). Thus, this work is carried out to address the influence of microstructure data size on the accuracy of a discrete dislocation dynamic model in predicting the critical resolved share stress (CRSS) of IN100. Microstructure features from backscattered electron images were extracted using image processing techniques. Single point statistics (e.g. area fraction, precipitate size, and distance between γ' particles) and higher order statistics using two-point correlations were calculated from segmented 2-D images. Modified Bhattacharyya Coefficient analysis techniques were employed to calculate three-dimensional particle size distributions. Results indicate a significant influence of the microstructure data size on the calculated CRSS.

  13. Accurate shear measurement with faint sources

    International Nuclear Information System (INIS)

    Zhang, Jun; Foucaud, Sebastien; Luo, Wentao

    2015-01-01

    For cosmic shear to become an accurate cosmological probe, systematic errors in the shear measurement method must be unambiguously identified and corrected for. Previous work of this series has demonstrated that cosmic shears can be measured accurately in Fourier space in the presence of background noise and finite pixel size, without assumptions on the morphologies of galaxy and PSF. The remaining major source of error is source Poisson noise, due to the finiteness of source photon number. This problem is particularly important for faint galaxies in space-based weak lensing measurements, and for ground-based images of short exposure times. In this work, we propose a simple and rigorous way of removing the shear bias from the source Poisson noise. Our noise treatment can be generalized for images made of multiple exposures through MultiDrizzle. This is demonstrated with the SDSS and COSMOS/ACS data. With a large ensemble of mock galaxy images of unrestricted morphologies, we show that our shear measurement method can achieve sub-percent level accuracy even for images of signal-to-noise ratio less than 5 in general, making it the most promising technique for cosmic shear measurement in the ongoing and upcoming large scale galaxy surveys

  14. Predicting Performance of a Face Recognition System Based on Image Quality

    NARCIS (Netherlands)

    Dutta, A.

    2015-01-01

    In this dissertation, we focus on several aspects of models that aim to predict performance of a face recognition system. Performance prediction models are commonly based on the following two types of performance predictor features: a) image quality features; and b) features derived solely from

  15. Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

    Science.gov (United States)

    Rahmati, Mehdi

    2017-08-01

    Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

  16. Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

    Science.gov (United States)

    Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

    2016-01-01

    Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

  17. Mini-Mental Status Examination: a short form of MMSE was as accurate as the original MMSE in predicting dementia

    DEFF Research Database (Denmark)

    Schultz-Larsen, Kirsten; Lomholt, Rikke Kirstine; Kreiner, Svend

    2006-01-01

    .4%), and positive predictive value (71.0%) but equal area under the receiver operating characteristic curve. Cross-validation on follow-up data confirmed the results. CONCLUSION: A short, valid MMSE, which is as sensitive and specific as the original MMSE for the screening of cognitive impairments and dementia......OBJECTIVES: This study assesses the properties of the Mini-Mental State Examination (MMSE) with the purpose of improving the efficiencies of the methods of screening for cognitive impairment and dementia. A specific purpose was to determine whether an abbreviated version would be as accurate...... is attractive for research and clinical practice, particularly if predictive power can be enhanced by combining the short MMSE with neuropsychological tests or informant reports....

  18. Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

    Science.gov (United States)

    Hengl, Tomislav; Heuvelink, Gerard B. M.; Perčec Tadić, Melita; Pebesma, Edzer J.

    2012-01-01

    A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval

  19. Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

    Science.gov (United States)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2002-01-01

    NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

  20. Imaging of congenital diaphragmatic hernias

    International Nuclear Information System (INIS)

    Taylor, George A.; Estroff, Judy A.; Atalabi, Omolola M.

    2009-01-01

    Congenital diaphragmatic hernias are complex and life-threatening lesions that are not just anatomic defects of the diaphragm, but represent a complex set of physiologic derangements of the lung, the pulmonary vasculature, and related structures. Imaging plays an increasingly important role in the care of these infants. Prenatal sonography and MRI have allowed early and accurate identification of the defect and associated anomalies. These tools have also been the key to defining the degree of pulmonary hypoplasia and to predicting neonatal survival and need for aggressive respiratory rescue strategies. In the postnatal period, conventional radiography supplemented by cross-sectional imaging in selected cases can be very useful in sorting out the differential diagnosis of intrathoracic masses, in the detection of associated anomalies, and in the management of complications. Understanding the pathogenesis of diaphragmatic defects, the underlying physiologic disturbances, and the strengths and limitations of current imaging protocols is essential to the effective and accurate management of these complex patients. (orig.)

  1. Imaging of congenital diaphragmatic hernias

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, George A.; Estroff, Judy A. [Harvard Medical School, Department of Radiology, Children' s Hospital Boston, Boston, MA (United States); Atalabi, Omolola M. [Harvard Medical School, Department of Radiology, Children' s Hospital Boston, Boston, MA (United States); The College of Medicine/University College Hospital, Ibadan (Nigeria)

    2009-01-15

    Congenital diaphragmatic hernias are complex and life-threatening lesions that are not just anatomic defects of the diaphragm, but represent a complex set of physiologic derangements of the lung, the pulmonary vasculature, and related structures. Imaging plays an increasingly important role in the care of these infants. Prenatal sonography and MRI have allowed early and accurate identification of the defect and associated anomalies. These tools have also been the key to defining the degree of pulmonary hypoplasia and to predicting neonatal survival and need for aggressive respiratory rescue strategies. In the postnatal period, conventional radiography supplemented by cross-sectional imaging in selected cases can be very useful in sorting out the differential diagnosis of intrathoracic masses, in the detection of associated anomalies, and in the management of complications. Understanding the pathogenesis of diaphragmatic defects, the underlying physiologic disturbances, and the strengths and limitations of current imaging protocols is essential to the effective and accurate management of these complex patients. (orig.)

  2. Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

    Science.gov (United States)

    Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

    2018-03-01

    Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error model predicts the exposure of

  3. Does magnetic resonance imaging predict future low back pain?

    DEFF Research Database (Denmark)

    Steffens, D; Hancock, M J; Maher, C G

    2014-01-01

    Magnetic resonance imaging (MRI) has the potential to identify pathology responsible for low back pain (LBP). However, the importance of findings on MRI remains controversial. We aimed to systematically review whether MRI findings of the lumbar spine predict future LBP in different samples...

  4. An efficient adaptive arithmetic coding image compression technology

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Yun Jiao-Jiao; Zhang Yong-Lei

    2011-01-01

    This paper proposes an efficient lossless image compression scheme for still images based on an adaptive arithmetic coding compression algorithm. The algorithm increases the image coding compression rate and ensures the quality of the decoded image combined with the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the code book information. We adopt an adaptive arithmetic coding algorithm for image compression that greatly improves the image compression rate. The results show that it is an effective compression technology. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

  5. Star tracking method based on multiexposure imaging for intensified star trackers.

    Science.gov (United States)

    Yu, Wenbo; Jiang, Jie; Zhang, Guangjun

    2017-07-20

    The requirements for the dynamic performance of star trackers are rapidly increasing with the development of space exploration technologies. However, insufficient knowledge of the angular acceleration has largely decreased the performance of the existing star tracking methods, and star trackers may even fail to track under highly dynamic conditions. This study proposes a star tracking method based on multiexposure imaging for intensified star trackers. The accurate estimation model of the complete motion parameters, including the angular velocity and angular acceleration, is established according to the working characteristic of multiexposure imaging. The estimation of the complete motion parameters is utilized to generate the predictive star image accurately. Therefore, the correct matching and tracking between stars in the real and predictive star images can be reliably accomplished under highly dynamic conditions. Simulations with specific dynamic conditions are conducted to verify the feasibility and effectiveness of the proposed method. Experiments with real starry night sky observation are also conducted for further verification. Simulations and experiments demonstrate that the proposed method is effective and shows excellent performance under highly dynamic conditions.

  6. Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?

    Science.gov (United States)

    De Robertis, Riccardo; Maris, Bogdan; Cardobi, Nicolò; Tinazzi Martini, Paolo; Gobbo, Stefano; Capelli, Paola; Ortolani, Silvia; Cingarlini, Sara; Paiella, Salvatore; Landoni, Luca; Butturini, Giovanni; Regi, Paolo; Scarpa, Aldo; Tortora, Giampaolo; D'Onofrio, Mirko

    2018-06-01

    To evaluate MRI derived whole-tumour histogram analysis parameters in predicting pancreatic neuroendocrine neoplasm (panNEN) grade and aggressiveness. Pre-operative MR of 42 consecutive patients with panNEN >1 cm were retrospectively analysed. T1-/T2-weighted images and ADC maps were analysed. Histogram-derived parameters were compared to histopathological features using the Mann-Whitney U test. Diagnostic accuracy was assessed by ROC-AUC analysis; sensitivity and specificity were assessed for each histogram parameter. ADC entropy was significantly higher in G2-3 tumours with ROC-AUC 0.757; sensitivity and specificity were 83.3 % (95 % CI: 61.2-94.5) and 61.1 % (95 % CI: 36.1-81.7). ADC kurtosis was higher in panNENs with vascular involvement, nodal and hepatic metastases (p= .008, .021 and .008; ROC-AUC= 0.820, 0.709 and 0.820); sensitivity and specificity were: 85.7/74.3 % (95 % CI: 42-99.2 /56.4-86.9), 36.8/96.5 % (95 % CI: 17.2-61.4 /76-99.8) and 100/62.8 % (95 % CI: 56.1-100/44.9-78.1). No significant differences between groups were found for other histogram-derived parameters (p >.05). Whole-tumour histogram analysis of ADC maps may be helpful in predicting tumour grade, vascular involvement, nodal and liver metastases in panNENs. ADC entropy and ADC kurtosis are the most accurate parameters for identification of panNENs with malignant behaviour. • Whole-tumour ADC histogram analysis can predict aggressiveness in pancreatic neuroendocrine neoplasms. • ADC entropy and kurtosis are higher in aggressive tumours. • ADC histogram analysis can quantify tumour diffusion heterogeneity. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information for prognostication.

  7. Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students' Preconceptions of Electric Circuits?

    Science.gov (United States)

    Lin, Jing-Wen

    2016-01-01

    Holding scientific conceptions and having the ability to accurately predict students' preconceptions are a prerequisite for science teachers to design appropriate constructivist-oriented learning experiences. This study explored the types and sources of students' preconceptions of electric circuits. First, 438 grade 3 (9 years old) students were…

  8. Intraoperative panoramic image using alignment grid, is it accurate?

    Science.gov (United States)

    Apivatthakakul, T; Duanghakrung, M; Luevitoonvechkit, S; Patumasutra, S

    2013-07-01

    Minimally invasive orthopedic trauma surgery relies heavily on intraoperative fluoroscopic images to evaluate the quality of fracture reduction and fixation. However, fluoroscopic images have a narrow field of view and often cannot visualize the entire long bone axis. To compare the coronal femoral alignment between conventional X-rays to that achieved with a new method of acquiring a panoramic intraoperative image. Twenty-four cadaveric femurs with simple diaphyseal fractures were fixed with an angulated broad DCP to create coronal plane malalignment. An intraoperative alignment grid was used to help stitch different fluoroscopic images together to produce a panoramic image. A conventional X-ray of the entire femur was then performed. The coronal plane angulation in the panoramic images was then compared to the conventional X-rays using a Wilcoxon signed rank test. The mean angle measured from the panoramic view was 173.9° (range 169.3°-178.0°) with median of 173.2°. The mean angle measured from the conventional X-ray was 173.4° (range 167.7°-178.7°) with a median angle of 173.5°. There was no significant difference between both methods of measurement (P = 0.48). Panoramic images produced by stitching fluoroscopic images together with help of an alignment grid demonstrated the same accuracy at evaluating the coronal plane alignment of femur fractures as conventional X-rays.

  9. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

    Science.gov (United States)

    Xie, Tian; Grossman, Jeffrey C.

    2018-04-01

    The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

  10. Ultra Deep Wave Equation Imaging and Illumination

    Energy Technology Data Exchange (ETDEWEB)

    Alexander M. Popovici; Sergey Fomel; Paul Sava; Sean Crawley; Yining Li; Cristian Lupascu

    2006-09-30

    In this project we developed and tested a novel technology, designed to enhance seismic resolution and imaging of ultra-deep complex geologic structures by using state-of-the-art wave-equation depth migration and wave-equation velocity model building technology for deeper data penetration and recovery, steeper dip and ultra-deep structure imaging, accurate velocity estimation for imaging and pore pressure prediction and accurate illumination and amplitude processing for extending the AVO prediction window. Ultra-deep wave-equation imaging provides greater resolution and accuracy under complex geologic structures where energy multipathing occurs, than what can be accomplished today with standard imaging technology. The objective of the research effort was to examine the feasibility of imaging ultra-deep structures onshore and offshore, by using (1) wave-equation migration, (2) angle-gathers velocity model building, and (3) wave-equation illumination and amplitude compensation. The effort consisted of answering critical technical questions that determine the feasibility of the proposed methodology, testing the theory on synthetic data, and finally applying the technology for imaging ultra-deep real data. Some of the questions answered by this research addressed: (1) the handling of true amplitudes in the downward continuation and imaging algorithm and the preservation of the amplitude with offset or amplitude with angle information required for AVO studies, (2) the effect of several imaging conditions on amplitudes, (3) non-elastic attenuation and approaches for recovering the amplitude and frequency, (4) the effect of aperture and illumination on imaging steep dips and on discriminating the velocities in the ultra-deep structures. All these effects were incorporated in the final imaging step of a real data set acquired specifically to address ultra-deep imaging issues, with large offsets (12,500 m) and long recording time (20 s).

  11. Prediction of foal carcass composition and wholesale cut yields by using video image analysis.

    Science.gov (United States)

    Lorenzo, J M; Guedes, C M; Agregán, R; Sarriés, M V; Franco, D; Silva, S R

    2018-01-01

    This work represents the first contribution for the application of the video image analysis (VIA) technology in predicting lean meat and fat composition in the equine species. Images of left sides of the carcass (n=42) were captured from the dorsal, lateral and medial views using a high-resolution digital camera. A total of 41 measurements (angles, lengths, widths and areas) were obtained by VIA. The variation of percentage of lean meat obtained from the forequarter (FQ) and hindquarter (HQ) carcass ranged between 5.86% and 7.83%. However, the percentage of fat (FAT) obtained from the FQ and HQ carcass presented a higher variation (CV between 41.34% and 44.58%). By combining different measurements and using prediction models with cold carcass weight (CCW) and VIA measurement the coefficient of determination (k-fold-R 2) were 0.458 and 0.532 for FQ and HQ, respectively. On the other hand, employing the most comprehensive model (CCW plus all VIA measurements), the k-fold-R 2 increased from 0.494 to 0.887 and 0.513 to 0.878 with respect to the simplest model (only with CCW), while precision increased with the reduction in the root mean square error (2.958 to 0.947 and 1.841 to 0.787) for the hindquarter fat and lean percentage, respectively. With CCW plus VIA measurements is possible to explain the wholesale value cuts yield variation (k-fold-R 2 between 0.533 and 0.889). Overall, the VIA technology performed in the present study could be considered as an accurate method to assess the horse carcass composition which could have a role in breeding programmes and research studies to assist in the development of a value-based marketing system for horse carcass.

  12. Regional lymph node staging using lymphotropic nanoparticle enhanced magnetic resonance imaging with ferumoxtran-10 in patients with penile cancer.

    Science.gov (United States)

    Tabatabaei, Shahin; Harisinghani, Mukesh; McDougal, W Scott

    2005-09-01

    We evaluated lymphotropic nanoparticle enhanced magnetic resonance imaging (LNMRI) with ferumoxtran-10 in determining the presence of regional lymph node metastases in patients with penile cancer. Seven patients with squamous cell carcinoma of the penis underwent LNMRI. All patients subsequently underwent groin dissection and the nodal images were correlated with histology. We found that LNMRI had sensitivity, specificity, and positive and negative predictive values of 100%, 97%, 81.2% and 100%, respectively, in predicting the presence of regional lymph node metastases in patients with penile cancer. Lymph node scanning using LNMRI accurately predicts the pathological status of regional lymph nodes in patients with cancer of the penis. LNMRI may accurately triage patients for regional lymphadenectomy.

  13. Predicting Reactive Transport Dynamics in Carbonates using Initial Pore Structure

    Science.gov (United States)

    Menke, H. P.; Nunes, J. P. P.; Blunt, M. J.

    2017-12-01

    Understanding rock-fluid interaction at the pore-scale is imperative for accurate predictive modelling of carbon storage permanence. However, coupled reactive transport models are computationally expensive, requiring either a sacrifice of resolution or high performance computing to solve relatively simple geometries. Many recent studies indicate that initial pore structure many be the dominant mechanism in determining the dissolution regime. Here we investigate how well the initial pore structure is predictive of distribution and amount of dissolution during reactive flow using particle tracking on the initial image. Two samples of carbonate rock with varying initial pore space heterogeneity were reacted with reservoir condition CO2-saturated brine and scanned dynamically during reactive flow at a 4-μm resolution between 4 and 40 times using 4D X-ray micro-tomography over the course of 1.5 hours using μ-CT. Flow was modelled on the initial binarized image using a Navier-Stokes solver. Particle tracking was then run on the velocity fields, the streamlines were traced, and the streamline density was calculated both on a voxel-by-voxel and a channel-by-channel basis. The density of streamlines was then compared to the amount of dissolution in subsequent time steps during reaction. It was found that for the flow and transport regimes studied, the streamline density distribution in the initial image accurately predicted the dominant pathways of dissolution and gave good indicators of the type of dissolution regime that would later develop. This work suggests that the eventual reaction-induced changes in pore structure are deterministic rather than stochastic and can be predicted with high resolution imaging of unreacted rock.

  14. Use of fundus autofluorescence images to predict geographic atrophy progression.

    Science.gov (United States)

    Bearelly, Srilaxmi; Khanifar, Aziz A; Lederer, David E; Lee, Jane J; Ghodasra, Jason H; Stinnett, Sandra S; Cousins, Scott W

    2011-01-01

    Fundus autofluorescence imaging has been shown to be helpful in predicting progression of geographic atrophy (GA) secondary to age-related macular degeneration. We assess the ability of fundus autofluorescence imaging to predict rate of GA progression using a simple categorical scheme. Subjects with GA secondary to age-related macular degeneration with fundus autofluorescence imaging acquired at least 12 months apart were included. Rim area focal hyperautofluorescence was defined as percentage of the 500-μm-wide margin bordering the GA that contained increased autofluorescence. Rim area focal hyperautofluorescence on baseline fundus autofluorescence images was assessed and categorized depending on the extent of rim area focal hyperautofluorescence (Category 1: ≤33%; Category 2: between 33 and 67%; Category 3: ≥67%). Total GA areas at baseline and follow-up were measured to calculate change in GA progression. Forty-five eyes of 45 subjects were included; average duration of follow-up was 18.5 months. Median growth rates differed among categories of baseline rim area focal hyperautofluorescence (P = 0.01 among Categories 1, 2, and 3; P = 0.008 for Category 1 compared with Category 3, Jonckheere-Terpstra test). A simple categorical scheme that stratifies the amount of increased autofluorescence in the 500-μm margin bordering GA may be used to differentiate faster and slower progressors.

  15. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory.

    Science.gov (United States)

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; Drisdell, Walter S; Shirley, Eric L; Prendergast, David

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can be rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.

  16. Development of a method to accurately calculate the Dpb and quickly predict the strength of a chemical bond

    International Nuclear Information System (INIS)

    Du, Xia; Zhao, Dong-Xia; Yang, Zhong-Zhi

    2013-01-01

    Highlights: ► A method from new respect to characterize and measure the bond strength is proposed. ► We calculate the D pb of a series of various bonds to justify our approach. ► A quite good linear relationship of the D pb with the bond lengths for series of various bonds is shown. ► Take the prediction of strengths of C–H and N–H bonds for base pairs in DNA as a practical application of our method. - Abstract: A new approach to characterize and measure bond strength has been developed. First, we propose a method to accurately calculate the potential acting on an electron in a molecule (PAEM) at the saddle point along a chemical bond in situ, denoted by D pb . Then, a direct method to quickly evaluate bond strength is established. We choose some familiar molecules as models for benchmarking this method. As a practical application, the D pb of base pairs in DNA along C–H and N–H bonds are obtained for the first time. All results show that C 7 –H of A–T and C 8 –H of G–C are the relatively weak bonds that are the injured positions in DNA damage. The significance of this work is twofold: (i) A method is developed to calculate D pb of various sizable molecules in situ quickly and accurately; (ii) This work demonstrates the feasibility to quickly predict the bond strength in macromolecules

  17. How does image noise affect actual and predicted human gaze allocation in assessing image quality?

    Science.gov (United States)

    Röhrbein, Florian; Goddard, Peter; Schneider, Michael; James, Georgina; Guo, Kun

    2015-07-01

    A central research question in natural vision is how to allocate fixation to extract informative cues for scene perception. With high quality images, psychological and computational studies have made significant progress to understand and predict human gaze allocation in scene exploration. However, it is unclear whether these findings can be generalised to degraded naturalistic visual inputs. In this eye-tracking and computational study, we methodically distorted both man-made and natural scenes with Gaussian low-pass filter, circular averaging filter and Additive Gaussian white noise, and monitored participants' gaze behaviour in assessing perceived image qualities. Compared with original high quality images, distorted images attracted fewer numbers of fixations but longer fixation durations, shorter saccade distance and stronger central fixation bias. This impact of image noise manipulation on gaze distribution was mainly determined by noise intensity rather than noise type, and was more pronounced for natural scenes than for man-made scenes. We furthered compared four high performing visual attention models in predicting human gaze allocation in degraded scenes, and found that model performance lacked human-like sensitivity to noise type and intensity, and was considerably worse than human performance measured as inter-observer variance. Furthermore, the central fixation bias is a major predictor for human gaze allocation, which becomes more prominent with increased noise intensity. Our results indicate a crucial role of external noise intensity in determining scene-viewing gaze behaviour, which should be considered in the development of realistic human-vision-inspired attention models. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

    Science.gov (United States)

    Unger, Jakob; Sun, Tianchen; Chen, Yi-Ling; Phipps, Jennifer E.; Bold, Richard J.; Darrow, Morgan A.; Ma, Kwan-Liu; Marcu, Laura

    2018-01-01

    An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67 mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

  19. Multispectral code excited linear prediction coding and its application in magnetic resonance images.

    Science.gov (United States)

    Hu, J H; Wang, Y; Cahill, P T

    1997-01-01

    This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

  20. Advances in Retinal Optical Imaging

    Directory of Open Access Journals (Sweden)

    Yanxiu Li

    2018-04-01

    Full Text Available Retinal imaging has undergone a revolution in the past 50 years to allow for better understanding of the eye in health and disease. Significant improvements have occurred both in hardware such as lasers and optics in addition to software image analysis. Optical imaging modalities include optical coherence tomography (OCT, OCT angiography (OCTA, photoacoustic microscopy (PAM, scanning laser ophthalmoscopy (SLO, adaptive optics (AO, fundus autofluorescence (FAF, and molecular imaging (MI. These imaging modalities have enabled improved visualization of retinal pathophysiology and have had a substantial impact on basic and translational medical research. These improvements in technology have translated into early disease detection, more accurate diagnosis, and improved management of numerous chorioretinal diseases. This article summarizes recent advances and applications of retinal optical imaging techniques, discusses current clinical challenges, and predicts future directions in retinal optical imaging.

  1. SU-E-J-134: An Augmented-Reality Optical Imaging System for Accurate Breast Positioning During Radiotherapy

    International Nuclear Information System (INIS)

    Nazareth, D; Malhotra, H; French, S; Hoffmann, K; Merrow, C

    2014-01-01

    Purpose: Breast radiotherapy, particularly electronic compensation, may involve large dose gradients and difficult patient positioning problems. We have developed a simple self-calibrating augmented-reality system, which assists in accurately and reproducibly positioning the patient, by displaying her live image from a single camera superimposed on the correct perspective projection of her 3D CT data. Our method requires only a standard digital camera capable of live-view mode, installed in the treatment suite at an approximately-known orientation and position (rotation R; translation T). Methods: A 10-sphere calibration jig was constructed and CT imaged to provide a 3D model. The (R,T) relating the camera to the CT coordinate system were determined by acquiring a photograph of the jig and optimizing an objective function, which compares the true image points to points calculated with a given candidate R and T geometry. Using this geometric information, 3D CT patient data, viewed from the camera's perspective, is plotted using a Matlab routine. This image data is superimposed onto the real-time patient image, acquired by the camera, and displayed using standard live-view software. This enables the therapists to view both the patient's current and desired positions, and guide the patient into assuming the correct position. The method was evaluated using an in-house developed bolus-like breast phantom, mounted on a supporting platform, which could be tilted at various angles to simulate treatment-like geometries. Results: Our system allowed breast phantom alignment, with an accuracy of about 0.5 cm and 1 ± 0.5 degree. Better resolution could be possible using a camera with higher-zoom capabilities. Conclusion: We have developed an augmented-reality system, which combines a perspective projection of a CT image with a patient's real-time optical image. This system has the potential to improve patient setup accuracy during breast radiotherapy, and could possibly be

  2. SU-E-J-134: An Augmented-Reality Optical Imaging System for Accurate Breast Positioning During Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Nazareth, D; Malhotra, H; French, S [Roswell Park Cancer Institute, Buffalo, NY (United States); Hoffmann, K [Neurosurgery at SUNY at Buffalo, Buffalo, NY (United States); Merrow, C [Bassett Healthcare, Oneonta, NY (United States)

    2014-06-01

    Purpose: Breast radiotherapy, particularly electronic compensation, may involve large dose gradients and difficult patient positioning problems. We have developed a simple self-calibrating augmented-reality system, which assists in accurately and reproducibly positioning the patient, by displaying her live image from a single camera superimposed on the correct perspective projection of her 3D CT data. Our method requires only a standard digital camera capable of live-view mode, installed in the treatment suite at an approximately-known orientation and position (rotation R; translation T). Methods: A 10-sphere calibration jig was constructed and CT imaged to provide a 3D model. The (R,T) relating the camera to the CT coordinate system were determined by acquiring a photograph of the jig and optimizing an objective function, which compares the true image points to points calculated with a given candidate R and T geometry. Using this geometric information, 3D CT patient data, viewed from the camera's perspective, is plotted using a Matlab routine. This image data is superimposed onto the real-time patient image, acquired by the camera, and displayed using standard live-view software. This enables the therapists to view both the patient's current and desired positions, and guide the patient into assuming the correct position. The method was evaluated using an in-house developed bolus-like breast phantom, mounted on a supporting platform, which could be tilted at various angles to simulate treatment-like geometries. Results: Our system allowed breast phantom alignment, with an accuracy of about 0.5 cm and 1 ± 0.5 degree. Better resolution could be possible using a camera with higher-zoom capabilities. Conclusion: We have developed an augmented-reality system, which combines a perspective projection of a CT image with a patient's real-time optical image. This system has the potential to improve patient setup accuracy during breast radiotherapy, and could

  3. Fingerprint image reconstruction for swipe sensor using Predictive Overlap Method

    Directory of Open Access Journals (Sweden)

    Mardiansyah Ahmad Zafrullah

    2018-01-01

    Full Text Available Swipe sensor is one of many biometric authentication sensor types that widely applied to embedded devices. The sensor produces an overlap on every pixel block of the image, so the picture requires a reconstruction process before heading to the feature extraction process. Conventional reconstruction methods require extensive computation, causing difficult to apply to embedded devices that have limited computing process. In this paper, image reconstruction is proposed using predictive overlap method, which determines the image block shift from the previous set of change data. The experiments were performed using 36 images generated by a swipe sensor with 128 x 8 pixels size of the area, where each image has an overlap in each block. The results reveal computation can increase up to 86.44% compared with conventional methods, with accuracy decreasing to 0.008% in average.

  4. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

    Science.gov (United States)

    Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie N. C.; Tomaszewski, John; González, Fabio A.; Madabhushi, Anant

    2017-04-01

    With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.

  5. Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information

    International Nuclear Information System (INIS)

    Kim, Kil Joong; Kim, Bohyoung; Lee, Hyunna; Choi, Hosik; Jeon, Jong-June; Ahn, Jeong-Hwan; Lee, Kyoung Ho

    2011-01-01

    Purpose: To propose multiple logistic regression (MLR) and artificial neural network (ANN) models constructed using digital imaging and communications in medicine (DICOM) header information in predicting the fidelity of Joint Photographic Experts Group (JPEG) 2000 compressed abdomen computed tomography (CT) images. Methods: Our institutional review board approved this study and waived informed patient consent. Using a JPEG2000 algorithm, 360 abdomen CT images were compressed reversibly (n = 48, as negative control) or irreversibly (n = 312) to one of different compression ratios (CRs) ranging from 4:1 to 10:1. Five radiologists independently determined whether the original and compressed images were distinguishable or indistinguishable. The 312 irreversibly compressed images were divided randomly into training (n = 156) and testing (n = 156) sets. The MLR and ANN models were constructed regarding the DICOM header information as independent variables and the pooled radiologists' responses as dependent variable. As independent variables, we selected the CR (DICOM tag number: 0028, 2112), effective tube current-time product (0018, 9332), section thickness (0018, 0050), and field of view (0018, 0090) among the DICOM tags. Using the training set, an optimal subset of independent variables was determined by backward stepwise selection in a four-fold cross-validation scheme. The MLR and ANN models were constructed with the determined independent variables using the training set. The models were then evaluated on the testing set by using receiver-operating-characteristic (ROC) analysis regarding the radiologists' pooled responses as the reference standard and by measuring Spearman rank correlation between the model prediction and the number of radiologists who rated the two images as distinguishable. Results: The CR and section thickness were determined as the optimal independent variables. The areas under the ROC curve for the MLR and ANN predictions were 0.91 (95% CI; 0

  6. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

    Directory of Open Access Journals (Sweden)

    Petros-Pavlos Ypsilantis

    Full Text Available Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.

  7. Accurate thickness measurement of graphene

    International Nuclear Information System (INIS)

    Shearer, Cameron J; Slattery, Ashley D; Stapleton, Andrew J; Shapter, Joseph G; Gibson, Christopher T

    2016-01-01

    Graphene has emerged as a material with a vast variety of applications. The electronic, optical and mechanical properties of graphene are strongly influenced by the number of layers present in a sample. As a result, the dimensional characterization of graphene films is crucial, especially with the continued development of new synthesis methods and applications. A number of techniques exist to determine the thickness of graphene films including optical contrast, Raman scattering and scanning probe microscopy techniques. Atomic force microscopy (AFM), in particular, is used extensively since it provides three-dimensional images that enable the measurement of the lateral dimensions of graphene films as well as the thickness, and by extension the number of layers present. However, in the literature AFM has proven to be inaccurate with a wide range of measured values for single layer graphene thickness reported (between 0.4 and 1.7 nm). This discrepancy has been attributed to tip-surface interactions, image feedback settings and surface chemistry. In this work, we use standard and carbon nanotube modified AFM probes and a relatively new AFM imaging mode known as PeakForce tapping mode to establish a protocol that will allow users to accurately determine the thickness of graphene films. In particular, the error in measuring the first layer is reduced from 0.1–1.3 nm to 0.1–0.3 nm. Furthermore, in the process we establish that the graphene-substrate adsorbate layer and imaging force, in particular the pressure the tip exerts on the surface, are crucial components in the accurate measurement of graphene using AFM. These findings can be applied to other 2D materials. (paper)

  8. Comparison of Model Predictions of Image Quality with Results of Clinical Trials in Chest and Lumbar Spine Screen-film Imaging

    International Nuclear Information System (INIS)

    Sandborg, M.; McVey, G.; Dance, D.R.; Carlsson, G.A.

    2000-01-01

    The ability to predict image quality from known physical and technical parameters is a prerequisite for making successful dose optimisation. In this study, imaging systems have been simulated using a Monte Carlo model of the imaging systems. The model includes a voxelised human anatomy and quantifies image quality in terms of contrast and signal-to-noise ratio for 5-6 anatomical details included in the anatomy. The imaging systems used in clinical trials were simulated and the ranking of the systems by the model and radiologists compared. The model and the results of the trial for chest PA both show that using a high maximum optical density was significantly better than using a low one. The model predicts that a good system is characterised by a large dynamic range and a high contrast of the blood vessels in the retrocardiac area. The ranking by the radiologists and the model agreed for the lumbar spine AP. (author)

  9. Can magnetic resonance imaging predict the success of parturition in oxytocin-induced pregnant women?

    International Nuclear Information System (INIS)

    Sabir, N.; Akkemik, B.; Dicle, O.; Yurdakul, B.

    2000-01-01

    The aim of this study was to assess whether magnetic resonance imaging could predict the outcome of attempted vaginal delivery in a group of pregnant women whose parturition had to be induced by oxytocin. The signal intensity and morphology alterations in the cervix of 21 full-term pregnant women were analyzed before the induction of parturition. T2-weighted gradient echo sequences were utilized and signal intensity in the cervix was measured from the anterior and posterior lips of the cervix. An index indicating the brightness range of the cervix was formulated to overcome the effects of the individual intensity changes. Imaging features including the signal intensity and the evidence of effacement were correlated with the actual type of delivery performed. Images were also assessed visually by two independent radiologists. Statistical analysis of brightness indexes that were considered to have a predictive value as an indicator for possible delivery was not significant. However, visually assessed signal intensity of the cervix correlated strongly with the type of delivery. Effacement itself was the most reliable parameter in predicting the progress of the delivery. In conclusion, MR imaging seems to be useful for predicting normal parturition in full-term pregnant women who need oxytocin induction. However, the presence of effacement seems to be a more reliable and practical parameter that will be preferred in that prediction. (orig.)

  10. Accurate Prediction of Coronary Artery Disease Using Bioinformatics Algorithms

    Directory of Open Access Journals (Sweden)

    Hajar Shafiee

    2016-06-01

    Full Text Available Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by 2030. According to the latest statistics reported by Iran’s Minister of health, 3.39% of all deaths are attributed to cardiovascular diseases and 19.5% are related to myocardial infarction. The aim of this study was to predict coronary artery disease using data mining algorithms. Methods: In this study, various bioinformatics algorithms, such as decision trees, neural networks, support vector machines, clustering, etc., were used to predict coronary heart disease. The data used in this study was taken from several valid databases (including 14 data. Results: In this research, data mining techniques can be effectively used to diagnose different diseases, including coronary artery disease. Also, for the first time, a prediction system based on support vector machine with the best possible accuracy was introduced. Conclusion: The results showed that among the features, thallium scan variable is the most important feature in the diagnosis of heart disease. Designation of machine prediction models, such as support vector machine learning algorithm can differentiate between sick and healthy individuals with 100% accuracy.

  11. Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description.

    Science.gov (United States)

    Gu, Ke; Jakhetiya, Vinit; Qiao, Jun-Fei; Li, Xiaoli; Lin, Weisi; Thalmann, Daniel

    2017-07-28

    New challenges have been brought out along with the emerging of 3D-related technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). Free viewpoint video (FVV), due to its applications in remote surveillance, remote education, etc, based on the flexible selection of direction and viewpoint, has been perceived as the development direction of next-generation video technologies and has drawn a wide range of researchers' attention. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a reliable real-time blind quality evaluation and monitoring system is urgently required. But existing assessment metrics do not render human judgments faithfully mainly because geometric distortions are generated by DIBR. To this end, this paper proposes a novel referenceless quality metric of DIBR-synthesized images using the autoregression (AR)-based local image description. It was found that, after the AR prediction, the reconstructed error between a DIBR-synthesized image and its AR-predicted image can accurately capture the geometry distortion. The visual saliency is then leveraged to modify the proposed blind quality metric to a sizable margin. Experiments validate the superiority of our no-reference quality method as compared with prevailing full-, reduced- and no-reference models.

  12. Prospective regularization design in prior-image-based reconstruction

    International Nuclear Information System (INIS)

    Dang, Hao; Siewerdsen, Jeffrey H; Stayman, J Webster

    2015-01-01

    Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in

  13. Comprehensive model for predicting perceptual image quality of smart mobile devices.

    Science.gov (United States)

    Gong, Rui; Xu, Haisong; Luo, M R; Li, Haifeng

    2015-01-01

    An image quality model for smart mobile devices was proposed based on visual assessments of several image quality attributes. A series of psychophysical experiments were carried out on two kinds of smart mobile devices, i.e., smart phones and tablet computers, in which naturalness, colorfulness, brightness, contrast, sharpness, clearness, and overall image quality were visually evaluated under three lighting environments via categorical judgment method for various application types of test images. On the basis of Pearson correlation coefficients and factor analysis, the overall image quality could first be predicted by its two constituent attributes with multiple linear regression functions for different types of images, respectively, and then the mathematical expressions were built to link the constituent image quality attributes with the physical parameters of smart mobile devices and image appearance factors. The procedure and algorithms were applicable to various smart mobile devices, different lighting conditions, and multiple types of images, and performance was verified by the visual data.

  14. A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

    Science.gov (United States)

    Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

    2018-06-01

    The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  15. Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images.

    Science.gov (United States)

    Nateghi, Ramin; Danyali, Habibollah; Helfroush, Mohammad Sadegh

    2017-08-14

    Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell. Then a novel Maximized Inter-class Weighted Mean (MIWM) method is proposed that aims at reducing the number of extracted non-mitosis candidates that results in reducing the false positive mitosis detection rate. Finally, segmented candidates are classified into mitosis and non-mitosis classes by using a support vector machine (SVM) classifier. Experimental results demonstrate a significant improvement in accuracy of mitosis cells detection in different grades of breast cancer histopathological images.

  16. Watershed area ratio accurately predicts daily streamflow in nested catchments in the Catskills, New York

    Directory of Open Access Journals (Sweden)

    Chris C. Gianfagna

    2015-09-01

    New hydrological insights for the region: Watershed area ratio was the most important basin parameter for estimating flow at upstream sites based on downstream flow. The area ratio alone explained 93% of the variance in the slopes of relationships between upstream and downstream flows. Regression analysis indicated that flow at any upstream point can be estimated by multiplying the flow at a downstream reference gage by the watershed area ratio. This method accurately predicted upstream flows at area ratios as low as 0.005. We also observed a very strong relationship (R2 = 0.79 between area ratio and flow–flow slopes in non-nested catchments. Our results indicate that a simple flow estimation method based on watershed area ratios is justifiable, and indeed preferred, for the estimation of daily streamflow in ungaged watersheds in the Catskills region.

  17. Assessing the predictive capability of optical imaging techniques, Spatial Frequency Domain Imaging (SFDI) and Laser Speckle Imaging (LSI), to the gold standard of clinical assessment in a controlled animal model

    Science.gov (United States)

    Ponticorvo, A.; Rowland, R.; Baldado, M.; Burmeister, D. M.; Christy, R. J.; Bernal, N.; Durkin, A. J.

    2018-02-01

    The current standard for assessment of burn severity and subsequent wound healing is through clinical examination, which is highly subjective. Accurate early assessment of burn severity is critical for dictating the course of wound management. Complicating matters is the fact that burn wounds are often large and can have multiple regions that vary in severity. In order to manage the treatment more effectively, a tool that can provide spatially resolved information related to mapping burn severity could aid clinicians when making decisions. Several new technologies focus on burn care in an attempt to help clinicians objectively determine burn severity. By quantifying perfusion, laser speckle imaging (LSI) has had success in categorizing burn wound severity at earlier time points than clinical assessment alone. Additionally, spatial frequency domain imaging (SFDI) is a new technique that can quantify the tissue structural damage associated with burns to achieve earlier categorization of burn severity. Here we compared the performance of a commercial LSI device (PeriCam PSI, Perimed Inc.), a SFDI device (Reflect RSTM, Modulated Imaging Inc.) and conventional clinical assessment in a controlled (porcine) model of graded burn wound severity over the course of 28 days. Specifically we focused on the ability of each system to predict the spatial heterogeneity of the healed wound at 28 days, based on the images at an early time point. Spatial heterogeneity was defined by clinical assessment of distinct regions of healing on day 28. Across six pigs, 96 burn wounds (3 cm diameter) were created. Clinical assessment at day 28 indicated that 39 had appeared to heal in a heterogeneous manner. Clinical observation at day 1 found 35 / 39 (90%) to be spatially heterogeneous in terms of burn severity. The LSI system was able to detect spatial heterogeneity of burn severity in 14 / 39 (36%) cases on day 1 and 23 / 39 cases (59%) on day 7. By contrast the SFDI system was able to

  18. An accurate and efficient system model of iterative image reconstruction in high-resolution pinhole SPECT for small animal research

    Energy Technology Data Exchange (ETDEWEB)

    Huang, P-C; Hsu, C-H [Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan (China); Hsiao, I-T [Department Medical Imaging and Radiological Sciences, Chang Gung University, Tao-Yuan, Taiwan (China); Lin, K M [Medical Engineering Research Division, National Health Research Institutes, Zhunan Town, Miaoli County, Taiwan (China)], E-mail: cghsu@mx.nthu.edu.tw

    2009-06-15

    Accurate modeling of the photon acquisition process in pinhole SPECT is essential for optimizing resolution. In this work, the authors develop an accurate system model in which pinhole finite aperture and depth-dependent geometric sensitivity are explicitly included. To achieve high-resolution pinhole SPECT, the voxel size is usually set in the range of sub-millimeter so that the total number of image voxels increase accordingly. It is inevitably that a system matrix that models a variety of favorable physical factors will become extremely sophisticated. An efficient implementation for such an accurate system model is proposed in this research. We first use the geometric symmetries to reduce redundant entries in the matrix. Due to the sparseness of the matrix, only non-zero terms are stored. A novel center-to-radius recording rule is also developed to effectively describe the relation between a voxel and its related detectors at every projection angle. The proposed system matrix is also suitable for multi-threaded computing. Finally, the accuracy and effectiveness of the proposed system model is evaluated in a workstation equipped with two Quad-Core Intel X eon processors.

  19. Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

    Science.gov (United States)

    Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

    2017-12-12

    Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

  20. Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

    Science.gov (United States)

    Beukinga, Roelof J; Hulshoff, Jan B; van Dijk, Lisanne V; Muijs, Christina T; Burgerhof, Johannes G M; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Slump, Cornelis H; Mul, Véronique E M; Plukker, John Th M

    2017-05-01

    Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUV max in 18 F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and 18 F-FDG PET/CT-derived textural features. Methods: From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment 18 F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18 F-FDG PET and CT. The current most accurate prediction model with SUV max as a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the 18 F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUV max model. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion

  1. Modality prediction of biomedical literature images using multimodal feature representation

    Directory of Open Access Journals (Sweden)

    Pelka, Obioma

    2016-08-01

    Full Text Available This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text representation was obtained by vector quantisation on a Bag-of-Words dictionary generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.

  2. Three-dimensional ultrasound image-guided robotic system for accurate microwave coagulation of malignant liver tumours.

    Science.gov (United States)

    Xu, Jing; Jia, Zhen-zhong; Song, Zhang-jun; Yang, Xiang-dong; Chen, Ken; Liang, Ping

    2010-09-01

    The further application of conventional ultrasound (US) image-guided microwave (MW) ablation of liver cancer is often limited by two-dimensional (2D) imaging, inaccurate needle placement and the resulting skill requirement. The three-dimensional (3D) image-guided robotic-assisted system provides an appealing alternative option, enabling the physician to perform consistent, accurate therapy with improved treatment effectiveness. Our robotic system is constructed by integrating an imaging module, a needle-driven robot, a MW thermal field simulation module, and surgical navigation software in a practical and user-friendly manner. The robot executes precise needle placement based on the 3D model reconstructed from freehand-tracked 2D B-scans. A qualitative slice guidance method for fine registration is introduced to reduce the placement error caused by target motion. By incorporating the 3D MW specific absorption rate (SAR) model into the heat transfer equation, the MW thermal field simulation module determines the MW power level and the coagulation time for improved ablation therapy. Two types of wrists are developed for the robot: a 'remote centre of motion' (RCM) wrist and a non-RCM wrist, which is preferred in real applications. The needle placement accuracies were robot with the RCM wrist was improved to 1.6 +/- 1.0 mm when real-time 2D US feedback was used in the artificial-tissue phantom experiment. By using the slice guidance method, the robot with the non-RCM wrist achieved accuracy of 1.8 +/- 0.9 mm in the ex vivo experiment; even target motion was introduced. In the thermal field experiment, a 5.6% relative mean error was observed between the experimental coagulated neurosis volume and the simulation result. The proposed robotic system holds promise to enhance the clinical performance of percutaneous MW ablation of malignant liver tumours. Copyright 2010 John Wiley & Sons, Ltd.

  3. Towards more accurate and reliable predictions for nuclear applications

    International Nuclear Information System (INIS)

    Goriely, S.

    2015-01-01

    The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models. Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications. In the present contribution, the reliability and accuracy of recent nuclear theories are discussed for most of the relevant quantities needed to estimate reaction cross sections and beta-decay rates, namely nuclear masses, nuclear level densities, gamma-ray strength, fission properties and beta-strength functions. It is shown that nowadays, mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenogical inputs in the prediction of nuclear data. While fundamental nuclear physicists keep on improving state-of-the-art models, e.g. within the shell model or ab initio models, nuclear applications could make use of their most recent results as quantitative constraints or guides to improve the predictions in energy or mass domain that will remain inaccessible experimentally. (orig.)

  4. Predictive value of MR imaging-dependent and non-MR imaging-dependent parameters for recurrence of laryngeal cancer after radiation therapy

    NARCIS (Netherlands)

    Castelijns, J. A.; van den Brekel, M. W.; Smit, E. M.; Tobi, H.; van Wagtendonk, F. W.; Golding, R. P.; Venema, H. W.; van Schaik, C.; Snow, G. B.

    1995-01-01

    To determine the predictive value of several clinical and radiologic parameters for recurrence of laryngeal cancer. Eighty previously untreated patients underwent magnetic resonance (MR) imaging before radiation therapy with curative intent. Tumor volume was calculated from T1-weighted MR images.

  5. Feedforward signal prediction for accurate motion systems using digital filters

    NARCIS (Netherlands)

    Butler, H.

    2012-01-01

    A positioning system that needs to accurately track a reference can benefit greatly from using feedforward. When using a force actuator, the feedforward needs to generate a force proportional to the reference acceleration, which can be measured by means of an accelerometer or can be created by

  6. A Comparative Study of Spectral Auroral Intensity Predictions From Multiple Electron Transport Models

    Science.gov (United States)

    Grubbs, Guy; Michell, Robert; Samara, Marilia; Hampton, Donald; Hecht, James; Solomon, Stanley; Jahn, Jorg-Micha

    2018-01-01

    It is important to routinely examine and update models used to predict auroral emissions resulting from precipitating electrons in Earth's magnetotail. These models are commonly used to invert spectral auroral ground-based images to infer characteristics about incident electron populations when in situ measurements are unavailable. In this work, we examine and compare auroral emission intensities predicted by three commonly used electron transport models using varying electron population characteristics. We then compare model predictions to same-volume in situ electron measurements and ground-based imaging to qualitatively examine modeling prediction error. Initial comparisons showed differences in predictions by the GLobal airglOW (GLOW) model and the other transport models examined. Chemical reaction rates and radiative rates in GLOW were updated using recent publications, and predictions showed better agreement with the other models and the same-volume data, stressing that these rates are important to consider when modeling auroral processes. Predictions by each model exhibit similar behavior for varying atmospheric constants, energies, and energy fluxes. Same-volume electron data and images are highly correlated with predictions by each model, showing that these models can be used to accurately derive electron characteristics and ionospheric parameters based solely on multispectral optical imaging data.

  7. Histological image classification using biologically interpretable shape-based features

    International Nuclear Information System (INIS)

    Kothari, Sonal; Phan, John H; Young, Andrew N; Wang, May D

    2013-01-01

    Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions

  8. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

    NARCIS (Netherlands)

    Cole, James H.; Poudel, Rudra P. K.; Tsagkrasoulis, Dimosthenis; Caan, Matthan W. A.; Steves, Claire; Spector, Tim D.; Montana, Giovanni

    2017-01-01

    Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of

  9. Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

    Science.gov (United States)

    Su, X.

    2017-12-01

    A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

  10. Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry

    DEFF Research Database (Denmark)

    Mollerup, Christian Brinch; Mardal, Marie; Dalsgaard, Petur Weihe

    2018-01-01

    artificial neural networks (ANNs). Prediction was based on molecular descriptors, 827 RTs, and 357 CCS values from pharmaceuticals, drugs of abuse, and their metabolites. ANN models for the prediction of RT or CCS separately were examined, and the potential to predict both from a single model......Exact mass, retention time (RT), and collision cross section (CCS) are used as identification parameters in liquid chromatography coupled to ion mobility high resolution accurate mass spectrometry (LC-IM-HRMS). Targeted screening analyses are now more flexible and can be expanded for suspect...

  11. On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data.

    Science.gov (United States)

    Aloufi, Samah; Zhu, Shiai; El Saddik, Abdulmotaleb

    2017-03-19

    The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user's preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image's visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner's popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining promising results in the

  12. Accurate microRNA target prediction correlates with protein repression levels

    Directory of Open Access Journals (Sweden)

    Simossis Victor A

    2009-09-01

    Full Text Available Abstract Background MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. Results DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Conclusion Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

  13. Prediction of lateral lymph node metastasis by magnetic resonance imaging

    International Nuclear Information System (INIS)

    Hatano, Satoshi; Kumamoto, Kensuke; Ishibashi, Keiichiro

    2010-01-01

    Considering the advantages and disadvantages of lateral lymph node dissection in patients with advanced lower rectal cancer, it would be ideal to select candidates for lateral lymph node dissection by preoperative imaging study including magnetic resonance imaging (MRI). We have reported that the cut-off value of minimal diameter of lateral lymph node could be set at 6 mm for indication of lateral lymph node dissection. In the present study, we evaluated whether it would be appropriate to apply the cut-off value of minimal diameter of lateral lymph node in MRI. Forty-four patients with advanced lower rectal cancer underwent a curative surgery with lateral lymph node dissection or sampling from 1997 to 2009 in our institute. Among them, 25 patients received MRI preoperatively and analyzed. The images were obtained by a sagittal method that was diagonal along sacro-iliac joint with 5 mm thick sections. Lateral lymph node metastasis was detected in 5 cases, one side in 4 cases and both sides in 1 case. The sensitivity, specificity, positive predict value, and accuracy for predicting metastasis was 50%, 90%, 42.9% and 84.8% respectively, when the cut-off value of the minimal diameter was set at 6 mm in MRI. Our results indicated that a 6 mm set as the cut-off value of minimal diameter of lateral lymph node was suitable for the prediction of lateral lymph node metastasis since the accuracy was relatively high (84.8%), though it was hardly to detect metastatic lymph node less than 6 mm. (author)

  14. [Prediction of lateral lymph node metastasis by magnetic resonance imaging].

    Science.gov (United States)

    Hatano, Satoshi; Kumamoto, Kensuke; Ishibashi, Keiichiro; Ishiguro, Toru; Ohsawa, Tomonori; Okada, Norimichi; Nakata, Hiroshi; Yokoyama, Masaru; Haga, Norihiro; Ishida, Hideyuki

    2010-11-01

    Considering the advantages and disadvantages of lateral lymph node dissection in patients with advanced lower rectal cancer, it would be ideal to select candidates for lateral lymph node dissection by preoperative imaging study including magnetic resonance imaging(MRI). We have reported that the cut-off value of minimal diameter of lateral lymph node could be set at 6 mm for indication of lateral lymph node dissection. In the present study, we evaluated whether it would be appropriate to apply the cut-off value of minimal diameter of lateral lymph node in MRI. Forty-four patients with advanced lower rectal cancer underwent a curative surgery with lateral lymph node dissection or sampling from 1997 to 2009 in our institute. Among them, 25 patients received MRI preoperatively and analyzed. The images were obtained by a sagittal method that was diagonal along sacro-iliac joint with 5 mm thick sections. Lateral lymph node metastasis was detected in 5 cases, one side in 4 cases and both sides in 1 case. The sensitivity, specificity, positive predict value, and accuracy for predicting metastasis was 50%, 90%, 42.9% and 84.8% respectively, when the cut-off value of the minimal diameter was set at 6 mm in MRI. Our results indicated that a 6 mm set as the cut-off value of minimal diameter of lateral lymph node was suitable for the prediction of lateral lymph node metastasis since the accuracy was relatively high (84.8%), though it was hardly to detect metastatic lymph node less than 6 mm.

  15. Short-term memory predictions across the lifespan: monitoring span before and after conducting a task.

    Science.gov (United States)

    Bertrand, Julie Marilyne; Moulin, Chris John Anthony; Souchay, Céline

    2017-05-01

    Our objective was to explore metamemory in short-term memory across the lifespan. Five age groups participated in this study: 3 groups of children (4-13 years old), and younger and older adults. We used a three-phase task: prediction-span-postdiction. For prediction and postdiction phases, participants reported with a Yes/No response if they could recall in order a series of images. For the span task, they had to actually recall such series. From 4 years old, children have some ability to monitor their short-term memory and are able to adjust their prediction after experiencing the task. However, accuracy still improves significantly until adolescence. Although the older adults had a lower span, they were as accurate as young adults in their evaluation, suggesting that metamemory is unimpaired for short-term memory tasks in older adults. •We investigate metamemory for short-term memory tasks across the lifespan. •We find younger children cannot accurately predict their span length. •Older adults are accurate in predicting their span length. •People's metamemory accuracy was related to their short-term memory span.

  16. An improved technique for the prediction of optimal image resolution ...

    African Journals Online (AJOL)

    Past studies to predict optimal image resolution required for generating spatial information for savannah ecosystems have yielded different outcomes, hence providing a knowledge gap that was investigated in the present study. The postulation, for the present study, was that by graphically solving two simultaneous ...

  17. Deep supervised dictionary learning for no-reference image quality assessment

    Science.gov (United States)

    Huang, Yuge; Liu, Xuesong; Tian, Xiang; Zhou, Fan; Chen, Yaowu; Jiang, Rongxin

    2018-03-01

    We propose a deep convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA), i.e., accurate prediction of image quality without a reference image. The proposed model consists of three components such as a local feature extractor that is a fully CNN, an encoding module with an inherent dictionary that aggregates local features to output a fixed-length global quality-aware image representation, and a regression module that maps the representation to an image quality score. Our model can be trained in an end-to-end manner, and all of the parameters, including the weights of the convolutional layers, the dictionary, and the regression weights, are simultaneously learned from the loss function. In addition, the model can predict quality scores for input images of arbitrary sizes in a single step. We tested our method on commonly used image quality databases and showed that its performance is comparable with that of state-of-the-art general-purpose NR-IQA algorithms.

  18. Improvement of a land surface model for accurate prediction of surface energy and water balances

    International Nuclear Information System (INIS)

    Katata, Genki

    2009-02-01

    In order to predict energy and water balances between the biosphere and atmosphere accurately, sophisticated schemes to calculate evaporation and adsorption processes in the soil and cloud (fog) water deposition on vegetation were implemented in the one-dimensional atmosphere-soil-vegetation model including CO 2 exchange process (SOLVEG2). Performance tests in arid areas showed that the above schemes have a significant effect on surface energy and water balances. The framework of the above schemes incorporated in the SOLVEG2 and instruction for running the model are documented. With further modifications of the model to implement the carbon exchanges between the vegetation and soil, deposition processes of materials on the land surface, vegetation stress-growth-dynamics etc., the model is suited to evaluate an effect of environmental loads to ecosystems by atmospheric pollutants and radioactive substances under climate changes such as global warming and drought. (author)

  19. [Study on method of tracking the active cells in image sequences based on EKF-PF].

    Science.gov (United States)

    Tang, Chunming; Liu, Ying

    2013-02-01

    In cell image sequences, due to the nonlinear and nonGaussian motion characteristics of active cells, the accurate prediction and tracking is still an unsolved problem. We applied extended Kalman particle filter (EKF-PF) here in our study, attempting to solve the problem. Firstly we confirmed the existence and positions of the active cells. Then we established a motion model and improved it via adding motion angle estimation. Next we predicted motion parameters, such as displacement, velocity, accelerated velocity and motion angle, in region centers of the cells being tracked. Finally we obtained the motion traces of active cells. There were fourteen active cells in three image sequences which have been tracked. The errors were less than 2.5 pixels when the prediction values were compared with actual values. It showed that the presented algorithm may basically reach the solution of accurate predition and tracking of the active cells.

  20. Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming.

    Science.gov (United States)

    Magozzi, Sarah; Calosi, Piero

    2015-01-01

    Predicting species vulnerability to global warming requires a comprehensive, mechanistic understanding of sublethal and lethal thermal tolerances. To date, however, most studies investigating species physiological responses to increasing temperature have focused on the underlying physiological traits of either acute or chronic tolerance in isolation. Here we propose an integrative, synthetic approach including the investigation of multiple physiological traits (metabolic performance and thermal tolerance), and their plasticity, to provide more accurate and balanced predictions on species and assemblage vulnerability to both acute and chronic effects of global warming. We applied this approach to more accurately elucidate relative species vulnerability to warming within an assemblage of six caridean prawns occurring in the same geographic, hence macroclimatic, region, but living in different thermal habitats. Prawns were exposed to four incubation temperatures (10, 15, 20 and 25 °C) for 7 days, their metabolic rates and upper thermal limits were measured, and plasticity was calculated according to the concept of Reaction Norms, as well as Q10 for metabolism. Compared to species occupying narrower/more stable thermal niches, species inhabiting broader/more variable thermal environments (including the invasive Palaemon macrodactylus) are likely to be less vulnerable to extreme acute thermal events as a result of their higher upper thermal limits. Nevertheless, they may be at greater risk from chronic exposure to warming due to the greater metabolic costs they incur. Indeed, a trade-off between acute and chronic tolerance was apparent in the assemblage investigated. However, the invasive species P. macrodactylus represents an exception to this pattern, showing elevated thermal limits and plasticity of these limits, as well as a high metabolic control. In general, integrating multiple proxies for species physiological acute and chronic responses to increasing

  1. On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data

    Science.gov (United States)

    Aloufi, Samah; Zhu, Shiai; El Saddik, Abdulmotaleb

    2017-01-01

    The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user’s preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image’s visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner’s popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining promising results in the

  2. On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data

    Directory of Open Access Journals (Sweden)

    Samah Aloufi

    2017-03-01

    Full Text Available The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user’s preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image’s visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner’s popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining

  3. An improved technique for the prediction of optimal image resolution ...

    African Journals Online (AJOL)

    user

    2010-10-04

    Oct 4, 2010 ... Available online at http://www.academicjournals.org/AJEST ... robust technique for predicting optimal image resolution for the mapping of savannah ecosystems was developed. .... whether to purchase multi-spectral imagery acquired by GeoEye-2 ..... Analysis of the spectral behaviour of the pasture class in.

  4. Predicting drive for muscularity behavioural engagement from body image attitudes and emotions.

    Science.gov (United States)

    Tod, David; Edwards, Christian

    2013-01-01

    We examined the potential moderating effects of appearance investment, body image disturbance, and situational body image dysphoria on the drive for muscularity attitude-behaviour relationship. Participants (339 British college men, M(AGE)=20.00 years, SD=2.59) completed drive for muscularity attitude and behaviour, appearance investment, body image disturbance, and situational body image dysphoria measures. Results indicated higher levels of appearance investment, body image disturbance, and situational body image dysphoria were associated with increases in the drive for muscularity attitude's relationship with physique-enhancing behavioural engagement. Results help extend recent research that has moved beyond identifying correlates to examining ways that groups of variables interact to predict drive for muscularity behavioural engagement and may contribute to theory development. Copyright © 2012 Elsevier Ltd. All rights reserved.

  5. Can radiation therapy treatment planning system accurately predict surface doses in postmastectomy radiation therapy patients?

    International Nuclear Information System (INIS)

    Wong, Sharon; Back, Michael; Tan, Poh Wee; Lee, Khai Mun; Baggarley, Shaun; Lu, Jaide Jay

    2012-01-01

    Skin doses have been an important factor in the dose prescription for breast radiotherapy. Recent advances in radiotherapy treatment techniques, such as intensity-modulated radiation therapy (IMRT) and new treatment schemes such as hypofractionated breast therapy have made the precise determination of the surface dose necessary. Detailed information of the dose at various depths of the skin is also critical in designing new treatment strategies. The purpose of this work was to assess the accuracy of surface dose calculation by a clinically used treatment planning system and those measured by thermoluminescence dosimeters (TLDs) in a customized chest wall phantom. This study involved the construction of a chest wall phantom for skin dose assessment. Seven TLDs were distributed throughout each right chest wall phantom to give adequate representation of measured radiation doses. Point doses from the CMS Xio® treatment planning system (TPS) were calculated for each relevant TLD positions and results correlated. There were no significant difference between measured absorbed dose by TLD and calculated doses by the TPS (p > 0.05 (1-tailed). Dose accuracy of up to 2.21% was found. The deviations from the calculated absorbed doses were overall larger (3.4%) when wedges and bolus were used. 3D radiotherapy TPS is a useful and accurate tool to assess the accuracy of surface dose. Our studies have shown that radiation treatment accuracy expressed as a comparison between calculated doses (by TPS) and measured doses (by TLD dosimetry) can be accurately predicted for tangential treatment of the chest wall after mastectomy.

  6. Image processing system performance prediction and product quality evaluation

    Science.gov (United States)

    Stein, E. K.; Hammill, H. B. (Principal Investigator)

    1976-01-01

    The author has identified the following significant results. A new technique for image processing system performance prediction and product quality evaluation was developed. It was entirely objective, quantitative, and general, and should prove useful in system design and quality control. The technique and its application to determination of quality control procedures for the Earth Resources Technology Satellite NASA Data Processing Facility are described.

  7. ABC/2 Method Does not Accurately Predict Cerebral Arteriovenous Malformation Volume.

    Science.gov (United States)

    Roark, Christopher; Vadlamudi, Venu; Chaudhary, Neeraj; Gemmete, Joseph J; Seinfeld, Joshua; Thompson, B Gregory; Pandey, Aditya S

    2018-02-01

    Stereotactic radiosurgery (SRS) is a treatment option for cerebral arteriovenous malformations (AVMs) to prevent intracranial hemorrhage. The decision to proceed with SRS is usually based on calculated nidal volume. Physicians commonly use the ABC/2 formula, based on digital subtraction angiography (DSA), when counseling patients for SRS. To determine whether AVM volume calculated using the ABC/2 method on DSA is accurate when compared to the exact volume calculated from thin-cut axial sections used for SRS planning. Retrospective search of neurovascular database to identify AVMs treated with SRS from 1995 to 2015. Maximum nidal diameters in orthogonal planes on DSA images were recorded to determine volume using ABC/2 formula. Nidal target volume was extracted from operative reports of SRS. Volumes were then compared using descriptive statistics and paired t-tests. Ninety intracranial AVMs were identified. Median volume was 4.96 cm3 [interquartile range (IQR) 1.79-8.85] with SRS planning methods and 6.07 cm3 (IQR 1.3-13.6) with ABC/2 methodology. Moderate correlation was seen between SRS and ABC/2 (r = 0.662; P ABC/2 (t = -3.2; P = .002). When AVMs were dichotomized based on ABC/2 volume, significant differences remained (t = 3.1, P = .003 for ABC/2 volume ABC/2 volume > 7 cm3). The ABC/2 method overestimates cerebral AVM volume when compared to volumetric analysis from SRS planning software. For AVMs > 7 cm3, the overestimation is even greater. SRS planning techniques were also significantly different than values derived from equations for cones and cylinders. Copyright © 2017 by the Congress of Neurological Surgeons

  8. Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT

    Directory of Open Access Journals (Sweden)

    Seung-Hoon Chae

    2014-01-01

    Full Text Available Internet of Things (IoT brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places. Medical image segmentation is needed for analysis, storage, and protection of medical image in telemedicine. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Performing segmentation in various organs, the accurate judgment of the region is needed in medical image. However, the removal of region occurs by the lack of information to determine the region in a small region. In this paper, we researched how to reconstruct segmentation region in a small region in order to improve the segmentation results. We generated predicted segmentation of slices using volume data with linear equation and proposed improvement method for small regions using the predicted segmentation. In order to verify the performance of the proposed method, lung region by chest CT images was segmented. As a result of experiments, volume data segmentation accuracy rose from 0.978 to 0.981 and from 0.281 to 0.187 with a standard deviation improvement confirmed.

  9. Characterization and Predictive Value of Near Infrared 2-Deoxyglucose Optical Imaging in Severe Acute Pancreatitis.

    Directory of Open Access Journals (Sweden)

    Cristiane de Oliveira

    Full Text Available Studying the uptake of 2-deoxy glucose (2-DG analogs such as 2-Deoxy-2-[18F] fluoroglucose (FDG is a common approach to identify and monitor malignancies and more recently chronic inflammation. While pancreatitis is a common cause for false positive results in human studies on pancreatic cancer using FDG, the relevance of these findings to acute pancreatitis (AP is unknown. FDG has a short half-life. Thus, with an aim to accurately characterize the metabolic demand of the pancreas during AP in real-time, we studied the uptake of the non-radioactive, near infrared fluorescence labelled 2-deoxyglucose analog, IRDye® 800CW 2-DG probe (NIR 2-DG; Li-Cor during mild and severe biliary AP.Wistar rats (300 g; 8-12/group were administered NIR 2-DG (10 nM; I.V.. Mild and severe biliary AP were respectively induced by biliopancreatic duct ligation (DL alone or along with infusing glyceryl trilinoleate (GTL; 50 μL/100 g within 10 minutes of giving NIR 2-DG. Controls (CON only received NIR 2-DG. Imaging was done every 5-10 minutes over 3 hrs. Average Radiant Efficiency [p/s/cm²/sr]/[μW/cm²] was measured over the pancreas using the IVIS 200 in-vivo imaging system (PerkinElmer using the Living Image® software and verified in ex vivo pancreata. Blood amylase, lipase and pancreatic edema, necrosis were measured over the course of AP.NIR 2-DG uptake over the first hour was not influenced by AP induction. However, while the signal declined in controls and rats with mild AP, there was significantly higher retention of NIR 2-DG in the pancreas after 1 hour in those with GTL pancreatitis. The increase was > 3 fold over controls in the GTL group and was verified to be in the pancreas ex vivo. In vitro, pancreatic acini exposed to GTL had a similar increase in NIR 2-DG uptake which was followed by progressively worse acinar necrosis. Greater retention of NIR 2-DG in vivo was associated with worse pancreatic necrosis, reduced ATP concentrations and mortality

  10. Integration of multi-modality imaging for accurate 3D reconstruction of human coronary arteries in vivo

    International Nuclear Information System (INIS)

    Giannoglou, George D.; Chatzizisis, Yiannis S.; Sianos, George; Tsikaderis, Dimitrios; Matakos, Antonis; Koutkias, Vassilios; Diamantopoulos, Panagiotis; Maglaveras, Nicos; Parcharidis, George E.; Louridas, George E.

    2006-01-01

    In conventional intravascular ultrasound (IVUS)-based three-dimensional (3D) reconstruction of human coronary arteries, IVUS images are arranged linearly generating a straight vessel volume. However, with this approach real vessel curvature is neglected. To overcome this limitation an imaging method was developed based on integration of IVUS and biplane coronary angiography (BCA). In 17 coronary arteries from nine patients, IVUS and BCA were performed. From each angiographic projection, a single end-diastolic frame was selected and in each frame the IVUS catheter was interactively detected for the extraction of 3D catheter path. Ultrasound data was obtained with a sheath-based catheter and recorded on S-VHS videotape. S-VHS data was digitized and lumen and media-adventitia contours were semi-automatically detected in end-diastolic IVUS images. Each pair of contours was aligned perpendicularly to the catheter path and rotated in space by implementing an algorithm based on Frenet-Serret rules. Lumen and media-adventitia contours were interpolated through generation of intermediate contours creating a real 3D lumen and vessel volume, respectively. The absolute orientation of the reconstructed lumen was determined by back-projecting it onto both angiographic planes and comparing the projected lumen with the actual angiographic lumen. In conclusion, our method is capable of performing rapid and accurate 3D reconstruction of human coronary arteries in vivo. This technique can be utilized for reliable plaque morphometric, geometrical and hemodynamic analyses

  11. Feasibility study for application of the compressed-sensing framework to interior computed tomography (ICT) for low-dose, high-accurate dental x-ray imaging

    Science.gov (United States)

    Je, U. K.; Cho, H. M.; Cho, H. S.; Park, Y. O.; Park, C. K.; Lim, H. W.; Kim, K. S.; Kim, G. A.; Park, S. Y.; Woo, T. H.; Choi, S. I.

    2016-02-01

    In this paper, we propose a new/next-generation type of CT examinations, the so-called Interior Computed Tomography (ICT), which may presumably lead to dose reduction to the patient outside the target region-of-interest (ROI), in dental x-ray imaging. Here an x-ray beam from each projection position covers only a relatively small ROI containing a target of diagnosis from the examined structure, leading to imaging benefits such as decreasing scatters and system cost as well as reducing imaging dose. We considered the compressed-sensing (CS) framework, rather than common filtered-backprojection (FBP)-based algorithms, for more accurate ICT reconstruction. We implemented a CS-based ICT algorithm and performed a systematic simulation to investigate the imaging characteristics. Simulation conditions of two ROI ratios of 0.28 and 0.14 between the target and the whole phantom sizes and four projection numbers of 360, 180, 90, and 45 were tested. We successfully reconstructed ICT images of substantially high image quality by using the CS framework even with few-view projection data, still preserving sharp edges in the images.

  12. Differentiated thyroid carcinomas: prediction of tumor invasion with MR imaging

    International Nuclear Information System (INIS)

    Takashima, S.; Takayama, F.; Wang, Q.; Kawakami, S.; Saito, A.; Sone, S.; Kobayashi, S.

    2000-01-01

    Purpose: To assess diagnostic accuracy for tumor invasion of surrounding organs by measurement of tumor circumferences on MR images in patients with differentiated thyroid carcinomas. Material and Methods: Surgical and MR imaging findings in 50 patients with differentiated thyroid carcinoma (43 primary, 7 recurrent lesions) were retrospectively reviewed. The degrees of circumference of tumor encroachment to the organs were measured, and the measurements and morphologic diagnosis of tumor invasion made by a head and neck radiologist were compared with surgical and pathologic findings using receiver operating characteristic curves. Results: Diagnosis of tumor invasion by the radiologist was superior to the measurements of the carotid artery and cartilage, while the reverse was true for the trachea and esophagus. However, no statistical differences were noted between them for each structure. Optimal thresholds for tumor invasion were 90 deg or more for the cartilage (94% accuracy) and esophagus (86% accuracy), 135 deg or more for the trachea (86% accuracy), and 225 deg or more for the carotid artery (90% accuracy). Conclusion: Tumor invasion was more accurately diagnosed by measurement of tumor circumferences of each organ on MR images

  13. Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images.

    Science.gov (United States)

    Banzato, T; Cherubini, G B; Atzori, M; Zotti, A

    2018-05-01

    An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and post-contrast T1-weighted (T1W), and T2-weighted (T2W) MR images. The images were randomly assigned to a training set, a validation set and a test set, comprising 60%, 10% and 30% of images, respectively. The combination of DNN and MR sequence displaying the highest discriminating accuracy was used to develop an image classifier to predict the grading of new cases. The algorithm based on transfer learning using the established DNN did not provide satisfactory results, whereas the newly designed DNN had high classification accuracy. On the basis of classification accuracy, an image classifier built on the newly designed DNN using post-contrast T1W images was developed. This image classifier correctly predicted the grading of 8 out of 10 images not included in the data set. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

    Science.gov (United States)

    Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Zheng, Bin; Cheng, Samuel

    2016-03-01

    Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) has been used increasingly in breast cancer diagnosis and assessment of cancer treatment efficacy. In this study, we applied a computer-aided detection (CAD) scheme to automatically segment breast regions depicting on MR images and used the kinetic image features computed from the global breast MR images acquired before neoadjuvant chemotherapy to build a new quantitative model to predict response of the breast cancer patients to the chemotherapy. To assess performance and robustness of this new prediction model, an image dataset involving breast MR images acquired from 151 cancer patients before undergoing neoadjuvant chemotherapy was retrospectively assembled and used. Among them, 63 patients had "complete response" (CR) to chemotherapy in which the enhanced contrast levels inside the tumor volume (pre-treatment) was reduced to the level as the normal enhanced background parenchymal tissues (post-treatment), while 88 patients had "partially response" (PR) in which the high contrast enhancement remain in the tumor regions after treatment. We performed the studies to analyze the correlation among the 22 global kinetic image features and then select a set of 4 optimal features. Applying an artificial neural network trained with the fusion of these 4 kinetic image features, the prediction model yielded an area under ROC curve (AUC) of 0.83+/-0.04. This study demonstrated that by avoiding tumor segmentation, which is often difficult and unreliable, fusion of kinetic image features computed from global breast MR images without tumor segmentation can also generate a useful clinical marker in predicting efficacy of chemotherapy.

  15. Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome

    International Nuclear Information System (INIS)

    Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; Moore, Kathleen; Liu, Hong; Zheng, Bin

    2016-01-01

    To investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome. A computerized image processing scheme was developed to segment visceral and subcutaneous fat areas, and compute adiposity-related image features. Then, logistic regression models were applied to analyze association between the scheme-generated assessment scores and progression-free survival (PFS) of patients using a leave-one-case-out cross-validation method and a dataset involving 32 patients. The correlation coefficients between automated and radiologist’s manual segmentation of visceral and subcutaneous fat areas were 0.76 and 0.89, respectively. The scheme-generated prediction scores using adiposity-related radiographic image features significantly associated with patients’ PFS (p < 0.01). Using a computerized scheme enables to more efficiently and robustly segment visceral and subcutaneous fat areas. The computed adiposity-related image features also have potential to improve accuracy in predicting chemotherapy outcome

  16. Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

    Science.gov (United States)

    Voorhies, Coerte V.; Conrad, Joy

    1996-01-01

    The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

  17. Accurate Rapid Lifetime Determination on Time-Gated FLIM Microscopy with Optical Sectioning.

    Science.gov (United States)

    Silva, Susana F; Domingues, José Paulo; Morgado, António Miguel

    2018-01-01

    Time-gated fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to assess the biochemistry of cells and tissues. When applied to living thick samples, it is hampered by the lack of optical sectioning and the need of acquiring many images for an accurate measurement of fluorescence lifetimes. Here, we report on the use of processing techniques to overcome these limitations, minimizing the acquisition time, while providing optical sectioning. We evaluated the application of the HiLo and the rapid lifetime determination (RLD) techniques for accurate measurement of fluorescence lifetimes with optical sectioning. HiLo provides optical sectioning by combining the high-frequency content from a standard image, obtained with uniform illumination, with the low-frequency content of a second image, acquired using structured illumination. Our results show that HiLo produces optical sectioning on thick samples without degrading the accuracy of the measured lifetimes. We also show that instrument response function (IRF) deconvolution can be applied with the RLD technique on HiLo images, improving greatly the accuracy of the measured lifetimes. These results open the possibility of using the RLD technique with pulsed diode laser sources to determine accurately fluorescence lifetimes in the subnanosecond range on thick multilayer samples, providing that offline processing is allowed.

  18. Convolutional neural networks for prostate cancer recurrence prediction

    Science.gov (United States)

    Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.

    2017-03-01

    Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs) - one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.

  19. Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

    International Nuclear Information System (INIS)

    Garzon, Benjamin; Emblem, Kyrre E.; Mouridsen, Kim; Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K.; Bjoernerud, Atle; Haaberg, Asta K.; Kvinnsland, Yngve

    2011-01-01

    Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

  20. Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

    Energy Technology Data Exchange (ETDEWEB)

    Garzon, Benjamin (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway)), email: benjamin.garzon@ntnu.no; Emblem, Kyrre E. (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway); Dept. of Radiology, MGH-HST AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston (United States)); Mouridsen, Kim (Center of Functionally Integrative Neuroscience, Aarhus Univ., Aarhus (Denmark)); Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K. (Dept. of Radiology and Nuclear Medicine, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Bjoernerud, Atle (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Haaberg, Asta K. (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway); Dept. of Medical Imaging, St Olav' s Hospital, Trondheim (Norway)); Kvinnsland, Yngve (NordicImagingLab, Bergen (Norway))

    2011-11-15

    Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

  1. A simple, fast, and accurate thermodynamic-based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part III: Retention time prediction on target column.

    Science.gov (United States)

    Hou, Siyuan; Stevenson, Keisean A J M; Harynuk, James J

    2018-03-27

    This is the third part of a three-part series of papers. In Part I, we presented a method for determining the actual effective geometry of a reference column as well as the thermodynamic-based parameters of a set of probe compounds in an in-house mixture. Part II introduced an approach for estimating the actual effective geometry of a target column by collecting retention data of the same mixture of probe compounds on the target column and using their thermodynamic parameters, acquired on the reference column, as a bridge between both systems. Part III, presented here, demonstrates the retention time transfer and prediction from the reference column to the target column using experimental data for a separate mixture of compounds. To predict the retention time of a new compound, we first estimate its thermodynamic-based parameters on the reference column (using geometric parameters determined previously). The compound's retention time on a second column (of previously determined geometry) is then predicted. The models and the associated optimization algorithms were tested using simulated and experimental data. The accuracy of predicted retention times shows that the proposed approach is simple, fast, and accurate for retention time transfer and prediction between gas chromatography columns. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Unilateral Prostate Cancer Cannot be Accurately Predicted in Low-Risk Patients

    International Nuclear Information System (INIS)

    Isbarn, Hendrik; Karakiewicz, Pierre I.; Vogel, Susanne

    2010-01-01

    Purpose: Hemiablative therapy (HAT) is increasing in popularity for treatment of patients with low-risk prostate cancer (PCa). The validity of this therapeutic modality, which exclusively treats PCa within a single prostate lobe, rests on accurate staging. We tested the accuracy of unilaterally unremarkable biopsy findings in cases of low-risk PCa patients who are potential candidates for HAT. Methods and Materials: The study population consisted of 243 men with clinical stage ≤T2a, a prostate-specific antigen (PSA) concentration of <10 ng/ml, a biopsy-proven Gleason sum of ≤6, and a maximum of 2 ipsilateral positive biopsy results out of 10 or more cores. All men underwent a radical prostatectomy, and pathology stage was used as the gold standard. Univariable and multivariable logistic regression models were tested for significant predictors of unilateral, organ-confined PCa. These predictors consisted of PSA, %fPSA (defined as the quotient of free [uncomplexed] PSA divided by the total PSA), clinical stage (T2a vs. T1c), gland volume, and number of positive biopsy cores (2 vs. 1). Results: Despite unilateral stage at biopsy, bilateral or even non-organ-confined PCa was reported in 64% of all patients. In multivariable analyses, no variable could clearly and independently predict the presence of unilateral PCa. This was reflected in an overall accuracy of 58% (95% confidence interval, 50.6-65.8%). Conclusions: Two-thirds of patients with unilateral low-risk PCa, confirmed by clinical stage and biopsy findings, have bilateral or non-organ-confined PCa at radical prostatectomy. This alarming finding questions the safety and validity of HAT.

  3. Accurate and fiducial-marker-free correction for three-dimensional chromatic shift in biological fluorescence microscopy.

    Science.gov (United States)

    Matsuda, Atsushi; Schermelleh, Lothar; Hirano, Yasuhiro; Haraguchi, Tokuko; Hiraoka, Yasushi

    2018-05-15

    Correction of chromatic shift is necessary for precise registration of multicolor fluorescence images of biological specimens. New emerging technologies in fluorescence microscopy with increasing spatial resolution and penetration depth have prompted the need for more accurate methods to correct chromatic aberration. However, the amount of chromatic shift of the region of interest in biological samples often deviates from the theoretical prediction because of unknown dispersion in the biological samples. To measure and correct chromatic shift in biological samples, we developed a quadrisection phase correlation approach to computationally calculate translation, rotation, and magnification from reference images. Furthermore, to account for local chromatic shifts, images are split into smaller elements, for which the phase correlation between channels is measured individually and corrected accordingly. We implemented this method in an easy-to-use open-source software package, called Chromagnon, that is able to correct shifts with a 3D accuracy of approximately 15 nm. Applying this software, we quantified the level of uncertainty in chromatic shift correction, depending on the imaging modality used, and for different existing calibration methods, along with the proposed one. Finally, we provide guidelines to choose the optimal chromatic shift registration method for any given situation.

  4. TU-G-303-00: Radiomics: Advances in the Use of Quantitative Imaging Used for Predictive Modeling

    International Nuclear Information System (INIS)

    2015-01-01

    ‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding

  5. TU-G-303-00: Radiomics: Advances in the Use of Quantitative Imaging Used for Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2015-06-15

    ‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding

  6. Saccades to future ball location reveal memory-based prediction in a virtual-reality interception task.

    Science.gov (United States)

    Diaz, Gabriel; Cooper, Joseph; Rothkopf, Constantin; Hayhoe, Mary

    2013-01-16

    Despite general agreement that prediction is a central aspect of perception, there is relatively little evidence concerning the basis on which visual predictions are made. Although both saccadic and pursuit eye-movements reveal knowledge of the future position of a moving visual target, in many of these studies targets move along simple trajectories through a fronto-parallel plane. Here, using a naturalistic and racquet-based interception task in a virtual environment, we demonstrate that subjects make accurate predictions of visual target motion, even when targets follow trajectories determined by the complex dynamics of physical interactions and the head and body are unrestrained. Furthermore, we found that, following a change in ball elasticity, subjects were able to accurately adjust their prebounce predictions of the ball's post-bounce trajectory. This suggests that prediction is guided by experience-based models of how information in the visual image will change over time.

  7. Detecting Weather Radar Clutter by Information Fusion With Satellite Images and Numerical Weather Prediction Model Output

    DEFF Research Database (Denmark)

    Bøvith, Thomas; Nielsen, Allan Aasbjerg; Hansen, Lars Kai

    2006-01-01

    A method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect...... information on precipitation in the atmosphere from Meteosat-8 multispectral images and near-surface temperature estimates from the DMI-HIRLAM-S05 numerical weather prediction model. Alternatively, an operational nowcasting product called 'Precipitating Clouds' based on Meteosat-8 input is used. A scale...

  8. Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis.

    Science.gov (United States)

    Masso, Majid; Vaisman, Iosif I

    2008-09-15

    Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (DeltaDeltaG) and denaturant (DeltaDeltaG(H2O)) denaturations, as well as mutant thermal stability (DeltaT(m)), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal. We detail a computational mutagenesis technique based on a four-body, knowledge-based, statistical contact potential. For any mutation due to a single amino acid replacement in a protein, the method provides an empirical normalized measure of the ensuing environmental perturbation occurring at every residue position. A feature vector is generated for the mutant by considering perturbations at the mutated position and it's ordered six nearest neighbors in the 3-dimensional (3D) protein structure. These predictors of stability change are evaluated by applying machine learning tools to large training sets of mutants derived from diverse proteins that have been experimentally studied and described. Predictive models based on our combined approach are either comparable to, or in many cases significantly outperform, previously published results. A web server with supporting documentation is available at http://proteins.gmu.edu/automute.

  9. Accurate measurement of peripheral blood mononuclear cell concentration using image cytometry to eliminate RBC-induced counting error.

    Science.gov (United States)

    Chan, Leo Li-Ying; Laverty, Daniel J; Smith, Tim; Nejad, Parham; Hei, Hillary; Gandhi, Roopali; Kuksin, Dmitry; Qiu, Jean

    2013-02-28

    Peripheral blood mononuclear cells (PBMCs) have been widely researched in the fields of immunology, infectious disease, oncology, transplantation, hematological malignancy, and vaccine development. Specifically, in immunology research, PBMCs have been utilized to monitor concentration, viability, proliferation, and cytokine production from immune cells, which are critical for both clinical trials and biomedical research. The viability and concentration of isolated PBMCs are traditionally measured by manual counting with trypan blue (TB) using a hemacytometer. One of the common issues of PBMC isolation is red blood cell (RBC) contamination. The RBC contamination can be dependent on the donor sample and/or technical skill level of the operator. RBC contamination in a PBMC sample can introduce error to the measured concentration, which can pass down to future experimental assays performed on these cells. To resolve this issue, RBC lysing protocol can be used to eliminate potential error caused by RBC contamination. In the recent years, a rapid fluorescence-based image cytometry system has been utilized for bright-field and fluorescence imaging analysis of cellular characteristics (Nexcelom Bioscience LLC, Lawrence, MA). The Cellometer image cytometry system has demonstrated the capability of automated concentration and viability detection in disposable counting chambers of unpurified mouse splenocytes and PBMCs stained with acridine orange (AO) and propidium iodide (PI) under fluorescence detection. In this work, we demonstrate the ability of Cellometer image cytometry system to accurately measure PBMC concentration, despite RBC contamination, by comparison of five different total PBMC counting methods: (1) manual counting of trypan blue-stained PBMCs in hemacytometer, (2) manual counting of PBMCs in bright-field images, (3) manual counting of acetic acid lysing of RBCs with TB-stained PBMCs, (4) automated counting of acetic acid lysing of RBCs with PI-stained PBMCs

  10. On Feature Relevance in Image-Based Prediction Models: An Empirical Study

    DEFF Research Database (Denmark)

    Konukoglu, E.; Ganz, Melanie; Van Leemput, Koen

    2013-01-01

    Determining disease-related variations of the anatomy and function is an important step in better understanding diseases and developing early diagnostic systems. In particular, image-based multivariate prediction models and the “relevant features” they produce are attracting attention from the co...

  11. Combining Mean and Standard Deviation of Hounsfield Unit Measurements from Preoperative CT Allows More Accurate Prediction of Urinary Stone Composition Than Mean Hounsfield Units Alone.

    Science.gov (United States)

    Tailly, Thomas; Larish, Yaniv; Nadeau, Brandon; Violette, Philippe; Glickman, Leonard; Olvera-Posada, Daniel; Alenezi, Husain; Amann, Justin; Denstedt, John; Razvi, Hassan

    2016-04-01

    The mineral composition of a urinary stone may influence its surgical and medical treatment. Previous attempts at identifying stone composition based on mean Hounsfield Units (HUm) have had varied success. We aimed to evaluate the additional use of standard deviation of HU (HUsd) to more accurately predict stone composition. We identified patients from two centers who had undergone urinary stone treatment between 2006 and 2013 and had mineral stone analysis and a computed tomography (CT) available. HUm and HUsd of the stones were compared with ANOVA. Receiver operative characteristic analysis with area under the curve (AUC), Youden index, and likelihood ratio calculations were performed. Data were available for 466 patients. The major components were calcium oxalate monohydrate (COM), uric acid, hydroxyapatite, struvite, brushite, cystine, and CO dihydrate (COD) in 41.4%, 19.3%, 12.4%, 7.5%, 5.8%, 5.4%, and 4.7% of patients, respectively. The HUm of UA and Br was significantly lower and higher than the HUm of any other stone type, respectively. HUm and HUsd were most accurate in predicting uric acid with an AUC of 0.969 and 0.851, respectively. The combined use of HUm and HUsd resulted in increased positive predictive value and higher likelihood ratios for identifying a stone's mineral composition for all stone types but COM. To the best of our knowledge, this is the first report of CT data aiding in the prediction of brushite stone composition. Both HUm and HUsd can help predict stone composition and their combined use results in higher likelihood ratios influencing probability.

  12. Dynamics of Flexible MLI-type Debris for Accurate Orbit Prediction

    Science.gov (United States)

    2014-09-01

    debris for accurate propagation under perturbations”, in Proceedings of 65th International Astronautical Congress (IAC 2014), Toronto, Canada , 2014...Surveillance Network ( SSN ) was able to detect more than 900 pieces of debris that were at risk to damage operational spacecraft. In February 10, 2009...created two large debris clouds and the SSN reported that 382 pieces of debris from Iridium 33 and 893 pieces from Cosmos 2251 were created, and

  13. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

    Science.gov (United States)

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-02-24

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

  14. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

    Directory of Open Access Journals (Sweden)

    Shiyao Wang

    2016-02-01

    Full Text Available A high-performance differential global positioning system (GPS  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU/dead reckoning (DR data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

  15. Predictive modelling using neuroimaging data in the presence of confounds.

    Science.gov (United States)

    Rao, Anil; Monteiro, Joao M; Mourao-Miranda, Janaina

    2017-04-15

    When training predictive models from neuroimaging data, we typically have available non-imaging variables such as age and gender that affect the imaging data but which we may be uninterested in from a clinical perspective. Such variables are commonly referred to as 'confounds'. In this work, we firstly give a working definition for confound in the context of training predictive models from samples of neuroimaging data. We define a confound as a variable which affects the imaging data and has an association with the target variable in the sample that differs from that in the population-of-interest, i.e., the population over which we intend to apply the estimated predictive model. The focus of this paper is the scenario in which the confound and target variable are independent in the population-of-interest, but the training sample is biased due to a sample association between the target and confound. We then discuss standard approaches for dealing with confounds in predictive modelling such as image adjustment and including the confound as a predictor, before deriving and motivating an Instance Weighting scheme that attempts to account for confounds by focusing model training so that it is optimal for the population-of-interest. We evaluate the standard approaches and Instance Weighting in two regression problems with neuroimaging data in which we train models in the presence of confounding, and predict samples that are representative of the population-of-interest. For comparison, these models are also evaluated when there is no confounding present. In the first experiment we predict the MMSE score using structural MRI from the ADNI database with gender as the confound, while in the second we predict age using structural MRI from the IXI database with acquisition site as the confound. Considered over both datasets we find that none of the methods for dealing with confounding gives more accurate predictions than a baseline model which ignores confounding, although

  16. Developing a NIR multispectral imaging for prediction and visualization of peanut protein content using variable selection algorithms

    Science.gov (United States)

    Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei

    2018-01-01

    The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.

  17. Diagnostic accuracy of imaging modalities for internal derangements of temporomandibular joint

    International Nuclear Information System (INIS)

    Kobayashi, Kaoru; Igarashi, Chinami; Yuasa, Masao; Imanaka, Masahiro; Kondoh, Toshirou

    1998-01-01

    The purpose of this study was to evaluate and review the diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value of imaging diagnosis for temporomandibular disorders. The role of diagnostic imaging is to detect and document specific anatomic abnormalities associated with the signs and symptoms in the temporomandibular joint. Magnetic resonance imaging (MR imaging) can accurately depict disc displacement and disc deformity. MR imaging is our first choice among the various imaging modalities for the patients with clinical signs and symptoms. However, it has been shown that intra-capsular adhesions and perforations of the disc and retrodiscal tissue are sometimes not detected by MR imaging. To improve the diagnostic technique for adhesions and perforations, double-contrast arthrotomography with fluoroscopy should be employed. The irregular surface of the eminences and the glenoid fossae shown by MR imaging and tomography are correlated with subchondral bone exposure by arthroscopy. Erosion of the condyles detected by MR imaging, tomography and rotational panoramic radiography is correlated with subchondral bone exposure detected by arthroscopy. (author). 69 refs

  18. How accurate is anatomic limb alignment in predicting mechanical limb alignment after total knee arthroplasty?

    Science.gov (United States)

    Lee, Seung Ah; Choi, Sang-Hee; Chang, Moon Jong

    2015-10-27

    Anatomic limb alignment often differs from mechanical limb alignment after total knee arthroplasty (TKA). We sought to assess the accuracy, specificity, and sensitivity for each of three commonly used ranges for anatomic limb alignment (3-9°, 5-10° and 2-10°) in predicting an acceptable range (neutral ± 3°) for mechanical limb alignment after TKA. We also assessed whether the accuracy of anatomic limb alignment was affected by anatomic variation. This retrospective study included 314 primary TKAs. The alignment of the limb was measured with both anatomic and mechanical methods of measurement. We also measured anatomic variation, including the femoral bowing angle, tibial bowing angle, and neck-shaft angle of the femur. All angles were measured on the same full-length standing anteroposterior radiographs. The accuracy, specificity, and sensitivity for each range of anatomic limb alignment were calculated and compared using mechanical limb alignment as the reference standard. The associations between the accuracy of anatomic limb alignment and anatomic variation were also determined. The range of 2-10° for anatomic limb alignment showed the highest accuracy, but it was only 73 % (3-9°, 65 %; 5-10°, 67 %). The specificity of the 2-10° range was 81 %, which was higher than that of the other ranges (3-9°, 69 %; 5-10°, 67 %). However, the sensitivity of the 2-10° range to predict varus malalignment was only 16 % (3-9°, 35 %; 5-10°, 68 %). In addition, the sensitivity of the 2-10° range to predict valgus malalignment was only 43 % (3-9°, 71 %; 5-10°, 43 %). The accuracy of anatomical limb alignment was lower for knees with greater femoral (odds ratio = 1.2) and tibial (odds ratio = 1.2) bowing. Anatomic limb alignment did not accurately predict mechanical limb alignment after TKA, and its accuracy was affected by anatomic variation. Thus, alignment after TKA should be assessed by measuring mechanical alignment rather than anatomic

  19. A three-dimensional image processing program for accurate, rapid, and semi-automated segmentation of neuronal somata with dense neurite outgrowth

    Science.gov (United States)

    Ross, James D.; Cullen, D. Kacy; Harris, James P.; LaPlaca, Michelle C.; DeWeerth, Stephen P.

    2015-01-01

    Three-dimensional (3-D) image analysis techniques provide a powerful means to rapidly and accurately assess complex morphological and functional interactions between neural cells. Current software-based identification methods of neural cells generally fall into two applications: (1) segmentation of cell nuclei in high-density constructs or (2) tracing of cell neurites in single cell investigations. We have developed novel methodologies to permit the systematic identification of populations of neuronal somata possessing rich morphological detail and dense neurite arborization throughout thick tissue or 3-D in vitro constructs. The image analysis incorporates several novel automated features for the discrimination of neurites and somata by initially classifying features in 2-D and merging these classifications into 3-D objects; the 3-D reconstructions automatically identify and adjust for over and under segmentation errors. Additionally, the platform provides for software-assisted error corrections to further minimize error. These features attain very accurate cell boundary identifications to handle a wide range of morphological complexities. We validated these tools using confocal z-stacks from thick 3-D neural constructs where neuronal somata had varying degrees of neurite arborization and complexity, achieving an accuracy of ≥95%. We demonstrated the robustness of these algorithms in a more complex arena through the automated segmentation of neural cells in ex vivo brain slices. These novel methods surpass previous techniques by improving the robustness and accuracy by: (1) the ability to process neurites and somata, (2) bidirectional segmentation correction, and (3) validation via software-assisted user input. This 3-D image analysis platform provides valuable tools for the unbiased analysis of neural tissue or tissue surrogates within a 3-D context, appropriate for the study of multi-dimensional cell-cell and cell-extracellular matrix interactions. PMID

  20. Learning-based deformable image registration for infant MR images in the first year of life.

    Science.gov (United States)

    Hu, Shunbo; Wei, Lifang; Gao, Yaozong; Guo, Yanrong; Wu, Guorong; Shen, Dinggang

    2017-01-01

    Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (a) large anatomical changes due to fast brain development and (b) dynamic appearance changes due to white-matter myelination. To address these two difficulties, we propose a learning-based registration method to not only align the anatomical structures but also alleviate the appearance differences between two arbitrary infant MR images (with large age gap) by leveraging the regression forest to predict both the initial displacement vector and appearance changes. Specifically, in the training stage, two regression models are trained separately, with (a) one model learning the relationship between local image appearance (of one development phase) and its displacement toward the template (of another development phase) and (b) another model learning the local appearance changes between the two brain development phases. Then, in the testing stage, to register a new infant image to the template, we first predict both its voxel-wise displacement and appearance changes by the two learned regression models. Since such initializations can alleviate significant appearance and shape differences between new infant image and the template, it is easy to just use a conventional registration method to refine the remaining registration. We apply our proposed registration method to align 24 infant subjects at five different time points (i.e., 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old), and achieve more accurate and robust registration

  1. A standardized imaging protocol for the endoscopic prediction of dysplasia within sessile serrated polyps (with video).

    Science.gov (United States)

    Tate, David J; Jayanna, Mahesh; Awadie, Halim; Desomer, Lobke; Lee, Ralph; Heitman, Steven J; Sidhu, Mayenaaz; Goodrick, Kathleen; Burgess, Nicholas G; Mahajan, Hema; McLeod, Duncan; Bourke, Michael J

    2018-01-01

    Dysplasia within sessile serrated polyps (SSPs) is difficult to detect and may be mistaken for an adenoma, risking incomplete resection of the background serrated tissue, and is strongly implicated in interval cancer after colonoscopy. The use of endoscopic imaging to detect dysplasia within SSPs has not been systematically studied. Consecutively detected SSPs ≥8 mm in size were evaluated by using a standardized imaging protocol at a tertiary-care endoscopy center over 3 years. Lesions suspected as SSPs were analyzed with high-definition white light then narrow-band imaging. A demarcated area with a neoplastic pit pattern (Kudo type III/IV, NICE type II) was sought among the serrated tissue. If this was detected, the lesion was labeled dysplastic (sessile serrated polyp with dysplasia); if not, it was labeled non-dysplastic (sessile serrated polyp without dysplasia). Histopathology was reviewed by 2 blinded specialist GI pathologists. A total of 141 SSPs were assessed in 83 patients. Median lesion size was 15.0 mm (interquartile range 10-20), and 54.6% were in the right side of the colon. Endoscopic evidence of dysplasia was detected in 36 of 141 (25.5%) SSPs; of these, 5 of 36 (13.9%) lacked dysplasia at histopathology. Two of 105 (1.9%) endoscopically designated non-dysplastic SSPs had dysplasia at histopathology. Endoscopic imaging, therefore, had an accuracy of 95.0% (95% confidence interval [CI], 90.1%-97.6%) and a negative predictive value of 98.1% (95% CI, 92.6%-99.7%) for detection of dysplasia within SSPs. Dysplasia within SSPs can be detected accurately by using a simple, broadly applicable endoscopic imaging protocol that allows complete resection. Independent validation of this protocol and its dissemination to the wider endoscopic community may have a significant impact on rates of interval cancer. (Clinical trial registration number: NCT03100552.). Copyright © 2018 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All

  2. Capsular contracture and possible implant rupture: is magnetic resonance imaging useful?

    Science.gov (United States)

    Paetau, Alyssa A; McLaughlin, Sarah A; McNeil, Rebecca B; Sternberg, Erez; TerKonda, Sarvam P; Waldorf, James C; Perdikis, Galen

    2010-03-01

    Currently, magnetic resonance imaging is considered the accepted standard to evaluate breast implant integrity. To evaluate its utility in diagnosing ruptured silicone implants in the setting of capsular contracture and to correlate the preoperative assessment of implant integrity with or without magnetic resonance imaging with operative findings, 319 capsulectomies (171 patients with capsular contractures) were retrospectively reviewed. Preoperative magnetic resonance imaging was done on 160 implants, whereas the remaining 159 were evaluated using only physical examination and/or mammography. Postoperative results were analyzed to determine the sensitivity, specificity, and accuracy of preoperative magnetic resonance imaging in comparison with clinical and/or mammography evaluation alone. Although occasionally valuable, overall, preoperative magnetic resonance imaging was no more accurate than clinical evaluation with or without mammography in predicting implant status: magnetic resonance imaging 124 of 160 (78 percent) and clinical 121 of 159 (76 percent; p = 0.77). In the setting of capsular contracture, physical examination with or without mammogram is as accurate as magnetic resonance imaging in determining implant integrity. Although magnetic resonance imaging is a sensitive diagnostic tool, in symptomatic patients with capsular contracture, it cannot be viewed as infallible.

  3. Accurate, low-cost 3D-models of gullies

    Science.gov (United States)

    Onnen, Nils; Gronz, Oliver; Ries, Johannes B.; Brings, Christine

    2015-04-01

    Soil erosion is a widespread problem in arid and semi-arid areas. The most severe form is the gully erosion. They often cut into agricultural farmland and can make a certain area completely unproductive. To understand the development and processes inside and around gullies, we calculated detailed 3D-models of gullies in the Souss Valley in South Morocco. Near Taroudant, we had four study areas with five gullies different in size, volume and activity. By using a Canon HF G30 Camcorder, we made varying series of Full HD videos with 25fps. Afterwards, we used the method Structure from Motion (SfM) to create the models. To generate accurate models maintaining feasible runtimes, it is necessary to select around 1500-1700 images from the video, while the overlap of neighboring images should be at least 80%. In addition, it is very important to avoid selecting photos that are blurry or out of focus. Nearby pixels of a blurry image tend to have similar color values. That is why we used a MATLAB script to compare the derivatives of the images. The higher the sum of the derivative, the sharper an image of similar objects. MATLAB subdivides the video into image intervals. From each interval, the image with the highest sum is selected. E.g.: 20min. video at 25fps equals 30.000 single images. The program now inspects the first 20 images, saves the sharpest and moves on to the next 20 images etc. Using this algorithm, we selected 1500 images for our modeling. With VisualSFM, we calculated features and the matches between all images and produced a point cloud. Then, MeshLab has been used to build a surface out of it using the Poisson surface reconstruction approach. Afterwards we are able to calculate the size and the volume of the gullies. It is also possible to determine soil erosion rates, if we compare the data with old recordings. The final step would be the combination of the terrestrial data with the data from our aerial photography. So far, the method works well and we

  4. Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

    Science.gov (United States)

    Okada, Toshiyuki; Linguraru, Marius George; Hori, Masatoshi; Summers, Ronald M; Tomiyama, Noriyuki; Sato, Yoshinobu

    2015-12-01

    This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more accurate segmentation as well as easy adaptation to various imaging conditions in CT images, as observed in clinical practice. We propose a general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information. The features of the framework are as follows: (1) A method for modeling conditional shape and location (shape-location) priors, which we call prediction-based priors, is developed to derive accurate priors specific to each subject, which enables the estimation of intensity priors without the need for supervised intensity information. (2) Organ correlation graph is introduced, which defines how the conditional priors are constructed and segmentation processes of multiple organs are executed. In our framework, predictor organs, whose segmentation is sufficiently accurate by using conventional single-organ segmentation methods, are pre-segmented, and the remaining organs are hierarchically segmented using conditional shape-location priors. The proposed framework was evaluated through the segmentation of eight abdominal organs (liver, spleen, left and right kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from 134 CT data from 86 patients obtained under six imaging conditions at two hospitals. The experimental results show the effectiveness of the proposed prediction-based priors and the applicability to various imaging conditions without the need for supervised intensity information. Average Dice coefficients for the liver, spleen, and kidneys were more than 92%, and were around 73% and 67% for the pancreas and gallbladder, respectively. Copyright © 2015

  5. SU-F-T-42: MRI and TRUS Image Fusion as a Mode of Generating More Accurate Prostate Contours

    Energy Technology Data Exchange (ETDEWEB)

    Petronek, M; Purysko, A; Balik, S; Ciezki, J; Klein, E; Wilkinson, D [Cleveland Clinic Foundation, Cleveland, OH (United States)

    2016-06-15

    Purpose: Transrectal Ultrasound (TRUS) imaging is utilized intra-operatively for LDR permanent prostate seed implant treatment planning. Prostate contouring with TRUS can be challenging at the apex and base. This study attempts to improve accuracy of prostate contouring with MRI-TRUS fusion to prevent over- or under-estimation of the prostate volume. Methods: 14 patients with previous MRI guided prostate biopsy and undergone an LDR permanent prostate seed implant have been selected. The prostate was contoured on the MRI images (1 mm slice thickness) by a radiologist. The prostate was also contoured on TRUS images (5 mm slice thickness) during LDR procedure by a urologist. MRI and TRUS images were rigidly fused manually and the prostate contours from MRI and TRUS were compared using Dice similarity coefficient, percentage volume difference and length, height and width differences. Results: The prostate volume was overestimated by 8 ± 18% (range: 34% to −25%) in TRUS images compared to MRI. The mean Dice was 0.77 ± 0.09 (range: 0.53 to 0.88). The mean difference (TRUS-MRI) in the prostate width was 0 ± 4 mm (range: −11 to 5 mm), height was −3 ± 6 mm (range: −13 to 6 mm) and length was 6 ± 6 (range: −10 to 16 mm). Prostate was overestimated with TRUS imaging at the base for 6 cases (mean: 8 ± 4 mm and range: 5 to 14 mm), at the apex for 6 cases (mean: 11 ± 3 mm and range: 5 to 15 mm) and 1 case was underestimated at both base and apex by 4 mm. Conclusion: Use of intra-operative TRUS and MRI image fusion can help to improve the accuracy of prostate contouring by accurately accounting for prostate over- or under-estimations, especially at the base and apex. The mean amount of discrepancy is within a range that is significant for LDR sources.

  6. SU-F-T-42: MRI and TRUS Image Fusion as a Mode of Generating More Accurate Prostate Contours

    International Nuclear Information System (INIS)

    Petronek, M; Purysko, A; Balik, S; Ciezki, J; Klein, E; Wilkinson, D

    2016-01-01

    Purpose: Transrectal Ultrasound (TRUS) imaging is utilized intra-operatively for LDR permanent prostate seed implant treatment planning. Prostate contouring with TRUS can be challenging at the apex and base. This study attempts to improve accuracy of prostate contouring with MRI-TRUS fusion to prevent over- or under-estimation of the prostate volume. Methods: 14 patients with previous MRI guided prostate biopsy and undergone an LDR permanent prostate seed implant have been selected. The prostate was contoured on the MRI images (1 mm slice thickness) by a radiologist. The prostate was also contoured on TRUS images (5 mm slice thickness) during LDR procedure by a urologist. MRI and TRUS images were rigidly fused manually and the prostate contours from MRI and TRUS were compared using Dice similarity coefficient, percentage volume difference and length, height and width differences. Results: The prostate volume was overestimated by 8 ± 18% (range: 34% to −25%) in TRUS images compared to MRI. The mean Dice was 0.77 ± 0.09 (range: 0.53 to 0.88). The mean difference (TRUS-MRI) in the prostate width was 0 ± 4 mm (range: −11 to 5 mm), height was −3 ± 6 mm (range: −13 to 6 mm) and length was 6 ± 6 (range: −10 to 16 mm). Prostate was overestimated with TRUS imaging at the base for 6 cases (mean: 8 ± 4 mm and range: 5 to 14 mm), at the apex for 6 cases (mean: 11 ± 3 mm and range: 5 to 15 mm) and 1 case was underestimated at both base and apex by 4 mm. Conclusion: Use of intra-operative TRUS and MRI image fusion can help to improve the accuracy of prostate contouring by accurately accounting for prostate over- or under-estimations, especially at the base and apex. The mean amount of discrepancy is within a range that is significant for LDR sources.

  7. Performance evaluation of objective quality metrics for HDR image compression

    Science.gov (United States)

    Valenzise, Giuseppe; De Simone, Francesca; Lauga, Paul; Dufaux, Frederic

    2014-09-01

    Due to the much larger luminance and contrast characteristics of high dynamic range (HDR) images, well-known objective quality metrics, widely used for the assessment of low dynamic range (LDR) content, cannot be directly applied to HDR images in order to predict their perceptual fidelity. To overcome this limitation, advanced fidelity metrics, such as the HDR-VDP, have been proposed to accurately predict visually significant differences. However, their complex calibration may make them difficult to use in practice. A simpler approach consists in computing arithmetic or structural fidelity metrics, such as PSNR and SSIM, on perceptually encoded luminance values but the performance of quality prediction in this case has not been clearly studied. In this paper, we aim at providing a better comprehension of the limits and the potentialities of this approach, by means of a subjective study. We compare the performance of HDR-VDP to that of PSNR and SSIM computed on perceptually encoded luminance values, when considering compressed HDR images. Our results show that these simpler metrics can be effectively employed to assess image fidelity for applications such as HDR image compression.

  8. More accurate picture of human body organs

    International Nuclear Information System (INIS)

    Kolar, J.

    1985-01-01

    Computerized tomography and nucler magnetic resonance tomography (NMRT) are revolutionary contributions to radiodiagnosis because they allow to obtain a more accurate image of human body organs. The principles are described of both methods. Attention is mainly devoted to NMRT which has clinically only been used for three years. It does not burden the organism with ionizing radiation. (Ha)

  9. Usefulness of MR coronal imaging of the ''pyramidal line''. Predictive value in motor function of stroke patients

    International Nuclear Information System (INIS)

    Satoh, Kenichi; Matsuzaki, Takayuki; Shimazaki, Mitsunori

    1997-01-01

    A coronal image was obtained along a straight line between the front edge of the medulla and the deepest point of the interpeduncular cistern in the midsagittal plane (the ''pyramidal line''). This coronal image along the pyramidal line revealed the pyramidal tract extending from the internal capsule down to the medulla. Using this technique, we studied 25 patients with motor deficit associated with cerebrovascular disease. We predicted the possibility of their recovery in the subacute stage. We were able to predict the recovery in 23 patients (92%). We conclude that a coronal image along the pyramidal line is useful for the understanding of relation between pyramidal tract and lesion and for predicting motor function. (author)

  10. Usefulness of MR coronal imaging of the ``pyramidal line``. Predictive value in motor function of stroke patients

    Energy Technology Data Exchange (ETDEWEB)

    Satoh, Kenichi; Matsuzaki, Takayuki; Shimazaki, Mitsunori [Hakodate Red Cross Hospital, Hokkaido (Japan)

    1997-06-01

    A coronal image was obtained along a straight line between the front edge of the medulla and the deepest point of the interpeduncular cistern in the midsagittal plane (the ``pyramidal line``). This coronal image along the pyramidal line revealed the pyramidal tract extending from the internal capsule down to the medulla. Using this technique, we studied 25 patients with motor deficit associated with cerebrovascular disease. We predicted the possibility of their recovery in the subacute stage. We were able to predict the recovery in 23 patients (92%). We conclude that a coronal image along the pyramidal line is useful for the understanding of relation between pyramidal tract and lesion and for predicting motor function. (author)

  11. Accurate thermoelastic tensor and acoustic velocities of NaCl

    Energy Technology Data Exchange (ETDEWEB)

    Marcondes, Michel L., E-mail: michel@if.usp.br [Physics Institute, University of Sao Paulo, Sao Paulo, 05508-090 (Brazil); Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455 (United States); Shukla, Gaurav, E-mail: shukla@physics.umn.edu [School of Physics and Astronomy, University of Minnesota, Minneapolis, 55455 (United States); Minnesota supercomputer Institute, University of Minnesota, Minneapolis, 55455 (United States); Silveira, Pedro da [Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455 (United States); Wentzcovitch, Renata M., E-mail: wentz002@umn.edu [Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455 (United States); Minnesota supercomputer Institute, University of Minnesota, Minneapolis, 55455 (United States)

    2015-12-15

    Despite the importance of thermoelastic properties of minerals in geology and geophysics, their measurement at high pressures and temperatures are still challenging. Thus, ab initio calculations are an essential tool for predicting these properties at extreme conditions. Owing to the approximate description of the exchange-correlation energy, approximations used in calculations of vibrational effects, and numerical/methodological approximations, these methods produce systematic deviations. Hybrid schemes combining experimental data and theoretical results have emerged as a way to reconcile available information and offer more reliable predictions at experimentally inaccessible thermodynamics conditions. Here we introduce a method to improve the calculated thermoelastic tensor by using highly accurate thermal equation of state (EoS). The corrective scheme is general, applicable to crystalline solids with any symmetry, and can produce accurate results at conditions where experimental data may not exist. We apply it to rock-salt-type NaCl, a material whose structural properties have been challenging to describe accurately by standard ab initio methods and whose acoustic/seismic properties are important for the gas and oil industry.

  12. A NEW CLINICAL PREDICTION CRITERION ACCURATELY DETERMINES A SUBSET OF PATIENTS WITH BILATERAL PRIMARY ALDOSTERONISM BEFORE ADRENAL VENOUS SAMPLING.

    Science.gov (United States)

    Kocjan, Tomaz; Janez, Andrej; Stankovic, Milenko; Vidmar, Gaj; Jensterle, Mojca

    2016-05-01

    Adrenal venous sampling (AVS) is the only available method to distinguish bilateral from unilateral primary aldosteronism (PA). AVS has several drawbacks, so it is reasonable to avoid this procedure when the results would not affect clinical management. Our objective was to identify a clinical criterion that can reliably predict nonlateralized AVS as a surrogate for bilateral PA that is not treated surgically. A retrospective diagnostic cross-sectional study conducted at Slovenian national endocrine referral center included 69 consecutive patients (mean age 56 ± 8 years, 21 females) with PA who underwent AVS. PA was confirmed with the saline infusion test (SIT). AVS was performed sequentially during continuous adrenocorticotrophic hormone (ACTH) infusion. The main outcome measures were variables associated with nonlateralized AVS to derive a clinical prediction rule. Sixty-seven (97%) patients had a successful AVS and were included in the statistical analysis. A total of 39 (58%) patients had nonlateralized AVS. The combined criterion of serum potassium ≥3.5 mmol/L, post-SIT aldosterone AVS. The best overall classification accuracy (50/67 = 75%) was achieved using the post-SIT aldosterone level AVS. Our clinical prediction criterion appears to accurately determine a subset of patients with bilateral PA who could avoid unnecessary AVS and immediately commence with medical treatment.

  13. Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

    Science.gov (United States)

    Reynolds, Sheila M.; Bilmes, Jeff A.; Noble, William Stafford

    2010-01-01

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence—301 base pairs, centered at the position to be scored—with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  14. Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

    Directory of Open Access Journals (Sweden)

    Sheila M Reynolds

    2010-07-01

    Full Text Available DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the

  15. Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

    Science.gov (United States)

    Reynolds, Sheila M; Bilmes, Jeff A; Noble, William Stafford

    2010-07-08

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  16. Cluster abundance in chameleon f ( R ) gravity I: toward an accurate halo mass function prediction

    Energy Technology Data Exchange (ETDEWEB)

    Cataneo, Matteo; Rapetti, David [Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen, Juliane Maries Vej 30, 2100 Copenhagen (Denmark); Lombriser, Lucas [Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ (United Kingdom); Li, Baojiu, E-mail: matteoc@dark-cosmology.dk, E-mail: drapetti@dark-cosmology.dk, E-mail: llo@roe.ac.uk, E-mail: baojiu.li@durham.ac.uk [Institute for Computational Cosmology, Department of Physics, Durham University, South Road, Durham DH1 3LE (United Kingdom)

    2016-12-01

    We refine the mass and environment dependent spherical collapse model of chameleon f ( R ) gravity by calibrating a phenomenological correction inspired by the parameterized post-Friedmann framework against high-resolution N -body simulations. We employ our method to predict the corresponding modified halo mass function, and provide fitting formulas to calculate the enhancement of the f ( R ) halo abundance with respect to that of General Relativity (GR) within a precision of ∼< 5% from the results obtained in the simulations. Similar accuracy can be achieved for the full f ( R ) mass function on the condition that the modeling of the reference GR abundance of halos is accurate at the percent level. We use our fits to forecast constraints on the additional scalar degree of freedom of the theory, finding that upper bounds competitive with current Solar System tests are within reach of cluster number count analyses from ongoing and upcoming surveys at much larger scales. Importantly, the flexibility of our method allows also for this to be applied to other scalar-tensor theories characterized by a mass and environment dependent spherical collapse.

  17. Assessing the performance of aerial image point cloud and spectral metrics in predicting boreal forest canopy cover

    Science.gov (United States)

    Melin, M.; Korhonen, L.; Kukkonen, M.; Packalen, P.

    2017-07-01

    Canopy cover (CC) is a variable used to describe the status of forests and forested habitats, but also the variable used primarily to define what counts as a forest. The estimation of CC has relied heavily on remote sensing with past studies focusing on satellite imagery as well as Airborne Laser Scanning (ALS) using light detection and ranging (lidar). Of these, ALS has been proven highly accurate, because the fraction of pulses penetrating the canopy represents a direct measurement of canopy gap percentage. However, the methods of photogrammetry can be applied to produce point clouds fairly similar to airborne lidar data from aerial images. Currently there is little information about how well such point clouds measure canopy density and gaps. The aim of this study was to assess the suitability of aerial image point clouds for CC estimation and compare the results with those obtained using spectral data from aerial images and Landsat 5. First, we modeled CC for n = 1149 lidar plots using field-measured CCs and lidar data. Next, this data was split into five subsets in north-south direction (y-coordinate). Finally, four CC models (AerialSpectral, AerialPointcloud, AerialCombi (spectral + pointcloud) and Landsat) were created and they were used to predict new CC values to the lidar plots, subset by subset, using five-fold cross validation. The Landsat and AerialSpectral models performed with RMSEs of 13.8% and 12.4%, respectively. AerialPointcloud model reached an RMSE of 10.3%, which was further improved by the inclusion of spectral data; RMSE of the AerialCombi model was 9.3%. We noticed that the aerial image point clouds managed to describe only the outermost layer of the canopy and missed the details in lower canopy, which was resulted in weak characterization of the total CC variation, especially in the tails of the data.

  18. No-reference image quality assessment based on statistics of convolution feature maps

    Science.gov (United States)

    Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

    2018-04-01

    We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

  19. 2D and 3D imaging resolution trade-offs in quantifying pore throats for prediction of permeability

    Energy Technology Data Exchange (ETDEWEB)

    Beckingham, Lauren E.; Peters, Catherine A.; Um, Wooyong; Jones, Keith W.; Lindquist, W.Brent

    2013-09-03

    Although the impact of subsurface geochemical reactions on porosity is relatively well understood, changes in permeability remain difficult to estimate. In this work, pore-network modeling was used to predict permeability based on pore- and pore-throat size distributions determined from analysis of 2D scanning electron microscopy (SEM) images of thin sections and 3D X-ray computed microtomography (CMT) data. The analyzed specimens were a Viking sandstone sample from the Alberta sedimentary basin and an experimental column of reacted Hanford sediments. For the column, a decrease in permeability due to mineral precipitation was estimated, but the permeability estimates were dependent on imaging technique and resolution. X-ray CT imaging has the advantage of reconstructing a 3D pore network while 2D SEM imaging can easily analyze sub-grain and intragranular variations in mineralogy. Pore network models informed by analyses of 2D and 3D images at comparable resolutions produced permeability esti- mates with relatively good agreement. Large discrepancies in predicted permeabilities resulted from small variations in image resolution. Images with resolutions 0.4 to 4 lm predicted permeabilities differ- ing by orders of magnitude. While lower-resolution scans can analyze larger specimens, small pore throats may be missed due to resolution limitations, which in turn overestimates permeability in a pore-network model in which pore-to-pore conductances are statistically assigned. Conversely, high-res- olution scans are capable of capturing small pore throats, but if they are not actually flow-conducting predicted permeabilities will be below expected values. In addition, permeability is underestimated due to misinterpreting surface-roughness features as small pore throats. Comparison of permeability pre- dictions with expected and measured permeability values showed that the largest discrepancies resulted from the highest resolution images and the best predictions of

  20. A unique charge-coupled device/xenon arc lamp based imaging system for the accurate detection and quantitation of multicolour fluorescence.

    Science.gov (United States)

    Spibey, C A; Jackson, P; Herick, K

    2001-03-01

    In recent years the use of fluorescent dyes in biological applications has dramatically increased. The continual improvement in the capabilities of these fluorescent dyes demands increasingly sensitive detection systems that provide accurate quantitation over a wide linear dynamic range. In the field of proteomics, the detection, quantitation and identification of very low abundance proteins are of extreme importance in understanding cellular processes. Therefore, the instrumentation used to acquire an image of such samples, for spot picking and identification by mass spectrometry, must be sensitive enough to be able, not only, to maximise the sensitivity and dynamic range of the staining dyes but, as importantly, adapt to the ever changing portfolio of fluorescent dyes as they become available. Just as the available fluorescent probes are improving and evolving so are the users application requirements. Therefore, the instrumentation chosen must be flexible to address and adapt to those changing needs. As a result, a highly competitive market for the supply and production of such dyes and the instrumentation for their detection and quantitation have emerged. The instrumentation currently available is based on either laser/photomultiplier tube (PMT) scanning or lamp/charge-coupled device (CCD) based mechanisms. This review briefly discusses the advantages and disadvantages of both System types for fluorescence imaging, gives a technical overview of CCD technology and describes in detail a unique xenon/are lamp CCD based instrument, from PerkinElmer Life Sciences. The Wallac-1442 ARTHUR is unique in its ability to scan both large areas at high resolution and give accurate selectable excitation over the whole of the UV/visible range. It operates by filtering both the excitation and emission wavelengths, providing optimal and accurate measurement and quantitation of virtually any available dye and allows excellent spectral resolution between different fluorophores

  1. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

    Science.gov (United States)

    Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-09-18

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

  2. Endovascular image-guided interventions (EIGIs)

    International Nuclear Information System (INIS)

    Rudin, Stephen; Bednarek, Daniel R.; Hoffmann, Kenneth R.

    2008-01-01

    Minimally invasive interventions are rapidly replacing invasive surgical procedures for the most prevalent human disease conditions. X-ray image-guided interventions carried out using the insertion and navigation of catheters through the vasculature are increasing in number and sophistication. In this article, we offer our vision for the future of this dynamic field of endovascular image-guided interventions in the form of predictions about (1) improvements in high-resolution detectors for more accurate guidance, (2) the implementation of high-resolution region of interest computed tomography for evaluation and planning, (3) the implementation of dose tracking systems to control patient radiation risk, (4) the development of increasingly sophisticated interventional devices, (5) the use of quantitative treatment planning with patient-specific computer fluid dynamic simulations, and (6) the new expanding role of the medical physicist. We discuss how we envision our predictions will come to fruition and result in the universal goal of improved patient care.

  3. Chondromalacia patellae: fat-suppressed MR imaging.

    Science.gov (United States)

    Rose, P M; Demlow, T A; Szumowski, J; Quinn, S F

    1994-11-01

    To evaluate the accuracy of fat-suppressed magnetic resonance (MR) imaging in diagnosing chondromalacia patellae. Seventy-one patients underwent fat-suppressed MR imaging and arthroscopy of the patellofemoral compartment. Findings were classified as early or advanced chondromalacia or as normal and were correlated with arthroscopic findings. Early and advanced stages of chondromalacia patellae were reliably detected, with positive predictive values of 85% and 92%, respectively. Specificity in early stages was 94% and in late stages was 98%. However, the overall accuracies did not differ substantially from those reported in studies that did not use fat-suppressed imaging. Axial, fat-suppressed MR imaging accurately depicts changes caused by chondromalacia patellae. Early stages can be seen as intrasubstance changes of increased signal intensity. Results of this study suggest a high degree of specificity in excluding both early and advanced changes.

  4. Extracting 3D layout from a single image using global image structures.

    Science.gov (United States)

    Lou, Zhongyu; Gevers, Theo; Hu, Ninghang

    2015-10-01

    Extracting the pixel-level 3D layout from a single image is important for different applications, such as object localization, image, and video categorization. Traditionally, the 3D layout is derived by solving a pixel-level classification problem. However, the image-level 3D structure can be very beneficial for extracting pixel-level 3D layout since it implies the way how pixels in the image are organized. In this paper, we propose an approach that first predicts the global image structure, and then we use the global structure for fine-grained pixel-level 3D layout extraction. In particular, image features are extracted based on multiple layout templates. We then learn a discriminative model for classifying the global layout at the image-level. Using latent variables, we implicitly model the sublevel semantics of the image, which enrich the expressiveness of our model. After the image-level structure is obtained, it is used as the prior knowledge to infer pixel-wise 3D layout. Experiments show that the results of our model outperform the state-of-the-art methods by 11.7% for 3D structure classification. Moreover, we show that employing the 3D structure prior information yields accurate 3D scene layout segmentation.

  5. Ability of preoperative 3.0-Tesla magnetic resonance imaging to predict the absence of side-specific extracapsular extension of prostate cancer.

    Science.gov (United States)

    Hara, Tomohiko; Nakanishi, Hiroyuki; Nakagawa, Tohru; Komiyama, Motokiyo; Kawahara, Takashi; Manabe, Tomoko; Miyake, Mototaka; Arai, Eri; Kanai, Yae; Fujimoto, Hiroyuki

    2013-10-01

    Recent studies have shown an improvement in prostate cancer diagnosis with the use of 3.0-Tesla magnetic resonance imaging. We retrospectively assessed the ability of this imaging technique to predict side-specific extracapsular extension of prostate cancer. From October 2007 to August 2011, prostatectomy was carried out in 396 patients after preoperative 3.0-Tesla magnetic resonance imaging. Among these, 132 (primary sample) and 134 patients (validation sample) underwent 12-core prostate biopsy at the National Cancer Center Hospital of Tokyo, Japan, and at other institutions, respectively. In the primary dataset, univariate and multivariate analyses were carried out to predict side-specific extracapsular extension using variables determined preoperatively, including 3.0-Tesla magnetic resonance imaging findings (T2-weighted and diffusion-weighted imaging). A prediction model was then constructed and applied to the validation study sample. Multivariate analysis identified four significant independent predictors (P Tesla diffusion-weighted magnetic resonance imaging findings, ≥2 positive biopsy cores on each side and a maximum percentage of positive cores ≥31% on each side. The negative predictive value was 93.9% in the combination model with these four predictors, meanwhile the positive predictive value was 33.8%. Good reproducibility of these four significant predictors and the combination model was observed in the validation study sample. The side-specific extracapsular extension prediction by the biopsy Gleason score and factors associated with tumor location, including a positive 3.0-Tesla diffusion-weighted magnetic resonance imaging finding, have a high negative predictive value, but a low positive predictive value. © 2013 The Japanese Urological Association.

  6. Multilocular cystic renal cell carcinoma: imaging and clinical correlation

    International Nuclear Information System (INIS)

    Xu Yong; Zhang Sheng

    2013-01-01

    Multilocular cystic renal cell carcinoma (MCRCC) is a subtype of clear cell renal cell carcinoma and has mild clinical symptoms and a favorable prognosis. Accordingly, nephron-sparing surgery is recommended as a therapeutic strategy. If histologic subtype of MCRCC can be predicted preoperatively with an acceptable level of accuracy, it may be important in predicting prognosis and make clinical management. Most MCRCCs show characteristic cross-sectional imaging findings and permit accurate diagnosis before the treatment. Cross -sectional imaging of MCRCC reveals a well -defined multilocular cystic mass with irregularly enhanced thickened septa and without enhanced intracystic solid nodule. It is often classified as Bosniak classification Ⅲ , which is significantly different from that of other renal cystic masses. The clinical, pathologic, and radiologic features of MCRCC were discussed and illustrated in this article. The role of the imaging preoperative evaluation for MCRCC, and management implications were emphasized. (authors)

  7. Accurate prediction of complex free surface flow around a high speed craft using a single-phase level set method

    Science.gov (United States)

    Broglia, Riccardo; Durante, Danilo

    2017-11-01

    This paper focuses on the analysis of a challenging free surface flow problem involving a surface vessel moving at high speeds, or planing. The investigation is performed using a general purpose high Reynolds free surface solver developed at CNR-INSEAN. The methodology is based on a second order finite volume discretization of the unsteady Reynolds-averaged Navier-Stokes equations (Di Mascio et al. in A second order Godunov—type scheme for naval hydrodynamics, Kluwer Academic/Plenum Publishers, Dordrecht, pp 253-261, 2001; Proceedings of 16th international offshore and polar engineering conference, San Francisco, CA, USA, 2006; J Mar Sci Technol 14:19-29, 2009); air/water interface dynamics is accurately modeled by a non standard level set approach (Di Mascio et al. in Comput Fluids 36(5):868-886, 2007a), known as the single-phase level set method. In this algorithm the governing equations are solved only in the water phase, whereas the numerical domain in the air phase is used for a suitable extension of the fluid dynamic variables. The level set function is used to track the free surface evolution; dynamic boundary conditions are enforced directly on the interface. This approach allows to accurately predict the evolution of the free surface even in the presence of violent breaking waves phenomena, maintaining the interface sharp, without any need to smear out the fluid properties across the two phases. This paper is aimed at the prediction of the complex free-surface flow field generated by a deep-V planing boat at medium and high Froude numbers (from 0.6 up to 1.2). In the present work, the planing hull is treated as a two-degree-of-freedom rigid object. Flow field is characterized by the presence of thin water sheets, several energetic breaking waves and plungings. The computational results include convergence of the trim angle, sinkage and resistance under grid refinement; high-quality experimental data are used for the purposes of validation, allowing to

  8. Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk

    Science.gov (United States)

    Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Patel, Bhavika; Heidari, Morteza; Liu, Hong; Zheng, Bin

    2018-05-01

    This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. In the next subsequent mammography screening, 402 women were diagnosed with breast cancer and 642 remained negative. An existing CAD scheme was applied ‘as is’ to process each image. From CAD-generated results, four detection features including the total number of (1) initial detection seeds and (2) the final detected false-positive regions, (3) average and (4) sum of detection scores, were computed from each image. Then, by combining the features computed from two bilateral images of left and right breasts from either craniocaudal or mediolateral oblique view, two logistic regression models were trained and tested using a leave-one-case-out cross-validation method to predict the likelihood of each testing case being positive in the next subsequent screening. The new prediction model yielded the maximum prediction accuracy with an area under a ROC curve of AUC  =  0.65  ±  0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of (2.95, 6.83). The results also showed an increasing trend in the adjusted odds ratio and risk prediction scores (p  breast cancer risk.

  9. Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.

    Science.gov (United States)

    Depeursinge, Adrien; Kurtz, Camille; Beaulieu, Christopher; Napel, Sandy; Rubin, Daniel

    2014-08-01

    We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using linear combinations of high-order steerable Riesz wavelets and support vector machines (SVM). In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a nonhierarchical computationally-derived ontology of VST containing inter-term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave-one-patient-out cross-validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST. The proposed framework is expected to foster human-computer synergies for the interpretation of radiological images while using rotation-covariant computational models of VSTs to 1) quantify their local likelihood and 2) explicitly link them with pixel-based image content in the context of a given imaging domain.

  10. "Refsdal" Meets Popper: Comparing Predictions of the Re-appearance of the Multiply Imaged Supernova Behind MACSJ1149.5+2223

    Science.gov (United States)

    Treu, T.; Brammer, G.; Diego, J. M.; Grillo, C.; Kelly, P. L.; Oguri, M.; Rodney, S. A.; Rosati, P.; Sharon, K.; Zitrin, A.; Balestra, I.; Bradač, M.; Broadhurst, T.; Caminha, G. B.; Halkola, A.; Hoag, A.; Ishigaki, M.; Johnson, T. L.; Karman, W.; Kawamata, R.; Mercurio, A.; Schmidt, K. B.; Strolger, L.-G.; Suyu, S. H.; Filippenko, A. V.; Foley, R. J.; Jha, S. W.; Patel, B.

    2016-01-01

    Supernova “Refsdal,” multiply imaged by cluster MACS1149.5+2223, represents a rare opportunity to make a true blind test of model predictions in extragalactic astronomy, on a timescale that is short compared to a human lifetime. In order to take advantage of this event, we produced seven gravitational lens models with five independent methods, based on Hubble Space Telescope (HST) Hubble Frontier Field images, along with extensive spectroscopic follow-up observations by HST, the Very Large and the Keck Telescopes. We compare the model predictions and show that they agree reasonably well with the measured time delays and magnification ratios between the known images, even though these quantities were not used as input. This agreement is encouraging, considering that the models only provide statistical uncertainties, and do not include additional sources of uncertainties such as structure along the line of sight, cosmology, and the mass sheet degeneracy. We then present the model predictions for the other appearances of supernova “Refsdal.” A future image will reach its peak in the first half of 2016, while another image appeared between 1994 and 2004. The past image would have been too faint to be detected in existing archival images. The future image should be approximately one-third as bright as the brightest known image (I.e., {H}{{AB}}≈ 25.7 mag at peak and {H}{{AB}}≈ 26.7 mag six months before peak), and thus detectable in single-orbit HST images. We will find out soon whether our predictions are correct.

  11. Validation of the Gatortail method for accurate sizing of pulmonary vessels from 3D medical images.

    Science.gov (United States)

    O'Dell, Walter G; Gormaley, Anne K; Prida, David A

    2017-12-01

    , representing vessel diameters ranging from 1.2 to 7 mm. The linear regression fit gave a slope of 1.056 and an R 2 value of 0.989. These three metrics reflect superior agreement of the radii estimates relative to previously published results over all sizes tested. Sizing via matched Gaussian filters resulted in size underestimates of >33% over all three test vessels, while the tubularity-metric matching exhibited a sizing uncertainty of >50%. In the human chest CT data set, the vessel voxel intensity profiles with and without branch model optimization showed excellent agreement and improvement in the objective measure of image similarity. Gatortail has been demonstrated to be an automated, objective, accurate and robust method for sizing of vessels in 3D non-invasively from chest CT scans. We anticipate that Gatortail, an image-based approach to automatically compute estimates of blood vessel radii and trajectories from 3D medical images, will facilitate future quantitative evaluation of vascular response to disease and environmental insult and improve understanding of the biological mechanisms underlying vascular disease processes. © 2017 American Association of Physicists in Medicine.

  12. Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors.

    Science.gov (United States)

    Yang, Wei; Zhong, Liming; Chen, Yang; Lin, Liyan; Lu, Zhentai; Liu, Shupeng; Wu, Yao; Feng, Qianjin; Chen, Wufan

    2018-04-01

    Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.

  13. Quantitative analysis of length-diameter distribution and cross-sectional properties of fibers from three-dimensional tomographic images

    DEFF Research Database (Denmark)

    Miettinen, Arttu; Joffe, Roberts; Madsen, Bo

    2013-01-01

    obtained from optical microscopy of polished cross-sections of a composite. This approach gives accurate yet local results, but a rather large number of optical images have to be processed to achieve a representative description of the morphology of the material. In this work a fully automatic algorithm......A number of rule-of-mixture micromechanical models have been successfully used to predict the mechanical properties of short fiber composites. However, in order to obtain accurate predictions, a detailed description of the internal structure of the material is required. This information is often...... for estimating the length-diameter distribution of solid or hollow fibers, utilizing three-dimensional X-ray tomographic images, is presented. The method is based on a granulometric approach for fiber length distribution measurement, combined with a novel algorithm that relates cross-sectional fiber properties...

  14. Preoperative implant planning considering alveolar bone grafting needs and complication prediction using panoramic versus CBCT images

    Energy Technology Data Exchange (ETDEWEB)

    Guerrero, Maria Eugenia; Jacobs, Reinhilde [OIC, OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven, Leuven (Belgium); Noriega, Jorge [Master of Periodontology, Universidad San Martin de Porres, Lima (Peru)

    2014-09-15

    This study was performed to determine the efficacy of observers' prediction for the need of bone grafting and presence of perioperative complications on the basis of cone-beam computed tomography (CBCT) and panoramic radiographic (PAN) planning as compared to the surgical outcome. One hundred and eight partially edentulous patients with a need for implant rehabilitation were referred for preoperative imaging. Imaging consisted of PAN and CBCT images. Four observers carried out implant planning using PAN image datasets, and at least one month later, using CBCT image datasets. Based on their own planning, the observers assessed the need for bone graft augmentation as well as complication prediction. The implant length and diameter, the need for bone graft augmentation, and the occurrence of anatomical complications during planning and implant placement were statistically compared. In the 108 patients, 365 implants were installed. Receiver operating characteristic analyses of both PAN and CBCT preoperative planning showed that CBCT performed better than PAN-based planning with respect to the need for bone graft augmentation and perioperative complications. The sensitivity and the specificity of CBCT for implant complications were 96.5% and 90.5%, respectively, and for bone graft augmentation, they were 95.2% and 96.3%, respectively. Significant differences were found between PAN-based planning and the surgery of posterior implant lengths. Our findings indicated that CBCT-based preoperative implant planning enabled treatment planning with a higher degree of prediction and agreement as compared to the surgical standard. In PAN-based surgery, the prediction of implant length was poor.

  15. Respiratory variation in peak aortic velocity accurately predicts fluid responsiveness in children undergoing neurosurgery under general anesthesia.

    Science.gov (United States)

    Morparia, Kavita G; Reddy, Srijaya K; Olivieri, Laura J; Spaeder, Michael C; Schuette, Jennifer J

    2018-04-01

    The determination of fluid responsiveness in the critically ill child is of vital importance, more so as fluid overload becomes increasingly associated with worse outcomes. Dynamic markers of volume responsiveness have shown some promise in the pediatric population, but more research is needed before they can be adopted for widespread use. Our aim was to investigate effectiveness of respiratory variation in peak aortic velocity and pulse pressure variation to predict fluid responsiveness, and determine their optimal cutoff values. We performed a prospective, observational study at a single tertiary care pediatric center. Twenty-one children with normal cardiorespiratory status undergoing general anesthesia for neurosurgery were enrolled. Respiratory variation in peak aortic velocity (ΔVpeak ao) was measured both before and after volume expansion using a bedside ultrasound device. Pulse pressure variation (PPV) value was obtained from the bedside monitor. All patients received a 10 ml/kg fluid bolus as volume expansion, and were qualified as responders if stroke volume increased >15% as a result. Utility of ΔVpeak ao and PPV and to predict responsiveness to volume expansion was investigated. A baseline ΔVpeak ao value of greater than or equal to 12.3% best predicted a positive response to volume expansion, with a sensitivity of 77%, specificity of 89% and area under receiver operating characteristic curve of 0.90. PPV failed to demonstrate utility in this patient population. Respiratory variation in peak aortic velocity is a promising marker for optimization of perioperative fluid therapy in the pediatric population and can be accurately measured using bedside ultrasonography. More research is needed to evaluate the lack of effectiveness of pulse pressure variation for this purpose.

  16. Studying depression using imaging and machine learning methods.

    Science.gov (United States)

    Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

    2016-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

  17. High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately.

    Science.gov (United States)

    Chen, Wenjin; Wong, Chung; Vosburgh, Evan; Levine, Arnold J; Foran, David J; Xu, Eugenia Y

    2014-07-08

    The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application - SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary "Manual Initialize" and "Hand Draw" tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model

  18. Non-isothermal kinetics model to predict accurate phase transformation and hardness of 22MnB5 boron steel

    Energy Technology Data Exchange (ETDEWEB)

    Bok, H.-H.; Kim, S.N.; Suh, D.W. [Graduate Institute of Ferrous Technology, POSTECH, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongsangbuk-do (Korea, Republic of); Barlat, F., E-mail: f.barlat@postech.ac.kr [Graduate Institute of Ferrous Technology, POSTECH, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongsangbuk-do (Korea, Republic of); Lee, M.-G., E-mail: myounglee@korea.ac.kr [Department of Materials Science and Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul (Korea, Republic of)

    2015-02-25

    A non-isothermal phase transformation kinetics model obtained by modifying the well-known JMAK approach is proposed for application to a low carbon boron steel (22MnB5) sheet. In the modified kinetics model, the parameters are functions of both temperature and cooling rate, and can be identified by a numerical optimization method. Moreover, in this approach the transformation start and finish temperatures are variable instead of the constants that depend on chemical composition. These variable reference temperatures are determined from the measured CCT diagram using dilatation experiments. The kinetics model developed in this work captures the complex transformation behavior of the boron steel sheet sample accurately. In particular, the predicted hardness and phase fractions in the specimens subjected to a wide range of cooling rates were validated by experiments.

  19. Computerized method for evaluating diagnostic image quality of calcified plaque images in cardiac CT: Validation on a physical dynamic cardiac phantom

    International Nuclear Information System (INIS)

    King, Martin; Rodgers, Zachary; Giger, Maryellen L.; Bardo, Dianna M. E.; Patel, Amit R.

    2010-01-01

    Purpose: In cardiac computed tomography (CT), important clinical indices, such as the coronary calcium score and the percentage of coronary artery stenosis, are often adversely affected by motion artifacts. As a result, the expert observer must decide whether or not to use these indices during image interpretation. Computerized methods potentially can be used to assist in these decisions. In a previous study, an artificial neural network (ANN) regression model provided assessability (image quality) indices of calcified plaque images from the software NCAT phantom that were highly agreeable with those provided by expert observers. The method predicted assessability indices based on computer-extracted features of the plaque. In the current study, the ANN-predicted assessability indices were used to identify calcified plaque images with diagnostic calcium scores (based on mass) from a physical dynamic cardiac phantom. The basic assumption was that better quality images were associated with more accurate calcium scores. Methods: A 64-channel CT scanner was used to obtain 500 calcified plaque images from a physical dynamic cardiac phantom at different heart rates, cardiac phases, and plaque locations. Two expert observers independently provided separate sets of assessability indices for each of these images. Separate sets of ANN-predicted assessability indices tailored to each observer were then generated within the framework of a bootstrap resampling scheme. For each resampling iteration, the absolute calcium score error between the calcium scores of the motion-contaminated plaque image and its corresponding stationary image served as the ground truth in terms of indicating images with diagnostic calcium scores. The performances of the ANN-predicted and observer-assigned indices in identifying images with diagnostic calcium scores were then evaluated using ROC analysis. Results: Assessability indices provided by the first observer and the corresponding ANN performed

  20. Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning.

    Science.gov (United States)

    Sun, Yu; Reynolds, Hayley M; Wraith, Darren; Williams, Scott; Finnegan, Mary E; Mitchell, Catherine; Murphy, Declan; Haworth, Annette

    2018-04-26

    There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques. In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements. Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 10 3 cells/mm 2 and a relative deviation of 13.3 ± 0.8%. Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

  1. Prediction of Early Response to Chemotherapy in Lung Cancer by Using Diffusion-Weighted MR Imaging

    Directory of Open Access Journals (Sweden)

    Jing Yu

    2014-01-01

    Full Text Available Purpose. To determine whether change of apparent diffusion coefficient (ADC value could predict early response to chemotherapy in lung cancer. Materials and Methods. Twenty-five patients with advanced non-small cell lung cancer underwent chest MR imaging including DWI before and at the end of the first cycle of chemotherapy. The tumor’s mean ADC value and diameters on MR images were calculated and compared. The grouping reference was based on serial CT scans according to Response Evaluation Criteria in Solid Tumors. Logistic regression was applied to assess treatment response prediction ability of ADC value and diameters. Results. The change of ADC value in partial response group was higher than that in stable disease group (P=0.004. ROC curve showed that ADC value could predict treatment response with 100% sensitivity, 64.71% specificity, 57.14% positive predictive value, 100% negative predictive value, and 82.7% accuracy. The area under the curve for combination of ADC value and longest diameter change was higher than any parameter alone (P≤0.01. Conclusions. The change of ADC value may be a sensitive indicator to predict early response to chemotherapy in lung cancer. Prediction ability could be improved by combining the change of ADC value and longest diameter.

  2. Laser Guided Automated Calibrating System for Accurate Bracket ...

    African Journals Online (AJOL)

    Background: The basic premise of preadjusted bracket system is accurate bracket positioning. ... using MATLAB ver. 7 software (The MathWorks Inc.). These images are in the form of matrices of size 640 × 480. 650 nm (red light) type III diode laser is used as ... motion control and Pitch, Yaw, Roll degrees of freedom (DOF).

  3. Accurate density functional prediction of molecular electron affinity with the scaling corrected Kohn–Sham frontier orbital energies

    Science.gov (United States)

    Zhang, DaDi; Yang, Xiaolong; Zheng, Xiao; Yang, Weitao

    2018-04-01

    Electron affinity (EA) is the energy released when an additional electron is attached to an atom or a molecule. EA is a fundamental thermochemical property, and it is closely pertinent to other important properties such as electronegativity and hardness. However, accurate prediction of EA is difficult with density functional theory methods. The somewhat large error of the calculated EAs originates mainly from the intrinsic delocalisation error associated with the approximate exchange-correlation functional. In this work, we employ a previously developed non-empirical global scaling correction approach, which explicitly imposes the Perdew-Parr-Levy-Balduz condition to the approximate functional, and achieve a substantially improved accuracy for the calculated EAs. In our approach, the EA is given by the scaling corrected Kohn-Sham lowest unoccupied molecular orbital energy of the neutral molecule, without the need to carry out the self-consistent-field calculation for the anion.

  4. Effect of computational grid on accurate prediction of a wind turbine rotor using delayed detached-eddy simulations

    Energy Technology Data Exchange (ETDEWEB)

    Bangga, Galih; Weihing, Pascal; Lutz, Thorsten; Krämer, Ewald [University of Stuttgart, Stuttgart (Germany)

    2017-05-15

    The present study focuses on the impact of grid for accurate prediction of the MEXICO rotor under stalled conditions. Two different blade mesh topologies, O and C-H meshes, and two different grid resolutions are tested for several time step sizes. The simulations are carried out using Delayed detached-eddy simulation (DDES) with two eddy viscosity RANS turbulence models, namely Spalart- Allmaras (SA) and Menter Shear stress transport (SST) k-ω. A high order spatial discretization, WENO (Weighted essentially non- oscillatory) scheme, is used in these computations. The results are validated against measurement data with regards to the sectional loads and the chordwise pressure distributions. The C-H mesh topology is observed to give the best results employing the SST k-ω turbulence model, but the computational cost is more expensive as the grid contains a wake block that increases the number of cells.

  5. Predicting Neuroinflammation in Morphine Tolerance for Tolerance Therapy from Immunostaining Images of Rat Spinal Cord.

    Directory of Open Access Journals (Sweden)

    Shinn-Long Lin

    Full Text Available Long-term morphine treatment leads to tolerance which attenuates analgesic effect and hampers clinical utilization. Recent studies have sought to reveal the mechanism of opioid receptors and neuroinflammation by observing morphological changes of cells in the rat spinal cord. This work proposes a high-content screening (HCS based computational method, HCS-Morph, for predicting neuroinflammation in morphine tolerance to facilitate the development of tolerance therapy using immunostaining images for astrocytes, microglia, and neurons in the spinal cord. HCS-Morph first extracts numerous HCS-based features of cellular phenotypes. Next, an inheritable bi-objective genetic algorithm is used to identify a minimal set of features by maximizing the prediction accuracy of neuroinflammation. Finally, a mathematic model using a support vector machine with the identified features is established to predict drug-treated images to assess the effects of tolerance therapy. The dataset consists of 15 saline controls (1 μl/h, 15 morphine-tolerant rats (15 μg/h, and 10 rats receiving a co-infusion of morphine (15 μg/h and gabapentin (15 μg/h, Sigma. The three individual models of astrocytes, microglia, and neurons for predicting neuroinflammation yielded respective Jackknife test accuracies of 96.67%, 90.00%, and 86.67% on the 30 rats, and respective independent test accuracies of 100%, 90%, and 60% on the 10 co-infused rats. The experimental results suggest that neuroinflammation activity expresses more predominantly in astrocytes and microglia than in neuron cells. The set of features for predicting neuroinflammation from images of astrocytes comprises mean cell intensity, total cell area, and second-order geometric moment (relating to cell distribution, relevant to cell communication, cell extension, and cell migration, respectively. The present investigation provides the first evidence for the role of gabapentin in the attenuation of morphine tolerance from

  6. Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers

    Science.gov (United States)

    Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

    2018-06-01

    Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3-1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that can guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and a highly economical path to determining the RI values for a wide range of organic polymers.

  7. Semi-Supervised Tripled Dictionary Learning for Standard-dose PET Image Prediction using Low-dose PET and Multimodal MRI

    Science.gov (United States)

    Wang, Yan; Ma, Guangkai; An, Le; Shi, Feng; Zhang, Pei; Lalush, David S.; Wu, Xi; Pu, Yifei; Zhou, Jiliu; Shen, Dinggang

    2017-01-01

    Objective To obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI). Methods It was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semi-supervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance. Results Validation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods. Conclusion This work proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. Significance The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients. PMID:27187939

  8. Comparison of bone single-photon emission tomography and planar imaging in the detection of vertebral metastases in patients with back pain

    International Nuclear Information System (INIS)

    Han, L.J.; Au-Yong, T.K.; Tong, W.C.M.; Chu, K.S.; Szeto, L.T.; Wong, C.P.

    1998-01-01

    Here we report our experience with bone SPET in the diagnosis of vertebral metastases. This is a retrospective study of technetium-99m methylene diphosphonate (MDP) bone scans in 174 consecutive patients who were referred for the investigation of back pain in our department. MDP planar and SPET images were obtained. Of teh 174 patients, 98 had a known history of malignant tumours. The diagnosis of vertebral metastasis was made on the basis of the patients' clinical histories and the findings with other imaging techniques such as magnetic resonance imaging, computed tomography or follow-up bone scan. We found that the presence of pedicle involvement as seen on SPET was an accurate diagnostic criterion of vertebral metastasis. SPET had a sensitivity of 87%, a specificity of 91%, a positive predictive value of 82%, a negative predictive value of 94% and an accuracy of 90%. On the other hand, planar study had a sensitivity of 74%, a specificity of 81%, a positive predictive value of 64%, a negative predictive value of 88% and an accuracy of 79% in diagnosing vertebral metastasis. Except with regard to the negative predictive value, SPET performed statistically better than planar imaging. Only 9/147 (6.4%) lesions involving the vertebral body alone and 3/49 (6.1%) lesions involving facet joints alone were subsequently found to be metastases. We conclude that bone SPET is an accurate diagnostic test for the detection of vertebral metastases and is superior to planar imaging in this respect. (orig./MG) (orig.)

  9. An Accurate liver segmentation method using parallel computing algorithm

    International Nuclear Information System (INIS)

    Elbasher, Eiman Mohammed Khalied

    2014-12-01

    Computed Tomography (CT or CAT scan) is a noninvasive diagnostic imaging procedure that uses a combination of X-rays and computer technology to produce horizontal, or axial, images (often called slices) of the body. A CT scan shows detailed images of any part of the body, including the bones muscles, fat and organs CT scans are more detailed than standard x-rays. CT scans may be done with or without "contrast Contrast refers to a substance taken by mouth and/ or injected into an intravenous (IV) line that causes the particular organ or tissue under study to be seen more clearly. CT scan of the liver and biliary tract are used in the diagnosis of many diseases in the abdomen structures, particularly when another type of examination, such as X-rays, physical examination, and ultra sound is not conclusive. Unfortunately, the presence of noise and artifact in the edges and fine details in the CT images limit the contrast resolution and make diagnostic procedure more difficult. This experimental study was conducted at the College of Medical Radiological Science, Sudan University of Science and Technology and Fidel Specialist Hospital. The sample of study was included 50 patients. The main objective of this research was to study an accurate liver segmentation method using a parallel computing algorithm, and to segment liver and adjacent organs using image processing technique. The main technique of segmentation used in this study was watershed transform. The scope of image processing and analysis applied to medical application is to improve the quality of the acquired image and extract quantitative information from medical image data in an efficient and accurate way. The results of this technique agreed wit the results of Jarritt et al, (2010), Kratchwil et al, (2010), Jover et al, (2011), Yomamoto et al, (1996), Cai et al (1999), Saudha and Jayashree (2010) who used different segmentation filtering based on the methods of enhancing the computed tomography images. Anther

  10. Rate dependent image distortions in proportional counters

    International Nuclear Information System (INIS)

    Trow, M.W.; Bento, A.C.; Smith, A.

    1994-01-01

    The positional linearity of imaging proportional counters is affected by the intensity distribution of the incident radiation. A mechanism for this effect is described, in which drifting positive ions in the gas produce a distorting electric field which perturbs the trajectories of the primary electrons. In certain cases, the phenomenon causes an apparent improvement of the position resolution. We demonstrate the effect in a detector filled with a xenon-argon-CO 2 mixture. The images obtained are compared with the results of a simulation. If quantitative predictions for a particular detector are required, accurate values of the absolute detector gain, ion mobility and electron drift velocity are needed. ((orig.))

  11. Predicting Electron Population Characteristics in 2-D Using Multispectral Ground-Based Imaging

    Science.gov (United States)

    Grubbs, Guy; Michell, Robert; Samara, Marilia; Hampton, Donald; Jahn, Jorg-Micha

    2018-01-01

    Ground-based imaging and in situ sounding rocket data are compared to electron transport modeling for an active inverted-V type auroral event. The Ground-to-Rocket Electrodynamics-Electrons Correlative Experiment (GREECE) mission successfully launched from Poker Flat, Alaska, on 3 March 2014 at 11:09:50 UT and reached an apogee of approximately 335 km over the aurora. Multiple ground-based electron-multiplying charge-coupled device (EMCCD) imagers were positioned at Venetie, Alaska, and aimed toward magnetic zenith. The imagers observed the intensity of different auroral emission lines (427.8, 557.7, and 844.6 nm) at the magnetic foot point of the rocket payload. Emission line intensity data are correlated with electron characteristics measured by the GREECE onboard electron spectrometer. A modified version of the GLobal airglOW (GLOW) model is used to estimate precipitating electron characteristics based on optical emissions. GLOW predicted the electron population characteristics with 20% error given the observed spectral intensities within 10° of magnetic zenith. Predictions are within 30% of the actual values within 20° of magnetic zenith for inverted-V-type aurora. Therefore, it is argued that this technique can be used, at least in certain types of aurora, such as the inverted-V type presented here, to derive 2-D maps of electron characteristics. These can then be used to further derive 2-D maps of ionospheric parameters as a function of time, based solely on multispectral optical imaging data.

  12. Reperfusion is a more accurate predictor of follow-up infarct volume than recanalization: a proof of concept using CT in acute ischemic stroke patients.

    Science.gov (United States)

    Soares, Bruno P; Tong, Elizabeth; Hom, Jason; Cheng, Su-Chun; Bredno, Joerg; Boussel, Loic; Smith, Wade S; Wintermark, Max

    2010-01-01

    The purpose of this study was to compare recanalization and reperfusion in terms of their predictive value for imaging outcomes (follow-up infarct volume, infarct growth, salvaged penumbra) and clinical outcome in acute ischemic stroke patients. Twenty-two patients admitted within 6 hours of stroke onset were retrospectively included in this study. These patients underwent a first stroke CT protocol including CT-angiography (CTA) and perfusion-CT (PCT) on admission, and similar imaging after treatment, typically around 24 hours, to assess recanalization and reperfusion. Recanalization was assessed by comparing arterial patency on admission and posttreatment CTAs; reperfusion, by comparing the volumes of CBV, CBF, and MTT abnormality on admission and posttreatment PCTs. Collateral flow was graded on the admission CTA. Follow-up infarct volume was measured on the discharge noncontrast CT. The groups of patients with reperfusion, no reperfusion, recanalization, and no recanalization were compared in terms of imaging and clinical outcomes. Reperfusion (using an MTT reperfusion index >75%) was a more accurate predictor of follow-up infarct volume than recanalization. Collateral flow and recanalization were not accurate predictors of follow-up infarct volume. An interaction term was found between reperfusion and the volume of the admission penumbra >50 mL. Our study provides evidence that reperfusion is a more accurate predictor of follow-up infarct volume in acute ischemic stroke patients than recanalization. We recommend an MTT reperfusion index >75% to assess therapy efficacy in future acute ischemic stroke trials that use perfusion-CT.

  13. Characterization of 3-Dimensional PET Systems for Accurate Quantification of Myocardial Blood Flow.

    Science.gov (United States)

    Renaud, Jennifer M; Yip, Kathy; Guimond, Jean; Trottier, Mikaël; Pibarot, Philippe; Turcotte, Eric; Maguire, Conor; Lalonde, Lucille; Gulenchyn, Karen; Farncombe, Troy; Wisenberg, Gerald; Moody, Jonathan; Lee, Benjamin; Port, Steven C; Turkington, Timothy G; Beanlands, Rob S; deKemp, Robert A

    2017-01-01

    Three-dimensional (3D) mode imaging is the current standard for PET/CT systems. Dynamic imaging for quantification of myocardial blood flow with short-lived tracers, such as 82 Rb-chloride, requires accuracy to be maintained over a wide range of isotope activities and scanner counting rates. We proposed new performance standard measurements to characterize the dynamic range of PET systems for accurate quantitative imaging. 82 Rb or 13 N-ammonia (1,100-3,000 MBq) was injected into the heart wall insert of an anthropomorphic torso phantom. A decaying isotope scan was obtained over 5 half-lives on 9 different 3D PET/CT systems and 1 3D/2-dimensional PET-only system. Dynamic images (28 × 15 s) were reconstructed using iterative algorithms with all corrections enabled. Dynamic range was defined as the maximum activity in the myocardial wall with less than 10% bias, from which corresponding dead-time, counting rates, and/or injected activity limits were established for each scanner. Scatter correction residual bias was estimated as the maximum cavity blood-to-myocardium activity ratio. Image quality was assessed via the coefficient of variation measuring nonuniformity of the left ventricular myocardium activity distribution. Maximum recommended injected activity/body weight, peak dead-time correction factor, counting rates, and residual scatter bias for accurate cardiac myocardial blood flow imaging were 3-14 MBq/kg, 1.5-4.0, 22-64 Mcps singles and 4-14 Mcps prompt coincidence counting rates, and 2%-10% on the investigated scanners. Nonuniformity of the myocardial activity distribution varied from 3% to 16%. Accurate dynamic imaging is possible on the 10 3D PET systems if the maximum injected MBq/kg values are respected to limit peak dead-time losses during the bolus first-pass transit. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

  14. Toward accurate and fast iris segmentation for iris biometrics.

    Science.gov (United States)

    He, Zhaofeng; Tan, Tieniu; Sun, Zhenan; Qiu, Xianchao

    2009-09-01

    Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.

  15. Advances of 11C-flumazenil receptor imaging in ischemic penumbra

    International Nuclear Information System (INIS)

    Zhang Jun

    2004-01-01

    The ischemic penumbra is the target of therapy for ischemic stroke patients, so it is extremely important to investigate an imaging technique that may identify accurately the viability of cerebral tissues early. The neuroreceptor imaging with positron emission tomography has achieved some successes in this study field, in particular, the 11 C-flumazenil receptor imaging, which can not only differentiate between the neurons of functional impairment and those of morphological destruction, and then distinguish the ischemic penumbra from the irreversible damage tissues, but predict the malignant course of cerebral infarction. Consequently, these will help to select the patients benefiting from the intervention therapy and plan effectively the therapeutic strategies. (authors)

  16. Integrated global digital image correlation for interface delamination characterization

    KAUST Repository

    Hoefnagels, Johan P.M.

    2013-07-23

    Interfacial delamination is a key reliability challenge in composites and micro-electronic systems due to (high-density) integration of dissimilar materials. Predictive finite element models are used to minimize delamination failures during design, but require accurate interface models to capture (irreversible) crack initiation and propagation behavior observed in experiments. Therefore, an Integrated Global Digital Image Correlation (I-GDIC) strategy is developed for accurate determination of mechanical interface behavior from in-situ delamination experiments. Recently, a novel miniature delamination setup was presented that enables in-situ microscopic characterization of interface delamination while sensitively measuring global load-displacement curves for all mode mixities. Nevertheless, extraction of detailed mechanical interface behavior from measured images is challenging, because deformations are tiny and measurement noise large. Therefore, an advanced I-GDIC methodology is developed which correlates the image patterns by only deforming the images using kinematically-admissible \\'eigenmodes\\' that correspond to the few parameters controlling the interface tractions in an analytic description of the crack tip deformation field, thereby greatly enhancing accuracy and robustness. This method is validated on virtual delamination experiments, simulated using a recently developed self-adaptive cohesive zone (CZ) finite element framework. © The Society for Experimental Mechanics, Inc. 2014.

  17. Predicting Tourist Loyalty to a Small Emerging Destination – The Importance of Destination Image

    Directory of Open Access Journals (Sweden)

    Bình Nghiêm-Phú

    2017-12-01

    Full Text Available In the Southeast Asia region, Vietnam is a developing country and also a developing tourism destination. The number of international tourists to Vietnam has been increasing in recent years. However, the post-trip issues (e.g., not returning, bad word-of-mouth have become the focal points of many arguments. Based on the existing literature, this study devel- oped and tested a theoretical model to predict international tourists’ loyalty to Vietnam from a combination of destination image, tourist motivation, and overall trip satisfaction. The findings revealed that destination image significantly and positively predicted tourists’ overall satisfaction with the trip and their loyalty to the country in the future. Tourist motiva- tion had some weak but significant effects on both overall trip satisfaction and destination loyalty when tested separately; however, the effect on overall trip satisfaction could not be observed when controlled by destination image. Implications were discussed for Vietnam and other small emerging destinations.

  18. The accurate definition of metabolic volumes on 18F-FDG-PET before treatment allows the response to chemoradiotherapy to be predicted in the case of oesophagus cancers

    International Nuclear Information System (INIS)

    Hatt, M.; Cheze-Le Rest, C.; Visvikis, D.; Pradier, O.

    2011-01-01

    This study aims at assessing the possibility of prediction of the response of locally advanced oesophagus cancers, even before the beginning of treatment, by using metabolic volume measurements performed on 18 F-FDG PET images made before the treatment. Medical files of 50 patients have been analyzed. According to the observed responses, and to metabolic volume and Total Lesion Glycosis (TLG) values, it appears that the images allow the extraction of parameters, such as the TLG, which are criteria for the prediction of the therapeutic response. Short communication

  19. Predicting accurate absolute binding energies in aqueous solution

    DEFF Research Database (Denmark)

    Jensen, Jan Halborg

    2015-01-01

    Recent predictions of absolute binding free energies of host-guest complexes in aqueous solution using electronic structure theory have been encouraging for some systems, while other systems remain problematic. In this paper I summarize some of the many factors that could easily contribute 1-3 kcal......-represented by continuum models. While I focus on binding free energies in aqueous solution the approach also applies (with minor adjustments) to any free energy difference such as conformational or reaction free energy differences or activation free energies in any solvent....

  20. Predictive values of Bi-Rads categories 3, 4 and 5 in non-palpable breast masses evaluated by mammography, ultrasound and magnetic resonance imaging

    International Nuclear Information System (INIS)

    Roveda Junior, Decio; Fleury, Eduardo de Castro Faria; Piato, Sebastiao; Oliveira, Vilmar Marques de; Rinaldi, Jose Francisco; Ferreira, Carlos Alberto Pecci

    2007-01-01

    Objective: To evaluate the predictive value of BI-RADS TM categories 3, 4 and 5 in non-palpable breast masses assessed by mammography, ultrasound and magnetic resonance imaging. Materials And Methods: Twenty-nine patients with BI-RADS categories 3, 4 and 5 non-palpable breast masses identified by mammograms were submitted to complementary ultrasound and magnetic resonance imaging studies, besides excisional biopsy. In total, 30 biopsies were performed. The lesions as well as their respective BI-RADS classification into 3, 4 and 5 were correlated with the histopathological results. The predictive values calculation was made by means of specific mathematical equations. Results: Negative predictive values for category 3 were: mammography, 69.23%; ultrasound, 70.58%; and magnetic resonance imaging, 100%. Positive predictive values for category 4 were: mammography, 63.63%; ultrasound, 50%; and magnetic resonance imaging, 30.76%. For category 5, positive predictive values were: mammography and ultrasound, 100%; and magnetic resonance imaging, 92.85%. Conclusion: For category 3, the negative predictive value of magnetic resonance imaging was high, and for categories 4 and 5, the positive predictive values of the three modalities were moderate. (author)

  1. Predicting tool life in turning operations using neural networks and image processing

    Science.gov (United States)

    Mikołajczyk, T.; Nowicki, K.; Bustillo, A.; Yu Pimenov, D.

    2018-05-01

    A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.

  2. Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction.

    Science.gov (United States)

    Watanabe, Eiji; Kitaoka, Akiyoshi; Sakamoto, Kiwako; Yasugi, Masaki; Tanaka, Kenta

    2018-01-01

    The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

  3. Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data.

    Science.gov (United States)

    Redlich, Ronny; Opel, Nils; Grotegerd, Dominik; Dohm, Katharina; Zaremba, Dario; Bürger, Christian; Münker, Sandra; Mühlmann, Lisa; Wahl, Patricia; Heindel, Walter; Arolt, Volker; Alferink, Judith; Zwanzger, Peter; Zavorotnyy, Maxim; Kugel, Harald; Dannlowski, Udo

    2016-06-01

    Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified. To investigate whether certain factors identified by structural magnetic resonance imaging (MRI) techniques are able to predict ECT response. In this nonrandomized prospective study, gray matter structure was assessed twice at approximately 6 weeks apart using 3-T MRI and voxel-based morphometry. Patients were recruited through the inpatient service of the Department of Psychiatry, University of Muenster, from March 11, 2010, to March 27, 2015. Two patient groups with acute major depressive disorder were included. One group received an ECT series in addition to antidepressants (n = 24); a comparison sample was treated solely with antidepressants (n = 23). Both groups were compared with a sample of healthy control participants (n = 21). Binary pattern classification was used to predict ECT response by structural MRI that was performed before treatment. In addition, univariate analysis was conducted to predict reduction of the Hamilton Depression Rating Scale score by pretreatment gray matter volumes and to investigate ECT-related structural changes. One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years). The binary pattern classification yielded a successful prediction of ECT response, with accuracy rates of 78.3% (18 of 23 patients in the ECT sample) and sensitivity rates of 100% (13 of 13 who responded to ECT). Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score. The principal findings of the univariate model indicated a positive association between pretreatment subgenual cingulate volume and individual ECT response (Montreal Neurological Institute [MNI] coordinates x = 8, y = 21, z = -18

  4. Multispectral Image Analysis for Robust Prediction of Astaxanthin Coating

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Frosch, Stina; Nielsen, Michael Engelbrecht

    2013-01-01

    The aim of this study was to investigate the possibility of predicting the type and concentration level of astaxanthin coating of aquaculture feed pellets using multispectral image analysis. We used both natural and synthetic astaxanthin, and we used several different concentration levels...... of synthetic astaxanthin in combination with four different recipes of feed pellets. We used a VideometerLab with 20 spectral bands in the range of 385-1050 nm. We used linear discriminant analysis and sparse linear discriminant analysis for classification and variable selection. We used partial least squares...

  5. Robust and Accurate Image-Based Georeferencing Exploiting Relative Orientation Constraints

    Science.gov (United States)

    Cavegn, S.; Blaser, S.; Nebiker, S.; Haala, N.

    2018-05-01

    Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2 cm in an indoor area, and 3 cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2-3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.

  6. ROBUST AND ACCURATE IMAGE-BASED GEOREFERENCING EXPLOITING RELATIVE ORIENTATION CONSTRAINTS

    Directory of Open Access Journals (Sweden)

    S. Cavegn

    2018-05-01

    Full Text Available Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2 cm in an indoor area, and 3 cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2–3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.

  7. The interplay of personality and negative comments about appearance in predicting body image.

    Science.gov (United States)

    Kvalem, Ingela Lundin; von Soest, Tilmann; Roald, Helge E; Skolleborg, Knut Chr

    2006-09-01

    This study investigates how personality traits in combination with frequency of and emotional reaction to negative comments about appearance while growing up are related to appearance evaluation and orientation among adult women. Nine hundred and seven participants from a representative sample of Norwegian women aged 22-55, answered questions measuring body image, personality (Big Five), and history of experiencing negative comments about appearance. Results indicated that only emotional reaction to negative comments about appearance significantly predicted both appearance evaluation and orientation, while frequency of negative comments did not. Being extrovert predicted more positive appearance evaluation and being more appearance oriented than being introvert. Scoring high on neuroticism was related to negative appearance evaluation and high appearance orientation. The findings demonstrate the importance of differentiating between the frequency and the emotional impact of teasing as well as including personality traits when studying body image.

  8. A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE).

    Science.gov (United States)

    Stacey, R Greg; Skinnider, Michael A; Scott, Nichollas E; Foster, Leonard J

    2017-10-23

    An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. PrInCE is the first fast and easy

  9. Automated prediction of tissue outcome after acute ischemic stroke in computed tomography perfusion images

    Science.gov (United States)

    Vos, Pieter C.; Bennink, Edwin; de Jong, Hugo; Velthuis, Birgitta K.; Viergever, Max A.; Dankbaar, Jan Willem

    2015-03-01

    Assessment of the extent of cerebral damage on admission in patients with acute ischemic stroke could play an important role in treatment decision making. Computed tomography perfusion (CTP) imaging can be used to determine the extent of damage. However, clinical application is hindered by differences among vendors and used methodology. As a result, threshold based methods and visual assessment of CTP images has not yet shown to be useful in treatment decision making and predicting clinical outcome. Preliminary results in MR studies have shown the benefit of using supervised classifiers for predicting tissue outcome, but this has not been demonstrated for CTP. We present a novel method for the automatic prediction of tissue outcome by combining multi-parametric CTP images into a tissue outcome probability map. A supervised classification scheme was developed to extract absolute and relative perfusion values from processed CTP images that are summarized by a trained classifier into a likelihood of infarction. Training was performed using follow-up CT scans of 20 acute stroke patients with complete recanalization of the vessel that was occluded on admission. Infarcted regions were annotated by expert neuroradiologists. Multiple classifiers were evaluated in a leave-one-patient-out strategy for their discriminating performance using receiver operating characteristic (ROC) statistics. Results showed that a RandomForest classifier performed optimally with an area under the ROC of 0.90 for discriminating infarct tissue. The obtained results are an improvement over existing thresholding methods and are in line with results found in literature where MR perfusion was used.

  10. Evaluation of a deep learning architecture for MR imaging prediction of ATRX in glioma patients

    Science.gov (United States)

    Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J.

    2018-02-01

    Predicting mutation/loss of alpha-thalassemia/mental retardation syndrome X-linked (ATRX) gene utilizing MR imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare a deep neural network approach based on a residual deep neural network (ResNet) architecture and one based on a classical machine learning approach and evaluate their ability in predicting ATRX mutation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture, pre trained on ImageNet data was the best performing model, achieving an accuracy of 0.91 for the test set (classification of a slice as no tumor, ATRX mutated, or mutated) in terms of f1 score in a test set of 35 cases. The SVM classifier achieved 0.63 for differentiating the Flair signal abnormality regions from the test patients based on their mutation status. We report a method that alleviates the need for extensive preprocessing and acts as a proof of concept that deep neural network architectures can be used to predict molecular biomarkers from routine medical images.

  11. Can Measured Synergy Excitations Accurately Construct Unmeasured Muscle Excitations?

    Science.gov (United States)

    Bianco, Nicholas A; Patten, Carolynn; Fregly, Benjamin J

    2018-01-01

    Accurate prediction of muscle and joint contact forces during human movement could improve treatment planning for disorders such as osteoarthritis, stroke, Parkinson's disease, and cerebral palsy. Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called "muscle excitations"), may reduce the redundancy of muscle excitation solutions predicted by optimization methods. This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called "included" muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called "excluded" muscle excitations). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called "synergy extrapolation"). We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. These results suggest that synergy excitations obtained from experimentally measured muscle excitations can accurately construct unmeasured muscle excitations, which could help limit muscle excitations predicted by muscle force optimizations.

  12. Accurate anisotropic material modelling using only tensile tests for hot and cold forming

    Science.gov (United States)

    Abspoel, M.; Scholting, M. E.; Lansbergen, M.; Neelis, B. M.

    2017-09-01

    Accurate material data for simulations require a lot of effort. Advanced yield loci require many different kinds of tests and a Forming Limit Curve (FLC) needs a large amount of samples. Many people use simple material models to reduce the effort of testing, however some models are either not accurate enough (i.e. Hill’48), or do not describe new types of materials (i.e. Keeler). Advanced yield loci describe the anisotropic materials behaviour accurately, but are not widely adopted because of the specialized tests, and data post-processing is a hurdle for many. To overcome these issues, correlations between the advanced yield locus points (biaxial, plane strain and shear) and mechanical properties have been investigated. This resulted in accurate prediction of the advanced stress points using only Rm, Ag and r-values in three directions from which a Vegter yield locus can be constructed with low effort. FLC’s can be predicted with the equations of Abspoel & Scholting depending on total elongation A80, r-value and thickness. Both predictive methods are initially developed for steel, aluminium and stainless steel (BCC and FCC materials). The validity of the predicted Vegter yield locus is investigated with simulation and measurements on both hot and cold formed parts and compared with Hill’48. An adapted specimen geometry, to ensure a homogeneous temperature distribution in the Gleeble hot tensile test, was used to measure the mechanical properties needed to predict a hot Vegter yield locus. Since for hot material, testing of stress states other than uniaxial is really challenging, the prediction for the yield locus adds a lot of value. For the hot FLC an A80 sample with a homogeneous temperature distribution is needed which is due to size limitations not possible in the Gleeble tensile tester. Heating the sample in an industrial type furnace and tensile testing it in a dedicated device is a good alternative to determine the necessary parameters for the FLC

  13. Feature Selection and ANN Solar Power Prediction

    Directory of Open Access Journals (Sweden)

    Daniel O’Leary

    2017-01-01

    Full Text Available A novel method of solar power forecasting for individuals and small businesses is developed in this paper based on machine learning, image processing, and acoustic classification techniques. Increases in the production of solar power at the consumer level require automated forecasting systems to minimize loss, cost, and environmental impact for homes and businesses that produce and consume power (prosumers. These new participants in the energy market, prosumers, require new artificial neural network (ANN performance tuning techniques to create accurate ANN forecasts. Input masking, an ANN tuning technique developed for acoustic signal classification and image edge detection, is applied to prosumer solar data to improve prosumer forecast accuracy over traditional macrogrid ANN performance tuning techniques. ANN inputs tailor time-of-day masking based on error clustering in the time domain. Results show an improvement in prediction to target correlation, the R2 value, lowering inaccuracy of sample predictions by 14.4%, with corresponding drops in mean average error of 5.37% and root mean squared error of 6.83%.

  14. Machine-Learning-Based Future Received Signal Strength Prediction Using Depth Images for mmWave Communications

    OpenAIRE

    Okamoto, Hironao; Nishio, Takayuki; Nakashima, Kota; Koda, Yusuke; Yamamoto, Koji; Morikura, Masahiro; Asai, Yusuke; Miyatake, Ryo

    2018-01-01

    This paper discusses a machine-learning (ML)-based future received signal strength (RSS) prediction scheme using depth camera images for millimeter-wave (mmWave) networks. The scheme provides the future RSS prediction of any mmWave links within the camera's view, including links where nodes are not transmitting frames. This enables network controllers to conduct network operations before line-of-sight path blockages degrade the RSS. Using the ML techniques, the prediction scheme automatically...

  15. Accurate means of detecting and characterizing abnormal patterns of ventricular activation by phase image analysis

    Energy Technology Data Exchange (ETDEWEB)

    Botvinick, E.H.; Frais, M.A.; Shosa, D.W.; O' Connell, J.W.; Pacheco-Alvarez, J.A.; Scheinman, M.; Hattner, R.S.; Morady, F.; Faulkner, D.B.

    1982-08-01

    The ability of scintigraphic phase image analysis to characterize patterns of abnormal ventricular activation was investigated. The pattern of phase distribution and sequential phase changes over both right and left ventricular regions of interest were evaluated in 16 patients with normal electrical activation and wall motion and compared with those in 8 patients with an artificial pacemaker and 4 patients with sinus rhythm with the Wolff-Parkinson-White syndrome and delta waves. Normally, the site of earliest phase angle was seen at the base of the interventricular septum, with sequential change affecting the body of the septum and the cardiac apex and then spreading laterally to involve the body of both ventricles. The site of earliest phase angle was located at the apex of the right ventricle in seven patients with a right ventricular endocardial pacemaker and on the lateral left ventricular wall in one patient with a left ventricular epicardial pacemaker. In each case the site corresponded exactly to the position of the pacing electrode as seen on posteroanterior and left lateral chest X-ray films, and sequential phase changes spread from the initial focus to affect both ventricles. In each of the patients with the Wolff-Parkinson-White syndrome, the site of earliest ventricular phase angle was located, and it corresponded exactly to the site of the bypass tract as determined by endocardial mapping. In this way, four bypass pathways, two posterior left paraseptal, one left lateral and one right lateral, were correctly localized scintigraphically. On the basis of the sequence of mechanical contraction, phase image analysis provides an accurate noninvasive method of detecting abnormal foci of ventricular activation.

  16. Accurate means of detecting and characterizing abnormal patterns of ventricular activation by phase image analysis

    International Nuclear Information System (INIS)

    Botvinick, E.H.; Frais, M.A.; Shosa, D.W.; O'Connell, J.W.; Pacheco-Alvarez, J.A.; Scheinman, M.; Hattner, R.S.; Morady, F.; Faulkner, D.B.

    1982-01-01

    The ability of scintigraphic phase image analysis to characterize patterns of abnormal ventricular activation was investigated. The pattern of phase distribution and sequential phase changes over both right and left ventricular regions of interest were evaluated in 16 patients with normal electrical activation and wall motion and compared with those in 8 patients with an artificial pacemaker and 4 patients with sinus rhythm with the Wolff-Parkinson-White syndrome and delta waves. Normally, the site of earliest phase angle was seen at the base of the interventricular septum, with sequential change affecting the body of the septum and the cardiac apex and then spreading laterally to involve the body of both ventricles. The site of earliest phase angle was located at the apex of the right ventricle in seven patients with a right ventricular endocardial pacemaker and on the lateral left ventricular wall in one patient with a left ventricular epicardial pacemaker. In each case the site corresponded exactly to the position of the pacing electrode as seen on posteroanterior and left lateral chest X-ray films, and sequential phase changes spread from the initial focus to affect both ventricles. In each of the patients with the Wolff-Parkinson-White syndrome, the site of earliest ventricular phase angle was located, and it corresponded exactly to the site of the bypass tract as determined by endocardial mapping. In this way, four bypass pathways, two posterior left paraseptal, one left lateral and one right lateral, were correctly localized scintigraphically. On the basis of the sequence of mechanical contraction, phase image analysis provides an accurate noninvasive method of detecting abnormal foci of ventricular activation

  17. Can We Predict Functional Outcome in Neonates with Hypoxic Ischemic Encephalopathy by the Combination of Neuroimaging and Electroencephalography?

    Science.gov (United States)

    Nanavati, Tania; Seemaladinne, Nirupama; Regier, Michael; Yossuck, Panitan; Pergami, Paola

    2015-01-01

    Background Neonatal hypoxic ischemic encephalopathy (HIE) is a major cause of mortality, morbidity, and long-term neurological deficits. Despite the availability of neuroimaging and neurophysiological testing, tools for accurate early diagnosis and prediction of developmental outcome are still lacking. The goal of this study was to determine if combined use of magnetic resonance imaging (MRI) and electroencephalography (EEG) findings could support outcome prediction. Methods We retrospectively reviewed records of 17 HIE neonates, classified brain MRI and EEG findings based on severity, and assessed clinical outcome up to 48 months. We determined the relation between MRI/EEG findings and clinical outcome. Results We demonstrated a significant relationship between MRI findings and clinical outcome (Fisher’s exact test, p = 0.017). EEG provided no additional information about the outcome beyond that contained in the MRI score. The statistical model for outcome prediction based on random forests suggested that EEG readings at 24 hours and 72 hours could be important variables for outcome prediction, but this needs to be investigated further. Conclusion Caution should be used when discussing prognosis for neonates with mild-to-moderate HIE based on early MR imaging and EEG findings. A robust, quantitative marker of HIE severity that allows for accurate prediction of long-term outcome, particularly for mild-to-moderate cases, is still needed. PMID:25862075

  18. Imaging brain tumour microstructure.

    Science.gov (United States)

    Nilsson, Markus; Englund, Elisabet; Szczepankiewicz, Filip; van Westen, Danielle; Sundgren, Pia C

    2018-05-08

    Imaging is an indispensable tool for brain tumour diagnosis, surgical planning, and follow-up. Definite diagnosis, however, often demands histopathological analysis of microscopic features of tissue samples, which have to be obtained by invasive means. A non-invasive alternative may be to probe corresponding microscopic tissue characteristics by MRI, or so called 'microstructure imaging'. The promise of microstructure imaging is one of 'virtual biopsy' with the goal to offset the need for invasive procedures in favour of imaging that can guide pre-surgical planning and can be repeated longitudinally to monitor and predict treatment response. The exploration of such methods is motivated by the striking link between parameters from MRI and tumour histology, for example the correlation between the apparent diffusion coefficient and cellularity. Recent microstructure imaging techniques probe even more subtle and specific features, providing parameters associated to cell shape, size, permeability, and volume distributions. However, the range of scenarios in which these techniques provide reliable imaging biomarkers that can be used to test medical hypotheses or support clinical decisions is yet unknown. Accurate microstructure imaging may moreover require acquisitions that go beyond conventional data acquisition strategies. This review covers a wide range of candidate microstructure imaging methods based on diffusion MRI and relaxometry, and explores advantages, challenges, and potential pitfalls in brain tumour microstructure imaging. Copyright © 2018. Published by Elsevier Inc.

  19. Interferometer predictions with triangulated images

    DEFF Research Database (Denmark)

    Brinch, Christian; Dullemond, C. P.

    2014-01-01

    the synthetic model images. To get the correct values of these integrals, the model images must have the right size and resolution. Insufficient care in these choices can lead to wrong results. We present a new general-purpose scheme for the computation of visibilities of radiative transfer images. Our method...... requires a model image that is a list of intensities at arbitrarily placed positions on the image-plane. It creates a triangulated grid from these vertices, and assumes that the intensity inside each triangle of the grid is a linear function. The Fourier integral over each triangle is then evaluated...... with an analytic expression and the complex visibility of the entire image is then the sum of all triangles. The result is a robust Fourier transform that does not suffer from aliasing effects due to grid regularities. The method automatically ensures that all structure contained in the model gets reflected...

  20. Radiomic biomarkers from PET/CT multi-modality fusion images for the prediction of immunotherapy response in advanced non-small cell lung cancer patients

    Science.gov (United States)

    Mu, Wei; Qi, Jin; Lu, Hong; Schabath, Matthew; Balagurunathan, Yoganand; Tunali, Ilke; Gillies, Robert James

    2018-02-01

    Purpose: Investigate the ability of using complementary information provided by the fusion of PET/CT images to predict immunotherapy response in non-small cell lung cancer (NSCLC) patients. Materials and methods: We collected 64 patients diagnosed with primary NSCLC treated with anti PD-1 checkpoint blockade. Using PET/CT images, fused images were created following multiple methodologies, resulting in up to 7 different images for the tumor region. Quantitative image features were extracted from the primary image (PET/CT) and the fused images, which included 195 from primary images and 1235 features from the fusion images. Three clinical characteristics were also analyzed. We then used support vector machine (SVM) classification models to identify discriminant features that predict immunotherapy response at baseline. Results: A SVM built with 87 fusion features and 13 primary PET/CT features on validation dataset had an accuracy and area under the ROC curve (AUROC) of 87.5% and 0.82, respectively, compared to a model built with 113 original PET/CT features on validation dataset 78.12% and 0.68. Conclusion: The fusion features shows better ability to predict immunotherapy response prediction compared to individual image features.

  1. Enhancement of a Turbulence Sub-Model for More Accurate Predictions of Vertical Stratifications in 3D Coastal and Estuarine Modeling

    Directory of Open Access Journals (Sweden)

    Wenrui Huang

    2010-03-01

    Full Text Available This paper presents an improvement of the Mellor and Yamada's 2nd order turbulence model in the Princeton Ocean Model (POM for better predictions of vertical stratifications of salinity in estuaries. The model was evaluated in the strongly stratified estuary, Apalachicola River, Florida, USA. The three-dimensional hydrodynamic model was applied to study the stratified flow and salinity intrusion in the estuary in response to tide, wind, and buoyancy forces. Model tests indicate that model predictions over estimate the stratification when using the default turbulent parameters. Analytic studies of density-induced and wind-induced flows indicate that accurate estimation of vertical eddy viscosity plays an important role in describing vertical profiles. Initial model revision experiments show that the traditional approach of modifying empirical constants in the turbulence model leads to numerical instability. In order to improve the performance of the turbulence model while maintaining numerical stability, a stratification factor was introduced to allow adjustment of the vertical turbulent eddy viscosity and diffusivity. Sensitivity studies indicate that the stratification factor, ranging from 1.0 to 1.2, does not cause numerical instability in Apalachicola River. Model simulations show that increasing the turbulent eddy viscosity by a stratification factor of 1.12 results in an optimal agreement between model predictions and observations in the case study presented in this study. Using the proposed stratification factor provides a useful way for coastal modelers to improve the turbulence model performance in predicting vertical turbulent mixing in stratified estuaries and coastal waters.

  2. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  3. Studying depression using imaging and machine learning methods

    Directory of Open Access Journals (Sweden)

    Meenal J. Patel

    2016-01-01

    Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.

  4. Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer.

    Science.gov (United States)

    Ghandehari, Masoud; Emig, Thorsten; Aghamohamadnia, Milad

    2018-02-02

    Despite decades of research seeking to derive the urban energy budget, the dynamics of thermal exchange in the densely constructed environment is not yet well understood. Using New York City as a study site, we present a novel hybrid experimental-computational approach for a better understanding of the radiative heat transfer in complex urban environments. The aim of this work is to contribute to the calculation of the urban energy budget, particularly the stored energy. We will focus our attention on surface thermal radiation. Improved understanding of urban thermodynamics incorporating the interaction of various bodies, particularly in high rise cities, will have implications on energy conservation at the building scale, and for human health and comfort at the urban scale. The platform presented is based on longwave hyperspectral imaging of nearly 100 blocks of Manhattan, in addition to a geospatial radiosity model that describes the collective radiative heat exchange between multiple buildings. Despite assumptions in surface emissivity and thermal conductivity of buildings walls, the close comparison of temperatures derived from measurements and computations is promising. Results imply that the presented geospatial thermodynamic model of urban structures can enable accurate and high resolution analysis of instantaneous urban surface temperatures.

  5. Nonpolitical images evoke neural predictors of political ideology.

    Science.gov (United States)

    Ahn, Woo-Young; Kishida, Kenneth T; Gu, Xiaosi; Lohrenz, Terry; Harvey, Ann; Alford, John R; Smith, Kevin B; Yaffe, Gideon; Hibbing, John R; Dayan, Peter; Montague, P Read

    2014-11-17

    Political ideologies summarize dimensions of life that define how a person organizes their public and private behavior, including their attitudes associated with sex, family, education, and personal autonomy. Despite the abstract nature of such sensibilities, fundamental features of political ideology have been found to be deeply connected to basic biological mechanisms that may serve to defend against environmental challenges like contamination and physical threat. These results invite the provocative claim that neural responses to nonpolitical stimuli (like contaminated food or physical threats) should be highly predictive of abstract political opinions (like attitudes toward gun control and abortion). We applied a machine-learning method to fMRI data to test the hypotheses that brain responses to emotionally evocative images predict individual scores on a standard political ideology assay. Disgusting images, especially those related to animal-reminder disgust (e.g., mutilated body), generate neural responses that are highly predictive of political orientation even though these neural predictors do not agree with participants' conscious rating of the stimuli. Images from other affective categories do not support such predictions. Remarkably, brain responses to a single disgusting stimulus were sufficient to make accurate predictions about an individual subject's political ideology. These results provide strong support for the idea that fundamental neural processing differences that emerge under the challenge of emotionally evocative stimuli may serve to structure political beliefs in ways formerly unappreciated. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  6. Predictive modeling for corrective maintenance of imaging devices from machine logs.

    Science.gov (United States)

    Patil, Ravindra B; Patil, Meru A; Ravi, Vidya; Naik, Sarif

    2017-07-01

    In the cost sensitive healthcare industry, an unplanned downtime of diagnostic and therapy imaging devices can be a burden on the financials of both the hospitals as well as the original equipment manufacturers (OEMs). In the current era of connectivity, it is easier to get these devices connected to a standard monitoring station. Once the system is connected, OEMs can monitor the health of these devices remotely and take corrective actions by providing preventive maintenance thereby avoiding major unplanned downtime. In this article, we present an overall methodology of predicting failure of these devices well before customer experiences it. We use data-driven approach based on machine learning to predict failures in turn resulting in reduced machine downtime, improved customer satisfaction and cost savings for the OEMs. One of the use-case of predicting component failure of PHILIPS iXR system is explained in this article.

  7. FDG PET imaging for grading and prediction of outcome in chondrosarcoma patients

    Energy Technology Data Exchange (ETDEWEB)

    Brenner, Winfried; Eary, Janet F. [Division of Nuclear Medicine, University of Washington Medical Center, 1959 NE Pacific Street, Box 356113, WA 98195-6113, Seattle (United States); Conrad, Ernest U. [Department of Orthopaedics, University of Washington Medical Center, Seattle, WA (United States)

    2004-02-01

    The aims of this study were to assess the potential of fluorine-18 fluorodeoxyglucose positron emission tomography (FDG PET) for tumor grading in chondrosarcoma patients and to evaluate the role of standardized uptake value (SUV) as a parameter for prediction of patient outcome. FDG PET imaging was performed in 31 patients with chondrosarcoma prior to therapy. SUV was calculated for each tumor and correlated to tumor grade and size, and to patient outcome in terms of local relapse or metastatic disease with a mean follow-up period of 48 months. Chondrosarcomas were detectable in all patients. Tumor SUV was 3.38{+-}1.61 for grade I (n=15), 5.44{+-}3.06 for grade II (n=13), and 7.10{+-}2.61 for grade III (n=3). Significant differences were found between patients with and without disease progression: SUV was 6.42{+-}2.70 (n=10) in patients developing recurrent or metastatic disease compared with 3.74{+-}2.22 in patients without relapse (P=0.015). Using a cut-off of 4 for SUV, sensitivity, specificity, and positive and negative predictive values for a relapse were 90%, 76%, 64%, and 94%, respectively. Combining tumor grade and SUV, these parameters improved to 90%, 95%, 90%, and 95%, respectively. Pretherapeutic tumor SUV obtained by FDG PET imaging was a useful parameter for tumor grading and prediction of outcome in chondrosarcoma patients. The combination of SUV and histopathologic tumor grade further improved prediction of outcome substantially, allowing identification of patients at high risk for local relapse or metastatic disease. (orig.)

  8. Accurate late gadolinium enhancement prediction by early T1- based quantitative synthetic mapping

    Energy Technology Data Exchange (ETDEWEB)

    Dijk, Randy van; Harst, Pim van der [University of Groningen, University Medical Centre Groningen, Centre for Medical Imaging, Groningen (Netherlands); University of Groningen, University Medical Centre Groningen, Department of Cardiology, Groningen (Netherlands); Kuijpers, Dirkjan [University of Groningen, University Medical Centre Groningen, Centre for Medical Imaging, Groningen (Netherlands); Department of Cardiovascular Imaging HMC-Bronovo, The Hague (Netherlands); Kaandorp, Theodorus A.M.; Dijkman, Paul R.M. van [Department of Cardiovascular Imaging HMC-Bronovo, The Hague (Netherlands); Vliegenthart, Rozemarijn [University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen (Netherlands); Oudkerk, Matthijs [University of Groningen, University Medical Centre Groningen, Centre for Medical Imaging, Groningen (Netherlands); University Medical Center Groningen, Center for Medical Imaging, Groningen (Netherlands)

    2018-02-15

    Early synthetic gadolinium enhancement (ESGE) imaging from post-contrast T1 mapping after adenosine stress-perfusion cardiac magnetic resonance (CMR) was compared to conventional late gadolinium enhancement (LGE) imaging for assessing myocardial scar. Two hundred fourteen consecutive patients suspected of myocardial ischaemia were referred for stress-perfusion CMR. Myocardial infarct volume was quantified on a per-subsegment basis in both synthetic (2-3 min post-gadolinium) and conventional (9 min post-gadolinium) images by two independent observers. Sensitivity, specificity, PPV and NPV were calculated on a per-patient and per-subsegment basis. Both techniques detected 39 gadolinium enhancement areas in 23 patients. The median amount of scar was 2.0 (1.0-3.1) g in ESGE imaging and 2.2 (1.1-3.1) g in LGE imaging (p=0.39). Excellent correlation (r=0.997) and agreement (mean absolute difference: -0.028±0.289 ml) were found between ESGE and LGE images. Sensitivity, specificity, PPV and NPV of ESGE imaging were 96 (78.9-99.9), 99 (97.1-100.0)%, 96 (76.5-99.4) and 99.5 (96.6-99.9) in patient-based and 99 (94.5-100.0), 100 (99.9-100.0)%, 97.0 (91.3-99.0) and 100.0 (99.8-100.0) in subsegment-based analysis. ESGE based on post-contrast T1 mapping after adenosine stress-perfusion CMR imaging shows excellent agreement with conventional LGE imaging for assessing myocardial scar, and can substantially shorten clinical acquisition time. (orig.)

  9. Accurate late gadolinium enhancement prediction by early T1- based quantitative synthetic mapping

    International Nuclear Information System (INIS)

    Dijk, Randy van; Harst, Pim van der; Kuijpers, Dirkjan; Kaandorp, Theodorus A.M.; Dijkman, Paul R.M. van; Vliegenthart, Rozemarijn; Oudkerk, Matthijs

    2018-01-01

    Early synthetic gadolinium enhancement (ESGE) imaging from post-contrast T1 mapping after adenosine stress-perfusion cardiac magnetic resonance (CMR) was compared to conventional late gadolinium enhancement (LGE) imaging for assessing myocardial scar. Two hundred fourteen consecutive patients suspected of myocardial ischaemia were referred for stress-perfusion CMR. Myocardial infarct volume was quantified on a per-subsegment basis in both synthetic (2-3 min post-gadolinium) and conventional (9 min post-gadolinium) images by two independent observers. Sensitivity, specificity, PPV and NPV were calculated on a per-patient and per-subsegment basis. Both techniques detected 39 gadolinium enhancement areas in 23 patients. The median amount of scar was 2.0 (1.0-3.1) g in ESGE imaging and 2.2 (1.1-3.1) g in LGE imaging (p=0.39). Excellent correlation (r=0.997) and agreement (mean absolute difference: -0.028±0.289 ml) were found between ESGE and LGE images. Sensitivity, specificity, PPV and NPV of ESGE imaging were 96 (78.9-99.9), 99 (97.1-100.0)%, 96 (76.5-99.4) and 99.5 (96.6-99.9) in patient-based and 99 (94.5-100.0), 100 (99.9-100.0)%, 97.0 (91.3-99.0) and 100.0 (99.8-100.0) in subsegment-based analysis. ESGE based on post-contrast T1 mapping after adenosine stress-perfusion CMR imaging shows excellent agreement with conventional LGE imaging for assessing myocardial scar, and can substantially shorten clinical acquisition time. (orig.)

  10. An accurate segmentation method for volumetry of brain tumor in 3D MRI

    Science.gov (United States)

    Wang, Jiahui; Li, Qiang; Hirai, Toshinori; Katsuragawa, Shigehiko; Li, Feng; Doi, Kunio

    2008-03-01

    Accurate volumetry of brain tumors in magnetic resonance imaging (MRI) is important for evaluating the interval changes in tumor volumes during and after treatment, and also for planning of radiation therapy. In this study, an automated volumetry method for brain tumors in MRI was developed by use of a new three-dimensional (3-D) image segmentation technique. First, the central location of a tumor was identified by a radiologist, and then a volume of interest (VOI) was determined automatically. To substantially simplify tumor segmentation, we transformed the 3-D image of the tumor into a two-dimensional (2-D) image by use of a "spiral-scanning" technique, in which a radial line originating from the center of the tumor scanned the 3-D image spirally from the "north pole" to the "south pole". The voxels scanned by the radial line provided a transformed 2-D image. We employed dynamic programming to delineate an "optimal" outline of the tumor in the transformed 2-D image. We then transformed the optimal outline back into 3-D image space to determine the volume of the tumor. The volumetry method was trained and evaluated by use of 16 cases with 35 brain tumors. The agreement between tumor volumes provided by computer and a radiologist was employed as a performance metric. Our method provided relatively accurate results with a mean agreement value of 88%.

  11. Phase rainbow refractometry for accurate droplet variation characterization.

    Science.gov (United States)

    Wu, Yingchun; Promvongsa, Jantarat; Saengkaew, Sawitree; Wu, Xuecheng; Chen, Jia; Gréhan, Gérard

    2016-10-15

    We developed a one-dimensional phase rainbow refractometer for the accurate trans-dimensional measurements of droplet size on the micrometer scale as well as the tiny droplet diameter variations at the nanoscale. The dependence of the phase shift of the rainbow ripple structures on the droplet variations is revealed. The phase-shifting rainbow image is recorded by a telecentric one-dimensional rainbow imaging system. Experiments on the evaporating monodispersed droplet stream show that the phase rainbow refractometer can measure the tiny droplet diameter changes down to tens of nanometers. This one-dimensional phase rainbow refractometer is capable of measuring the droplet refractive index and diameter, as well as variations.

  12. Generating Facial Expressions Using an Anatomically Accurate Biomechanical Model.

    Science.gov (United States)

    Wu, Tim; Hung, Alice; Mithraratne, Kumar

    2014-11-01

    This paper presents a computational framework for modelling the biomechanics of human facial expressions. A detailed high-order (Cubic-Hermite) finite element model of the human head was constructed using anatomical data segmented from magnetic resonance images. The model includes a superficial soft-tissue continuum consisting of skin, the subcutaneous layer and the superficial Musculo-Aponeurotic system. Embedded within this continuum mesh, are 20 pairs of facial muscles which drive facial expressions. These muscles were treated as transversely-isotropic and their anatomical geometries and fibre orientations were accurately depicted. In order to capture the relative composition of muscles and fat, material heterogeneity was also introduced into the model. Complex contact interactions between the lips, eyelids, and between superficial soft tissue continuum and deep rigid skeletal bones were also computed. In addition, this paper investigates the impact of incorporating material heterogeneity and contact interactions, which are often neglected in similar studies. Four facial expressions were simulated using the developed model and the results were compared with surface data obtained from a 3D structured-light scanner. Predicted expressions showed good agreement with the experimental data.

  13. Predicting Visible Image Degradation by Colour Image Difference Formulae

    Institute of Scientific and Technical Information of China (English)

    Eriko Bando; Jon Y. Hardeberg; David Connah; Ivar Farup

    2004-01-01

    It carried out a CRT monitor based psychophysical experiment to investigate the quality of three colour image difference metrics, the CIEAE ab equation, the iCAM and the S-CIELAB metrics. Six original images were reproduced through six gamut mapping algorithms for the observer experiment. The result indicates that the colour image difference calculated by each metric does not directly relate to perceived image difference.

  14. Computerized method for evaluating diagnostic image quality of calcified plaque images in cardiac CT: Validation on a physical dynamic cardiac phantom

    Energy Technology Data Exchange (ETDEWEB)

    King, Martin; Rodgers, Zachary; Giger, Maryellen L.; Bardo, Dianna M. E.; Patel, Amit R. [Department of Radiology, Committee on Medical Physics, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637 (United States); Department of Diagnostic Radiology, Oregon Health and Science University, 3181 Southwest Sam Jackson Park Road, Portland, Oregon 97239 (United States); Department of Medicine, University of Chicago, 5841 South Maryland Avenue, MC 5084, Chicago, Illinois 60637 (United States)

    2010-11-15

    Purpose: In cardiac computed tomography (CT), important clinical indices, such as the coronary calcium score and the percentage of coronary artery stenosis, are often adversely affected by motion artifacts. As a result, the expert observer must decide whether or not to use these indices during image interpretation. Computerized methods potentially can be used to assist in these decisions. In a previous study, an artificial neural network (ANN) regression model provided assessability (image quality) indices of calcified plaque images from the software NCAT phantom that were highly agreeable with those provided by expert observers. The method predicted assessability indices based on computer-extracted features of the plaque. In the current study, the ANN-predicted assessability indices were used to identify calcified plaque images with diagnostic calcium scores (based on mass) from a physical dynamic cardiac phantom. The basic assumption was that better quality images were associated with more accurate calcium scores. Methods: A 64-channel CT scanner was used to obtain 500 calcified plaque images from a physical dynamic cardiac phantom at different heart rates, cardiac phases, and plaque locations. Two expert observers independently provided separate sets of assessability indices for each of these images. Separate sets of ANN-predicted assessability indices tailored to each observer were then generated within the framework of a bootstrap resampling scheme. For each resampling iteration, the absolute calcium score error between the calcium scores of the motion-contaminated plaque image and its corresponding stationary image served as the ground truth in terms of indicating images with diagnostic calcium scores. The performances of the ANN-predicted and observer-assigned indices in identifying images with diagnostic calcium scores were then evaluated using ROC analysis. Results: Assessability indices provided by the first observer and the corresponding ANN performed

  15. Using destination image to predict visitors' intention to revisit three Hudson River Valley, New York, communities

    Science.gov (United States)

    Rudy M. Schuster; Laura Sullivan; Duarte Morais; Diane Kuehn

    2009-01-01

    This analysis explores the differences in Affective and Cognitive Destination Image among three Hudson River Valley (New York) tourism communities. Multiple regressions were used with six dimensions of visitors' images to predict future intention to revisit. Two of the three regression models were significant. The only significantly contributing independent...

  16. Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.

    Science.gov (United States)

    Yang, Yu Xin; Chong, Mei Sian; Tay, Laura; Yew, Suzanne; Yeo, Audrey; Tan, Cher Heng

    2016-10-01

    To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy. The automatic detection of body composition is formulized as a three-class classification issue. Each image voxel in the training dataset is assigned with a correct label. A voxel classifier is trained and subsequently used to predict unseen data. Morphological operations are finally applied to generate volumetric segmented images for different structures. We applied this algorithm on datasets of (1) four contrast images, (2) water and fat images, and (3) unsuppressed images acquired from 190 subjects. The proposed method using four contrasts achieved most accurate and robust segmentation compared to the use of combined fat and water images and the use of unsuppressed image, average Dice coefficients of 0.94 ± 0.03, 0.96 ± 0.03, 0.80 ± 0.03, and 0.97 ± 0.01 has been achieved to bone region, subcutaneous adipose tissue (SAT), inter-muscular adipose tissue (IMAT), and muscle respectively. Our proposed method based on machine learning produces accurate tissue quantification and showed an effective use of large information provided by the four contrast images from Dixon MRI.

  17. Predicting postnatal renal function of prenatally detected posterior urethral valves using fetal diffusion-weighted magnetic resonance imaging with apparent diffusion coefficient determination.

    Science.gov (United States)

    Faure, Alice; Panait, Nicoleta; Panuel, Michel; Alessandrini, Pierre; D'Ercole, Claude; Chaumoitre, Kathia; Merrot, Thierry

    2017-07-01

    The objective of this study was to evaluate the accuracy of fetal diffusion-weighted magnetic resonance imaging with apparent diffusion coefficient (ADC) determination to predict postnatal renal function (nadir creatinine at 1 year and eGFR) of men with posterior urethral valves (PUV). Between 2003 and 2014, 11 MRI were performed on fetuses (between 28 and 32 weeks) in whom second trimester sonography suggested severe bilateral urinary tract anomalies, suspected of PUV. The ADC of the 11 fetuses ranged from 1.3 to 2.86 mm 2  s -1 (median = 1.79 mm 2  s -1 , normal range for fetal kidney: 1.1-1.8). Two pregnancies with ADC > 2.6 mm 2  s -1 were interrupted; the autopsy confirmed PUV and Potter syndrome. For the remaining nine babies, the follow-up was 5.4 years (0.8-10). Four children with abnormal ADC (1.8-2.3) had chronic kidney disease. The remaining five cases with normal nadir creatinine and eGFR had normal ADC. One case with unilateral elevated ADC had a poor ipsilateral renal function on dimercaptosuccinic acid scan. Here, it seems that diffusion-weighted magnetic resonance imaging with ADC determination could be useful in accurately evaluating fetal kidneys in PUV and predicting renal function. It may be an additional, non-invasive method when biologic and sonographic findings are inconclusive, especially in the case of oligohydramnios. Further studies are needed to confirm our data. © 2017 John Wiley & Sons, Ltd. © 2017 John Wiley & Sons, Ltd.

  18. Transfer function analysis of radiographic imaging systems

    International Nuclear Information System (INIS)

    Metz, C.E.; Doi, K.

    1979-01-01

    The theoretical and experimental aspects of the techniques of transfer function analysis used in radiographic imaging systems are reviewed. The mathematical principles of transfer function analysis are developed for linear, shift-invariant imaging systems, for the relation between object and image and for the image due to a sinusoidal plane wave object. The other basic mathematical principle discussed is 'Fourier analysis' and its application to an input function. Other aspects of transfer function analysis included are alternative expressions for the 'optical transfer function' of imaging systems and expressions are derived for both serial and parallel transfer image sub-systems. The applications of transfer function analysis to radiographic imaging systems are discussed in relation to the linearisation of the radiographic imaging system, the object, the geometrical unsharpness, the screen-film system unsharpness, other unsharpness effects and finally noise analysis. It is concluded that extensive theoretical, computer simulation and experimental studies have demonstrated that the techniques of transfer function analysis provide an accurate and reliable means for predicting and understanding the effects of various radiographic imaging system components in most practical diagnostic medical imaging situations. (U.K.)

  19. Imaging in Patients with Merkel Cell Carcinoma

    International Nuclear Information System (INIS)

    Enzenhofer, E.; Ubl, P.; Czerny, C.; Erovic, B. M.

    2013-01-01

    Merkel cell carcinoma (MCC) is a rare, aggressive neuroendocrine tumor of the skin with a mortality rate of approximately 25% (Peloschek et al., 2010). Accurate assessment of nodal involvement in patients with MCC predicts significantly overall outcome (Smith et al., 2012 and Ortin-Perez et al., 2007). Due to the rarity of this highly aggressive disease, only a few imaging reports on MCC were published, and subsequently still to date no accepted imaging algorithm for MCC is available. For primary staging of MCC, general recommendations have included ultrasonography, chest X-ray CT, and MRI, but recent articles show that the use of sentinel node and FDG-PET/PET-CT is gaining more and more importance

  20. An accurate test for acute appendicitis: In-111 WBC imaging

    International Nuclear Information System (INIS)

    Navarro, D.A.; Weber, P.M.; Kang, I.Y.; dosRemedios, L.V.; Jasko, I.A.

    1985-01-01

    The decision to operate when acute appendicitis (APPY) is suspected is often difficult. Surgeons accept up to a 20% false positive rate to avoid any delay that may result in appendiceal rupture and peritonitis. The authors have successfully improved early diagnostic accuracy by using abdominal imaging beginning 2 hours after injecting In-111 labeled WBC. Patients with clear-cut (APPY) had laparotomy and were not studied. Those who were to be observed in the ER for possible (APPY) had their leukocytes harvested, labeled with In-111 oxine, and reinjected. Abnormal localized activity in the right lower quadrant (RLQ) imaged at 2 hours was graded relative to bone marrow activity (8M): 0, 1+ BM. When available the surgical specimen was imaged for In-111 activity. Of 31 patients studied there were 13 with positive scans for (APPY) all surgically confirmed. There were 4 additional abnormal studies all demonstrating known diagnostic patterns, 2 of pertonitis and 2 of colitis. There were 14 negative studies in 8 of whom the clinical course was benign; the remaining 6 had laparotomy with 3 having (APPY) and 3 not. Thus there were no false positives and 3 false negatives. One case negative at 2 hours had appendiceal activity later. The 3 cases with 3+ activity all had apendiceal abscesses. This new application of In-111 oxine WBC imaging is safe, simple, sensitive and specific. It shortens the time to surgical intervention and should reduce the surgical false positive rate

  1. Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models.

    Science.gov (United States)

    Yock, Adam D; Rao, Arvind; Dong, Lei; Beadle, Beth M; Garden, Adam S; Kudchadker, Rajat J; Court, Laurence E

    2014-05-01

    The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: -11.6%-23.8%) and 14.6% (range: -7.3%-27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: -6.8%-40.3%) and 13.1% (range: -1.5%-52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: -11.1%-20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.

  2. Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models

    International Nuclear Information System (INIS)

    Yock, Adam D.; Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.

    2014-01-01

    Purpose: The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Methods: Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. Results: In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: −11.6%–23.8%) and 14.6% (range: −7.3%–27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: −6.8%–40.3%) and 13.1% (range: −1.5%–52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: −11.1%–20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. Conclusions: A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography

  3. 111In platelet imaging of left ventricular thrombi. Predictive value for systemic emboli

    International Nuclear Information System (INIS)

    Stratton, J.R.; Ritchie, J.L.

    1990-01-01

    To determine whether a positive indium 111 platelet image for a left ventricular thrombus, which indicates ongoing thrombogenic activity, predicts an increased risk of systemic embolization, we compared the embolic rate in 34 patients with positive 111 In platelet images with that in 69 patients with negative images during a mean follow-up of 38 +/- 31 (+/- SD) months after platelet imaging. The positive and negative image groups were similar with respect to age (59 +/- 11 vs. 62 +/- 10 years), prevalence of previous infarction (94% vs. 78%, p less than 0.05), time from last infarction (28 +/- 51 vs. 33 +/- 47 months), ejection fraction (29 +/- 14 vs. 33 +/- 14), long-term or paroxysmal atrial fibrillation (15% vs. 26%), warfarin therapy during follow-up (26% vs. 20%), platelet-inhibitory therapy during follow-up (50% vs. 33%), injected 111 In dose (330 +/- 92 vs. 344 +/- 118 microCi), and latest imaging time (greater than or equal to 48 hours in all patients). During follow-up, embolic events occurred in 21% (seven of 34) of patients with positive platelet images for left ventricular thrombi as compared with 3% (two of 69) of patients with negative images (p = 0.002). By actuarial methods, at 42 months after platelet imaging, only 86% of patients with positive images were embolus free as compared with 98% of patients with negative images (p less than 0.01)

  4. Uncertainty quantification of cinematic imaging for development of predictive simulations of turbulent combustion.

    Energy Technology Data Exchange (ETDEWEB)

    Lawson, Matthew; Debusschere, Bert J.; Najm, Habib N.; Sargsyan, Khachik; Frank, Jonathan H.

    2010-09-01

    Recent advances in high frame rate complementary metal-oxide-semiconductor (CMOS) cameras coupled with high repetition rate lasers have enabled laser-based imaging measurements of the temporal evolution of turbulent reacting flows. This measurement capability provides new opportunities for understanding the dynamics of turbulence-chemistry interactions, which is necessary for developing predictive simulations of turbulent combustion. However, quantitative imaging measurements using high frame rate CMOS cameras require careful characterization of the their noise, non-linear response, and variations in this response from pixel to pixel. We develop a noise model and calibration tools to mitigate these problems and to enable quantitative use of CMOS cameras. We have demonstrated proof of principle for image de-noising using both wavelet methods and Bayesian inference. The results offer new approaches for quantitative interpretation of imaging measurements from noisy data acquired with non-linear detectors. These approaches are potentially useful in many areas of scientific research that rely on quantitative imaging measurements.

  5. Utility of spatial frequency domain imaging (SFDI) and laser speckle imaging (LSI) to non-invasively diagnose burn depth in a porcine model☆

    Science.gov (United States)

    Burmeister, David M.; Ponticorvo, Adrien; Yang, Bruce; Becerra, Sandra C.; Choi, Bernard; Durkin, Anthony J.; Christy, Robert J.

    2015-01-01

    Surgical intervention of second degree burns is often delayed because of the difficulty in visual diagnosis, which increases the risk of scarring and infection. Non-invasive metrics have shown promise in accurately assessing burn depth. Here, we examine the use of spatial frequency domain imaging (SFDI) and laser speckle imaging (LSI) for predicting burn depth. Contact burn wounds of increasing severity were created on the dorsum of a Yorkshire pig, and wounds were imaged with SFDI/LSI starting immediately after-burn and then daily for the next 4 days. In addition, on each day the burn wounds were biopsied for histological analysis of burn depth, defined by collagen coagulation, apoptosis, and adnexal/vascular necrosis. Histological results show that collagen coagulation progressed from day 0 to day 1, and then stabilized. Results of burn wound imaging using non-invasive techniques were able to produce metrics that correlate to different predictors of burn depth. Collagen coagulation and apoptosis correlated with SFDI scattering coefficient parameter ( μs′) and adnexal/vascular necrosis on the day of burn correlated with blood flow determined by LSI. Therefore, incorporation of SFDI scattering coefficient and blood flow determined by LSI may provide an algorithm for accurate assessment of the severity of burn wounds in real time. PMID:26138371

  6. Prospective and retrospective ECG-gating for CT coronary angiography perform similarly accurate at low heart rates

    Energy Technology Data Exchange (ETDEWEB)

    Stolzmann, Paul, E-mail: paul.stolzmann@usz.ch [Institute of Diagnostic Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich (Switzerland); Goetti, Robert; Baumueller, Stephan [Institute of Diagnostic Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich (Switzerland); Plass, Andre; Falk, Volkmar [Clinic for Cardiovascular Surgery, University Hospital Zurich (Switzerland); Scheffel, Hans; Feuchtner, Gudrun; Marincek, Borut [Institute of Diagnostic Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich (Switzerland); Alkadhi, Hatem [Institute of Diagnostic Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich (Switzerland); Cardiac MR PET CT Program, Massachusetts General Hospital and Harvard Medical School, Boston, MA (United States); Leschka, Sebastian [Institute of Diagnostic Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich (Switzerland)

    2011-07-15

    Objective: To compare, in patients with suspicion of coronary artery disease (CAD) and low heart rates, image quality, diagnostic performance, and radiation dose values of prospectively and retrospectively electrocardiography (ECG)-gated dual-source computed tomography coronary angiography (CTCA) for the diagnosis of significant coronary stenoses. Materials and methods: Two-hundred consecutive patients with heart rates {<=}70 bpm were retrospectively enrolled; 100 patients undergoing prospectively ECG-gated CTCA (group 1) and 100 patients undergoing retrospectively-gated CTCA (group 2). Coronary artery segments were assessed for image quality and significant luminal diameter narrowing. Sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and accuracy of both CTCA groups were determined using conventional catheter angiography (CCA) as reference standard. Radiation dose values were calculated. Results: Both groups were comparable regarding gender, body weight, cardiovascular risk profile, severity of CAD, mean heart rate, heart rate variability, and Agatston score (all p > 0.05). There was no significant difference in the rate of non-assessable coronary segments between group 1 (1.6%, 24/1404) and group 2 (1.4%, 19/1385; p = 0.77); non-diagnostic image quality was significantly (p < 0.001) more often attributed to stair step artifacts in group 1. Segment-based sensitivity, specificity, PPV, NPV, and accuracy were 98%, 98%, 88%, 100%, and 100% among group 1; 96%, 99%, 90%, 100%, and 98% among group 2, respectively. Parameters of diagnostic performance were similar (all p > 0.05). Mean effective radiation dose of prospectively ECG-gated CTCA (2.2 {+-} 0.4 mSv) was significantly (p < 0.0001) smaller than that of retrospectively ECG-gated CTCA (8.1 {+-} 0.6 mSv). Conclusion: Prospectively ECG-gated CTCA yields similar image quality, performs as accurately as retrospectively ECG-gated CTCA in patients having heart rates {<=}70 bpm

  7. Long Range Aircraft Trajectory Prediction

    OpenAIRE

    Magister, Tone

    2009-01-01

    The subject of the paper is the improvement of the aircraft future trajectory prediction accuracy for long-range airborne separation assurance. The strategic planning of safe aircraft flights and effective conflict avoidance tactics demand timely and accurate conflict detection based upon future four–dimensional airborne traffic situation prediction which is as accurate as each aircraft flight trajectory prediction. The improved kinematics model of aircraft relative flight considering flight ...

  8. To help, or not to help, that is not the only question: An investigation of the interplay of different factors to predict helping behavior in an accurate and effective way.

    OpenAIRE

    Urschler, David F.

    2016-01-01

    Previous research has shown that people’s willingness to help those in need is influenced by a multitude of factors (e.g., perceived dangerousness of a situation, cost-benefit analysis, attributions of responsibility, kinship, status, and culture). However, past research has often focused on single factors to predict helping intentions. Therefore, the present thesis examines the interplay of different factors in order to predict helping intentions in the most accurate and effective way. Th...

  9. What is the most accurate whole-body imaging modality for assessment of local and distant recurrent disease in colorectal cancer? A meta-analysis. Imaging for recurrent colorectal cancer

    International Nuclear Information System (INIS)

    Maas, Monique; Lambregts, Doenja M.J.; Rutten, Iris J.G.; Cappendijk, Vincent C.; Beets-Tan, Regina G.H.; Nelemans, Patty J.; Beets, Geerard L.

    2011-01-01

    The objective of this study was to compare the diagnostic performance of positron emission tomography (PET), PET/CT, CT and MRI as whole-body imaging modalities for the detection of local and/or distant recurrent disease in colorectal cancer (CRC) patients who have a (high) suspicion of recurrent disease, based on clinical findings or rise in carcinoembryonic antigen (CEA). A meta-analysis was undertaken. PubMed and Embase were searched for studies on the accuracy of whole-body imaging for patients with suspected local and/or distant recurrence of their CRC. Additionally, studies had to have included at least 20 patients with CRC and 2 x 2 contingency tables had to be provided or derivable. Articles evaluating only local recurrence or liver metastasis were excluded. Summary receiver-operating characteristic (ROC) curves were constructed from the data on sensitivity and specificity of individual studies and pooled estimates of diagnostic odds ratios (DORs) and areas under the ROC curve (AUCs) were calculated. To test for heterogeneity the Cochran Q test was used. Fourteen observational studies were included which evaluated PET, PET/CT, CT and/or MRI. Study results were available in 12 studies for PET, in 5 studies for CT, in 5 studies for PET/CT and in 1 study for MRI. AUCs for PET, PET/CT and CT were 0.94 (0.90-0.97), 0.94 (0.87-0.98) and 0.83 (0.72-0.90), respectively. In patient based analyses PET/CT had a higher diagnostic performance than PET with an AUC of 0.95 (0.89-0.97) for PET/CT vs 0.92 (0.86-0.96) for PET. Both whole-body PET and PET/CT are very accurate for the detection of local and/or distant recurrent disease in CRC patients with a (high) suspicion of recurrent disease. CT has the lowest diagnostic performance. This difference is probably mainly due to the lower accuracy of CT for detection of extrahepatic metastases (including local recurrence). For clinical practice PET/CT might be the modality of choice when evaluating patients with a (high

  10. Using image analysis as a tool for assessment of prognostic and predictive biomarkers for breast cancer: How reliable is it?

    Directory of Open Access Journals (Sweden)

    Mark C Lloyd

    2010-01-01

    Full Text Available Background : Estrogen receptor (ER, progesterone receptor (PR and human epidermal growth factor receptor-2 (HER2 are important and well-established prognostic and predictive biomarkers for breast cancers and routinely tested on patient′s tumor samples by immunohistochemical (IHC study. The accuracy of these test results has substantial impact on patient management. A critical factor that contributes to the result is the interpretation (scoring of IHC. This study investigates how computerized image analysis can play a role in a reliable scoring, and identifies potential pitfalls with common methods. Materials and Methods : Whole slide images of 33 invasive ductal carcinoma (IDC (10 ER and 23 HER2 were scored by pathologist under the light microscope and confirmed by another pathologist. The HER2 results were additionally confirmed by fluorescence in situ hybridization (FISH. The scoring criteria were adherent to the guidelines recommended by the American Society of Clinical Oncology/College of American Pathologists. Whole slide stains were then scored by commercially available image analysis algorithms from Definiens (Munich, Germany and Aperio Technologies (Vista, CA, USA. Each algorithm was modified specifically for each marker and tissue. The results were compared with the semi-quantitative manual scoring, which was considered the gold standard in this study. Results : For HER2 positive group, each algorithm scored 23/23 cases within the range established by the pathologist. For ER, both algorithms scored 10/10 cases within range. The performance of each algorithm varies somewhat from the percentage of staining as compared to the pathologist′s reading. Conclusions : Commercially available computerized image analysis can be useful in the evaluation of ER and HER2 IHC results. In order to achieve accurate results either manual pathologist region selection is necessary, or an automated region selection tool must be employed. Specificity can

  11. Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction

    Directory of Open Access Journals (Sweden)

    Eiji Watanabe

    2018-03-01

    Full Text Available The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

  12. SU-D-218-05: Material Quantification in Spectral X-Ray Imaging: Optimization and Validation.

    Science.gov (United States)

    Nik, S J; Thing, R S; Watts, R; Meyer, J

    2012-06-01

    To develop and validate a multivariate statistical method to optimize scanning parameters for material quantification in spectral x-rayimaging. An optimization metric was constructed by extensively sampling the thickness space for the expected number of counts for m (two or three) materials. This resulted in an m-dimensional confidence region ofmaterial quantities, e.g. thicknesses. Minimization of the ellipsoidal confidence region leads to the optimization of energy bins. For the given spectrum, the minimum counts required for effective material separation can be determined by predicting the signal-to-noise ratio (SNR) of the quantification. A Monte Carlo (MC) simulation framework using BEAM was developed to validate the metric. Projection data of the m-materials was generated and material decomposition was performed for combinations of iodine, calcium and water by minimizing the z-score between the expected spectrum and binned measurements. The mean square error (MSE) and variance were calculated to measure the accuracy and precision of this approach, respectively. The minimum MSE corresponds to the optimal energy bins in the BEAM simulations. In the optimization metric, this is equivalent to the smallest confidence region. The SNR of the simulated images was also compared to the predictions from the metric. TheMSE was dominated by the variance for the given material combinations,which demonstrates accurate material quantifications. The BEAMsimulations revealed that the optimization of energy bins was accurate to within 1keV. The SNRs predicted by the optimization metric yielded satisfactory agreement but were expectedly higher for the BEAM simulations due to the inclusion of scattered radiation. The validation showed that the multivariate statistical method provides accurate material quantification, correct location of optimal energy bins and adequateprediction of image SNR. The BEAM code system is suitable for generating spectral x- ray imaging simulations.

  13. SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance

    Energy Technology Data Exchange (ETDEWEB)

    Cline, K; Narayanasamy, G; Obediat, M; Stanley, D; Stathakis, S; Kirby, N [University of Texas Health Science Center at San Antonio, Cancer Therapy and Research Center, San Antonio, TX (United States); Kim, H [University of California San Francisco, San Francisco, CA (United States)

    2015-06-15

    Purpose: Deformable image registration (DIR) is used routinely in the clinic without a formalized quality assurance (QA) process. Using simulated deformations to digitally deform images in a known way and comparing to DIR algorithm predictions is a powerful technique for DIR QA. This technique must also simulate realistic image noise and artifacts, especially between modalities. This study developed an algorithm to create simulated daily kV cone-beam computed-tomography (CBCT) images from CT images for DIR QA between these modalities. Methods: A Catphan and physical head-and-neck phantom, with known deformations, were used. CT and kV-CBCT images of the Catphan were utilized to characterize the changes in Hounsfield units, noise, and image cupping that occur between these imaging modalities. The algorithm then imprinted these changes onto a CT image of the deformed head-and-neck phantom, thereby creating a simulated-CBCT image. CT and kV-CBCT images of the undeformed and deformed head-and-neck phantom were also acquired. The Velocity and MIM DIR algorithms were applied between the undeformed CT image and each of the deformed CT, CBCT, and simulated-CBCT images to obtain predicted deformations. The error between the known and predicted deformations was used as a metric to evaluate the quality of the simulated-CBCT image. Ideally, the simulated-CBCT image registration would produce the same accuracy as the deformed CBCT image registration. Results: For Velocity, the mean error was 1.4 mm for the CT-CT registration, 1.7 mm for the CT-CBCT registration, and 1.4 mm for the CT-simulated-CBCT registration. These same numbers were 1.5, 4.5, and 5.9 mm, respectively, for MIM. Conclusion: All cases produced similar accuracy for Velocity. MIM produced similar values of accuracy for CT-CT registration, but was not as accurate for CT-CBCT registrations. The MIM simulated-CBCT registration followed this same trend, but overestimated MIM DIR errors relative to the CT

  14. SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance

    International Nuclear Information System (INIS)

    Cline, K; Narayanasamy, G; Obediat, M; Stanley, D; Stathakis, S; Kirby, N; Kim, H

    2015-01-01

    Purpose: Deformable image registration (DIR) is used routinely in the clinic without a formalized quality assurance (QA) process. Using simulated deformations to digitally deform images in a known way and comparing to DIR algorithm predictions is a powerful technique for DIR QA. This technique must also simulate realistic image noise and artifacts, especially between modalities. This study developed an algorithm to create simulated daily kV cone-beam computed-tomography (CBCT) images from CT images for DIR QA between these modalities. Methods: A Catphan and physical head-and-neck phantom, with known deformations, were used. CT and kV-CBCT images of the Catphan were utilized to characterize the changes in Hounsfield units, noise, and image cupping that occur between these imaging modalities. The algorithm then imprinted these changes onto a CT image of the deformed head-and-neck phantom, thereby creating a simulated-CBCT image. CT and kV-CBCT images of the undeformed and deformed head-and-neck phantom were also acquired. The Velocity and MIM DIR algorithms were applied between the undeformed CT image and each of the deformed CT, CBCT, and simulated-CBCT images to obtain predicted deformations. The error between the known and predicted deformations was used as a metric to evaluate the quality of the simulated-CBCT image. Ideally, the simulated-CBCT image registration would produce the same accuracy as the deformed CBCT image registration. Results: For Velocity, the mean error was 1.4 mm for the CT-CT registration, 1.7 mm for the CT-CBCT registration, and 1.4 mm for the CT-simulated-CBCT registration. These same numbers were 1.5, 4.5, and 5.9 mm, respectively, for MIM. Conclusion: All cases produced similar accuracy for Velocity. MIM produced similar values of accuracy for CT-CT registration, but was not as accurate for CT-CBCT registrations. The MIM simulated-CBCT registration followed this same trend, but overestimated MIM DIR errors relative to the CT

  15. Fast and accurate semi-automated segmentation method of spinal cord MR images at 3T applied to the construction of a cervical spinal cord template.

    Directory of Open Access Journals (Sweden)

    Mohamed-Mounir El Mendili

    Full Text Available To design a fast and accurate semi-automated segmentation method for spinal cord 3T MR images and to construct a template of the cervical spinal cord.A semi-automated double threshold-based method (DTbM was proposed enabling both cross-sectional and volumetric measures from 3D T2-weighted turbo spin echo MR scans of the spinal cord at 3T. Eighty-two healthy subjects, 10 patients with amyotrophic lateral sclerosis, 10 with spinal muscular atrophy and 10 with spinal cord injuries were studied. DTbM was compared with active surface method (ASM, threshold-based method (TbM and manual outlining (ground truth. Accuracy of segmentations was scored visually by a radiologist in cervical and thoracic cord regions. Accuracy was also quantified at the cervical and thoracic levels as well as at C2 vertebral level. To construct a cervical template from healthy subjects' images (n=59, a standardization pipeline was designed leading to well-centered straight spinal cord images and accurate probability tissue map.Visual scoring showed better performance for DTbM than for ASM. Mean Dice similarity coefficient (DSC was 95.71% for DTbM and 90.78% for ASM at the cervical level and 94.27% for DTbM and 89.93% for ASM at the thoracic level. Finally, at C2 vertebral level, mean DSC was 97.98% for DTbM compared with 98.02% for TbM and 96.76% for ASM. DTbM showed similar accuracy compared with TbM, but with the advantage of limited manual interaction.A semi-automated segmentation method with limited manual intervention was introduced and validated on 3T images, enabling the construction of a cervical spinal cord template.

  16. Fast and accurate semi-automated segmentation method of spinal cord MR images at 3T applied to the construction of a cervical spinal cord template.

    Science.gov (United States)

    El Mendili, Mohamed-Mounir; Chen, Raphaël; Tiret, Brice; Villard, Noémie; Trunet, Stéphanie; Pélégrini-Issac, Mélanie; Lehéricy, Stéphane; Pradat, Pierre-François; Benali, Habib

    2015-01-01

    To design a fast and accurate semi-automated segmentation method for spinal cord 3T MR images and to construct a template of the cervical spinal cord. A semi-automated double threshold-based method (DTbM) was proposed enabling both cross-sectional and volumetric measures from 3D T2-weighted turbo spin echo MR scans of the spinal cord at 3T. Eighty-two healthy subjects, 10 patients with amyotrophic lateral sclerosis, 10 with spinal muscular atrophy and 10 with spinal cord injuries were studied. DTbM was compared with active surface method (ASM), threshold-based method (TbM) and manual outlining (ground truth). Accuracy of segmentations was scored visually by a radiologist in cervical and thoracic cord regions. Accuracy was also quantified at the cervical and thoracic levels as well as at C2 vertebral level. To construct a cervical template from healthy subjects' images (n=59), a standardization pipeline was designed leading to well-centered straight spinal cord images and accurate probability tissue map. Visual scoring showed better performance for DTbM than for ASM. Mean Dice similarity coefficient (DSC) was 95.71% for DTbM and 90.78% for ASM at the cervical level and 94.27% for DTbM and 89.93% for ASM at the thoracic level. Finally, at C2 vertebral level, mean DSC was 97.98% for DTbM compared with 98.02% for TbM and 96.76% for ASM. DTbM showed similar accuracy compared with TbM, but with the advantage of limited manual interaction. A semi-automated segmentation method with limited manual intervention was introduced and validated on 3T images, enabling the construction of a cervical spinal cord template.

  17. Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images.

    Science.gov (United States)

    Jian, Junming; Xiong, Fei; Xia, Wei; Zhang, Rui; Gu, Jinhui; Wu, Xiaodong; Meng, Xiaochun; Gao, Xin

    2018-06-01

    Segmentation of colorectal tumors is the basis of preoperative prediction, staging, and therapeutic response evaluation. Due to the blurred boundary between lesions and normal colorectal tissue, it is hard to realize accurate segmentation. Routinely manual or semi-manual segmentation methods are extremely tedious, time-consuming, and highly operator-dependent. In the framework of FCNs, a segmentation method for colorectal tumor was presented. Normalization was applied to reduce the differences among images. Borrowing from transfer learning, VGG-16 was employed to extract features from normalized images. We conducted five side-output blocks from the last convolutional layer of each block of VGG-16 along the network, these side-output blocks can deep dive multiscale features, and produced corresponding predictions. Finally, all of the predictions from side-output blocks were fused to determine the final boundaries of the tumors. A quantitative comparison of 2772 colorectal tumor manual segmentation results from T2-weighted magnetic resonance images shows that the average Dice similarity coefficient, positive predictive value, specificity, sensitivity, Hammoude distance, and Hausdorff distance were 83.56, 82.67, 96.75, 87.85%, 0.2694, and 8.20, respectively. The proposed method is superior to U-net in colorectal tumor segmentation (P colorectal tumor segmentation (P > 0.05). The results indicate that the introduction of FCNs contributed to accurate segmentation of colorectal tumors. This method has the potential to replace the present time-consuming and nonreproducible manual segmentation method.

  18. Absolute Hounsfield unit measurement on noncontrast computed tomography cannot accurately predict struvite stone composition.

    Science.gov (United States)

    Marchini, Giovanni Scala; Gebreselassie, Surafel; Liu, Xiaobo; Pynadath, Cindy; Snyder, Grace; Monga, Manoj

    2013-02-01

    The purpose of our study was to determine, in vivo, whether single-energy noncontrast computed tomography (NCCT) can accurately predict the presence/percentage of struvite stone composition. We retrospectively searched for all patients with struvite components on stone composition analysis between January 2008 and March 2012. Inclusion criteria were NCCT prior to stone analysis and stone size ≥4 mm. A single urologist, blinded to stone composition, reviewed all NCCT to acquire stone location, dimensions, and Hounsfield unit (HU). HU density (HUD) was calculated by dividing mean HU by the stone's largest transverse diameter. Stone analysis was performed via Fourier transform infrared spectrometry. Independent sample Student's t-test and analysis of variance (ANOVA) were used to compare HU/HUD among groups. Spearman's correlation test was used to determine the correlation between HU and stone size and also HU/HUD to % of each component within the stone. Significance was considered if pR=0.017; p=0.912) and negative with HUD (R=-0.20; p=0.898). Overall, 3 (6.8%) had stones (n=5) with other miscellaneous stones (n=39), no difference was found for HU (p=0.09) but HUD was significantly lower for pure stones (27.9±23.6 v 72.5±55.9, respectively; p=0.006). Again, significant overlaps were seen. Pure struvite stones have significantly lower HUD than mixed struvite stones, but overlap exists. A low HUD may increase the suspicion for a pure struvite calculus.

  19. The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction

    DEFF Research Database (Denmark)

    Sabuncu, Mert R.; Van Leemput, Koen

    2011-01-01

    This paper presents the Relevance VoxelMachine (RVoxM), a Bayesian multivariate pattern analysis (MVPA) algorithm that is specifically designed for making predictions based on image data. In contrast to generic MVPA algorithms that have often been used for this purpose, the method is designed to ...

  20. Prediction method of unburnt carbon for coal fired utility boiler using image processing technique of combustion flame

    International Nuclear Information System (INIS)

    Shimoda, M.; Sugano, A.; Kimura, T.; Watanabe, Y.; Ishiyama, K.

    1990-01-01

    This paper reports on a method predicting unburnt carbon in a coal fired utility boiler developed using an image processing technique. The method consists of an image processing unit and a furnace model unit. temperature distribution of combustion flames can be obtained through the former unit. The later calculates dynamics of the carbon reduction from the burner stages to the furnace outlet using coal feed rate, air flow rate, chemical and ash content of coal. An experimental study shows that the prediction error of the unburnt carbon can be reduced to 10%

  1. Comparison of photon attenuation coefficients (2-150 KeV) for diagnostic imaging simulations

    Science.gov (United States)

    Dodge, Charles W., III; Flynn, Michael J.

    2004-05-01

    The Radiology Research Laboratory at the Henry Ford Hospital has been involved in modeling x-ray units in order to predict image quality. A critical part of that modeling process is the accurate choice of interaction coefficients. This paper serves as a review and comparison of existing interaction models. Our objective was to obtain accurate and easily calculated interaction coefficients, at diagnostically relevant energies. We obtained data from: McMaster, Lawrence Berkeley Lab data (LBL), XCOM and FFAST Data from NIST, and the EPDL-97 database via LLNL. Our studies involve low energy photons; therefore, comparisons were limited to Coherent (Rayleigh), Incoherent (Compton) and Photoelectric effects, which were summed to determine a total interaction cross section. Without measured data, it becomes difficult to definitively choose the most accurate method. However, known limitations in the McMaster data and smoothing of photo-edge transitions can be used as a guide to establish more valid approaches. Each method was compared to one another graphically and at individual points. We found that agreement between all methods was excellent when away from photo-edges. Near photo-edges and at low energies, most methods were less accurate. Only the Chanter (FFAST) data seems to have consistently and accurately predicted the placement of edges (through M-shell), while minimizing smoothing errors. The EPDL-97 data by LLNL was the best over method in predicting coherent and incoherent cross sections.

  2. Effect of Imaging Parameter Thresholds on MRI Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes.

    Directory of Open Access Journals (Sweden)

    Wei-Ching Lo

    Full Text Available The purpose of this study is to evaluate the predictive performance of magnetic resonance imaging (MRI markers in breast cancer patients by subtype. Sixty-four patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy were enrolled in this study. Each patient received a dynamic contrast-enhanced (DCE-MRI at baseline, after 1 cycle of chemotherapy and before surgery. Functional tumor volume (FTV, the imaging marker measured by DCE-MRI, was computed at various thresholds of percent enhancement (PEt and signal-enhancement ratio (SERt. Final FTV before surgery and percent changes of FTVs at the early and final treatment time points were used to predict patients' recurrence-free survival. The full cohort and each subtype defined by the status of hormone receptor and human epidermal growth factor receptor 2 (HR+/HER2-, HER2+, triple negative were analyzed. Predictions were evaluated using the Cox proportional hazard model when PEt changed from 30% to 200% in steps of 10% and SERt changed from 0 to 2 in steps of 0.2. Predictions with high hazard ratios and low p-values were considered as strong. Different profiles of FTV as predictors for recurrence-free survival were observed in each breast cancer subtype and strong associations with survival were observed at different PEt/SERt combinations that resulted in different FTVs. Findings from this retrospective study suggest that the predictive performance of imaging markers based on FTV may be improved with enhancement thresholds being optimized separately for clinically-relevant subtypes defined by HR and HER2 receptor expression.

  3. Prediction of the microsurgical window for skull-base tumors by advanced three-dimensional multi-fusion volumetric imaging

    International Nuclear Information System (INIS)

    Oishi, Makoto; Fukuda, Masafumi; Saito, Akihiko; Hiraishi, Tetsuya; Fujii, Yukihiko; Ishida, Go

    2011-01-01

    The surgery of skull base tumors (SBTs) is difficult due to the complex and narrow surgical window that is restricted by the cranium and important structures. The utility of three-dimensional multi-fusion volumetric imaging (3-D MFVI) for visualizing the predicted window for SBTs was evaluated. Presurgical simulation using 3-D MFVI was performed in 32 patients with SBTs. Imaging data were collected from computed tomography, magnetic resonance imaging, and digital subtraction angiography. Skull data was processed to imitate actual bone resection and integrated with various structures extracted from appropriate imaging modalities by image-analyzing software. The simulated views were compared with the views obtained during surgery. All craniotomies and bone resections except opening of the acoustic canal in 2 patients were performed as simulated. The simulated window allowed observation of the expected microsurgical anatomies including tumors, vasculatures, and cranial nerves, through the predicted operative window. We could not achieve the planned tumor removal in only 3 patients. 3-D MFVI afforded high quality images of the relevant microsurgical anatomies during the surgery of SBTs. The intraoperative deja-vu effect of the simulation increased the confidence of the surgeon in the planned surgical procedures. (author)

  4. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    Science.gov (United States)

    An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

    2017-01-01

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

  5. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    International Nuclear Information System (INIS)

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin; Hollingsworth, Alan B.; Qian, Wei

    2015-01-01

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy

  6. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin, E-mail: Bin.Zheng-1@ou.edu [School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019 (United States); Hollingsworth, Alan B. [Mercy Women’s Center, Mercy Health Center, Oklahoma City, Oklahoma 73120 (United States); Qian, Wei [Department of Electrical and Computer Engineering, University of Texas, El Paso, Texas 79968 (United States)

    2015-11-15

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

  7. Comparison of high-resolution magnification narrow-band imaging and white-light endoscopy in the prediction of histology in Barrett's oesophagus.

    Science.gov (United States)

    Singh, Rajvinder; Karageorgiou, Haris; Owen, Victoria; Garsed, Klara; Fortun, Paul J; Fogden, Edward; Subramaniam, Venkataraman; Shonde, Anthony; Kaye, Philip; Hawkey, Christopher J; Ragunath, Krish

    2009-01-01

    To evaluate whether there is any appreciable difference in imaging characteristics between high-resolution magnification white-light endoscopy (WLE-Z) and narrow-band imaging (NBI-Z) in Barrett's oesophagus (BE) and if this translates into superior prediction of histology. This was a prospective single-centre study involving 21 patients (75 areas, corresponding NBI-Z and WLE-Z images) with BE. Mucosal patterns (pit pattern and microvascular morphology) were evaluated for their image quality on a visual analogue scale (VAS) of 1-10 by five expert endoscopists. The endoscopists then predicted mucosal morphology based on four subtypes which can be visualized in BE. Type A: round pits, regular microvasculature; type B: villous/ridge pits, regular microvasculature; type C: absent pits, regular microvasculature; type D: distorted pits, irregular microvasculature. The sensitivity (Sn), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were then compared with the final histopathological analysis and the interobserver variability calculated. The overall pit and microvasculature quality was significantly higher for NBI-Z, pit: NBI-Z=6, WLE-Z=4.5, p < 0.001; microvasculature: NBI-Z=7.3, WLE-Z=4.9, p < 0.001. This translated into a superior prediction of histology (Sn: NBI-Z: 88.9, WLE-Z: 71.9, p < 0.001). For the prediction of dysplasia, NBI-Z was superior to WLE-Z (chi(2)=10.3, p < 0.05). The overall kappa agreement among the five endoscopists for NBI-Z and WLE-Z, respectively, was 0.59 and 0.31 (p < 0.001). NBI-Z is superior to WLE-Z in the prediction of histology in BE, with good reproducibility. This novel imaging modality could be an important tool for surveillance of patients with BE.

  8. A Real-Time Accurate Model and Its Predictive Fuzzy PID Controller for Pumped Storage Unit via Error Compensation

    Directory of Open Access Journals (Sweden)

    Jianzhong Zhou

    2017-12-01

    Full Text Available Model simulation and control of pumped storage unit (PSU are essential to improve the dynamic quality of power station. Only under the premise of the PSU models reflecting the actual transient process, the novel control method can be properly applied in the engineering. The contributions of this paper are that (1 a real-time accurate equivalent circuit model (RAECM of PSU via error compensation is proposed to reconcile the conflict between real-time online simulation and accuracy under various operating conditions, and (2 an adaptive predicted fuzzy PID controller (APFPID based on RAECM is put forward to overcome the instability of conventional control under no-load conditions with low water head. Respectively, all hydraulic factors in pipeline system are fully considered based on equivalent lumped-circuits theorem. The pretreatment, which consists of improved Suter-transformation and BP neural network, and online simulation method featured by two iterative loops are synthetically proposed to improve the solving accuracy of the pump-turbine. Moreover, the modified formulas for compensating error are derived with variable-spatial discretization to improve the accuracy of the real-time simulation further. The implicit RadauIIA method is verified to be more suitable for PSUGS owing to wider stable domain. Then, APFPID controller is constructed based on the integration of fuzzy PID and the model predictive control. Rolling prediction by RAECM is proposed to replace rolling optimization with its computational speed guaranteed. Finally, the simulation and on-site measurements are compared to prove trustworthy of RAECM under various running conditions. Comparative experiments also indicate that APFPID controller outperforms other controllers in most cases, especially low water head conditions. Satisfying results of RAECM have been achieved in engineering and it provides a novel model reference for PSUGS.

  9. Spinal cord multi-parametric magnetic resonance imaging for survival prediction in amyotrophic lateral sclerosis.

    Science.gov (United States)

    Querin, G; El Mendili, M M; Lenglet, T; Delphine, S; Marchand-Pauvert, V; Benali, H; Pradat, P-F

    2017-08-01

    Assessing survival is a critical issue in patients with amyotrophic lateral sclerosis (ALS). Neuroimaging seems to be promising in the assessment of disease severity and several studies also suggest a strong relationship between spinal cord (SC) atrophy described by magnetic resonance imaging (MRI) and disease progression. The aim of the study was to determine the predictive added value of multimodal SC MRI on survival. Forty-nine ALS patients were recruited and clinical data were collected. Patients were scored on the Revised ALS Functional Rating Scale and manual muscle testing. They were followed longitudinally to assess survival. The cervical SC was imaged using the 3 T MRI system. Cord volume and cross-sectional area (CSA) at each vertebral level were computed. Diffusion tensor imaging metrics were measured. Imaging metrics and clinical variables were used as inputs for a multivariate Cox regression survival model. On building a multivariate Cox regression model with clinical and MRI parameters, fractional anisotropy, magnetization transfer ratio and CSA at C2-C3, C4-C5, C5-C6 and C6-C7 vertebral levels were significant. Moreover, the hazard ratio calculated for CSA at the C3-C4 and C5-C6 levels indicated an increased risk for patients with SC atrophy (respectively 0.66 and 0.68). In our cohort, MRI parameters seem to be more predictive than clinical variables, which had a hazard ratio very close to 1. It is suggested that multimodal SC MRI could be a useful tool in survival prediction especially if used at the beginning of the disease and when combined with clinical variables. To validate it as a biomarker, confirmation of the results in bigger independent cohorts of patients is warranted. © 2017 EAN.

  10. Machine learning to analyze images of shocked materials for precise and accurate measurements

    Energy Technology Data Exchange (ETDEWEB)

    Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.; Meehan, B. T.; Ramos, Kyle J.; Bolme, Cindy A.; Sandberg, Richard L.; Nelson, Keith A.

    2017-09-14

    A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

  11. Accurately Diagnosing Uric Acid Stones from Conventional Computerized Tomography Imaging: Development and Preliminary Assessment of a Pixel Mapping Software.

    Science.gov (United States)

    Ganesan, Vishnu; De, Shubha; Shkumat, Nicholas; Marchini, Giovanni; Monga, Manoj

    2018-02-01

    Preoperative determination of uric acid stones from computerized tomography imaging would be of tremendous clinical use. We sought to design a software algorithm that could apply data from noncontrast computerized tomography to predict the presence of uric acid stones. Patients with pure uric acid and calcium oxalate stones were identified from our stone registry. Only stones greater than 4 mm which were clearly traceable from initial computerized tomography to final composition were included in analysis. A semiautomated computer algorithm was used to process image data. Average and maximum HU, eccentricity (deviation from a circle) and kurtosis (peakedness vs flatness) were automatically generated. These parameters were examined in several mathematical models to predict the presence of uric acid stones. A total of 100 patients, of whom 52 had calcium oxalate and 48 had uric acid stones, were included in the final analysis. Uric acid stones were significantly larger (12.2 vs 9.0 mm, p = 0.03) but calcium oxalate stones had higher mean attenuation (457 vs 315 HU, p = 0.001) and maximum attenuation (918 vs 553 HU, p uric acid stones. A combination of stone size, attenuation intensity and attenuation pattern from conventional computerized tomography can distinguish uric acid stones from calcium oxalate stones with high sensitivity and specificity. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  12. MR imaging of hypoglycemic encephalopathy: lesion distribution and prognosis prediction by diffusion-weighted imaging

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Jeong-Hyun; Kim, Young-Joo; Yoo, Won-Jong; Ihn, Yon-Kwon; Kim, Jee-Young; Kim, Bum-Soo [The Catholic University of Korea, Department of Radiology, College of Medicine, Uijongbu, Kyunggi-do (Korea); Song, Ha-Hun [Cheju Halla General Hospital, Department of Radiology, Jeju (Korea)

    2009-10-15

    The aim of this study was to evaluate the patterns of hypoglycemic encephalopathy on diffusion-weighted imaging (DWI) and the relationship between the imaging patterns and clinical outcomes. This retrospective study included 17 consecutive patients that had hypoglycemic encephalopathy with DWI abnormalities. The topographic distributions of the DWI abnormalities of the cortex, deep gray matter, and white matter structures were assessed. In addition, possible correlation between the patterns of brain injury on DWI and clinical outcomes was investigated. There were three patterns of DWI abnormalities: involvement of both gray and white matter (n=8), selective involvement of gray matter (n=4), and selective involvement of white matter (n=5). There was no significant difference in the initial blood glucose levels among patients for each of the imaging patterns. Most patients (16/17) had bilateral symmetrical abnormalities. Among patients with bilateral symmetrical gray and/or white matter injuries, one had moderate to severe disability and 14 remained in a persistent vegetative state. The two patients with a focal unilateral white matter abnormality and a localized splenial abnormality recovered without neurological deficits. The results of this study showed that white matter was more sensitive to hypoglycemia than previously thought and there was no specific association between the patterns of injury and clinical outcomes whether the cerebral cortex, deep gray matter, and/or white matter were affected. Diffuse and extensive injury observed on the DWI predicts a poor neurologic outcome in patients with hypoglycemic injuries. (orig.)

  13. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

    Directory of Open Access Journals (Sweden)

    Snowdon Stuart

    2009-07-01

    Full Text Available Abstract Background Metabolomics experiments using Mass Spectrometry (MS technology measure the mass to charge ratio (m/z and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of Results Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50% of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data. Conclusion We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data.

  14. CFD-FEM coupling for accurate prediction of thermal fatigue

    International Nuclear Information System (INIS)

    Hannink, M.H.C.; Kuczaj, A.K.; Blom, F.J.; Church, J.M.; Komen, E.M.J.

    2009-01-01

    Thermal fatigue is a safety related issue in primary pipework systems of nuclear power plants. Life extension of current reactors and the design of a next generation of new reactors lead to growing importance of research in this direction. The thermal fatigue degradation mechanism is induced by temperature fluctuations in a fluid, which arise from mixing of hot and cold flows. Accompanied physical phenomena include thermal stratification, thermal striping, and turbulence [1]. Current plant instrumentation systems allow monitoring of possible causes as stratification and temperature gradients at fatigue susceptible locations [1]. However, high-cycle temperature fluctuations associated with turbulent mixing cannot be adequately detected by common thermocouple instrumentations. For a proper evaluation of thermal fatigue, therefore, numerical simulations are necessary that couple instantaneous fluid and solid interactions. In this work, a strategy for the numerical prediction of thermal fatigue is presented. The approach couples Computational Fluid Dynamics (CFD) and the Finite Element Method (FEM). For the development of the computational approach, a classical test case for the investigation of thermal fatigue problems is studied, i.e. mixing in a T-junction. Due to turbulent mixing of hot and cold fluids in two perpendicularly connected pipes, temperature fluctuations arise in the mixing zone downstream in the flow. Subsequently, these temperature fluctuations are also induced in the pipes. The stresses that arise due to the fluctuations may eventually lead to thermal fatigue. In the first step of the applied procedure, the temperature fluctuations in both fluid and structure are calculated using the CFD method. Subsequently, the temperature fluctuations in the structure are imposed as thermal loads in a FEM model of the pipes. A mechanical analysis is then performed to determine the thermal stresses, which are used to predict the fatigue lifetime of the structure

  15. Anterior Cruciate Ligament Tear: Reliability of MR Imaging to Predict Stability after Conservative Treatment

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Hye Won; Ahn, Jin Hwan; Ahn, Joong Mo; Yoon, Young Cheol; Hong, Hyun Pyo; Yoo, So Young; Kim, Seon Woo [Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

    2007-06-15

    The aim of this study is to evaluate the reliability of MR imaging to predict the stability of the torn anterior cruciate ligament (ACL) after complete recovery of the ligament's continuity. Twenty patients with 20 knee injuries (13 males and 7 females; age range, 20 54) were enrolled in the study. The inclusion criteria were a positive history of acute trauma, diagnosis of the ACL tear by both the physical examination and the MR imaging at the initial presentation, conservative treatment, complete recovery of the continuity of the ligament on the follow up (FU) MR images and availability of the KT-2000 measurements. Two radiologists, who worked in consensus, graded the MR findings with using a 3-point system for the signal intensity, sharpness, straightness and the thickness of the healed ligament. The insufficiency of ACL was categorized into three groups according to the KT-2000 measurements. The statistic correlations between the grades of the MR findings and the degrees of ACL insufficiency were analyzed using the Cochran-Mantel-Haenszel test (p < 0.05). The p-values for each category of the MR findings according to the different groups of the KT-2000 measurements were 0.9180 for the MR signal intensity, 1.0000 for sharpness, 0.5038 for straightness and 0.2950 for thickness of the ACL. The MR findings were not significantly different between the different KT-2000 groups. MR imaging itself is not a reliable examination to predict stability of the ACL rupture outcome, even when the MR images show an intact appearance of the ACL.

  16. Hyperspectral imaging based on diffused laser light for prediction of astaxanthin coating concentration

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Nielsen, Otto Højager Attermann; Frosch, Stina

    2014-01-01

    -continuum laser as the light source was introduced. Furthermore, a parallel study with the commercially available multispectral VideometerLab imaging system was performed. The SuperK setup used 113 spectral bands (455–1,015 nm), and the VideometerLab used 20 spectral bands (385–1,050 nm). To predict...

  17. Winnerless competition principle and prediction of the transient dynamics in a Lotka-Volterra model

    Science.gov (United States)

    Afraimovich, Valentin; Tristan, Irma; Huerta, Ramon; Rabinovich, Mikhail I.

    2008-12-01

    Predicting the evolution of multispecies ecological systems is an intriguing problem. A sufficiently complex model with the necessary predicting power requires solutions that are structurally stable. Small variations of the system parameters should not qualitatively perturb its solutions. When one is interested in just asymptotic results of evolution (as time goes to infinity), then the problem has a straightforward mathematical image involving simple attractors (fixed points or limit cycles) of a dynamical system. However, for an accurate prediction of evolution, the analysis of transient solutions is critical. In this paper, in the framework of the traditional Lotka-Volterra model (generalized in some sense), we show that the transient solution representing multispecies sequential competition can be reproducible and predictable with high probability.

  18. Image-Based Visual Servoing for Manipulation Via Predictive Control – A Survey of Some Results

    Directory of Open Access Journals (Sweden)

    Corneliu Lazăr

    2016-09-01

    Full Text Available In this paper, a review of predictive control algorithms developed by the authors for visual servoing of robots in manipulation applications is presented. Using these algorithms, a control predictive framework was created for image-based visual servoing (IBVS systems. Firstly, considering the point features, in the year 2008 we introduced an internal model predictor based on the interaction matrix. Secondly, distinctly from the set-point trajectory, we introduced in 2011 the reference trajectory using the concept from predictive control. Finally, minimizing a sum of squares of predicted errors, the optimal input trajectory was obtained. The new concept of predictive control for IBVS systems was employed to develop a cascade structure for motion control of robot arms. Simulation results obtained with a simulator for predictive IBVS systems are also presented.

  19. Value of a new multiparametric score for prediction of microvascular obstruction lesions in ST-segment elevation myocardial infarction revascularized by percutaneous coronary intervention.

    Science.gov (United States)

    Amabile, Nicolas; Jacquier, Alexis; Gaudart, Jean; Sarran, Anthony; Shuaib, Anes; Panuel, Michel; Moulin, Guy; Bartoli, Jean-Michel; Paganelli, Franck

    2010-10-01

    Despite improvement in revascularization strategies, microvascular obstruction (MO) lesions remain associated with poor outcome after ST-segment elevation myocardial infarction (STEMI). To establish a bedside-available score for predicting MO lesions in STEMI, with cardiac magnetic resonance imaging (CMR) as the reference standard, and to test its prognostic value for clinical outcome. Patients with STEMI of4 accurately identified microcirculatory injuries (sensitivity 84%; specificity 82%) and independently predicted the presence of MO lesions on CMR. MO score>4 predicted adverse cardiovascular events during the first year after STEMI (relative risk 2.60 [1.10-6.60], p=0.03). MO lesions are frequent in PCI-treated STEMI and are associated with larger MIs. MO score accurately predicted MO lesions and identified patients with poor outcome post-STEMI. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

  20. Prediction of prostate cancer extracapsular extension with high spatial resolution dynamic contrast-enhanced 3-T MRI

    International Nuclear Information System (INIS)

    Bloch, B.N.; Genega, Elizabeth M.; Costa, Daniel N.; Pedrosa, Ivan; Rofsky, Neil M.; Smith, Martin P.; Kressel, Herbert Y.; Ngo, Long; Sanda, Martin G.; DeWolf, William C.

    2012-01-01

    To assess the value of dynamic contrast-enhanced (DCE) combined with T2-weighted (T2W) endorectal coil (ERC) magnetic resonance imaging (MRI) at 3 T for determining extracapsular extension (ECE) of prostate cancer. In this IRB-approved study, ERC 3-T MRI of the prostate was performed in 108 patients before radical prostatectomy. T2W fast spin-echo and DCE 3D gradient echo images were acquired. The interpretations of readers with varied experience were analysed. MRI-based staging results were compared with radical prostatectomy histology. Descriptive statistics were generated for prediction of ECE and staging accuracies were determined by the area under the receiver-operating characteristic curve. The overall sensitivity, specificity, positive predictive value and negative predictive value for ECE were 75 %, 92 %, 79 % and 91 %, respectively. Diagnostic accuracy for staging was 86 %, 80 % and 91 % for all readers, experienced and less experienced readers, respectively. ERC 3-T MRI of the prostate combining DCE and T2W imaging is an accurate pretherapeutic staging tool for assessment of ECE in clinical practice across varying levels of reader experience. (orig.)

  1. Magnetic resonance imaging of valvular heart disease

    DEFF Research Database (Denmark)

    Søndergaard, Lise; Ståhlberg, F; Thomsen, C

    1999-01-01

    The optimum management of patients with valvular heart diseases requires accurate and reproducible assessment of the valvular lesion and its hemodynamic consequences. Magnetic resonance imaging (MRI) techniques, such as volume measurements, signal-void phenomena, and velocity mapping, can be used...... in an integrated approach to gain qualitative and quantitative information on valvular heart disease as well as ventricular dimensions and functions. Thus, MRI may be advantageous to the established diagnostic tools in assessing the severity of valvular heart disease as well as monitoring the lesion and predicting...... the optimal timing for valvular surgery. This paper reviews the validation of these MRI techniques in assessing valvular heart disease and discusses some typical pitfalls of the techniques, including suggestions for solutions.J. Magn. Reson. Imaging 1999;10:627-638....

  2. Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners.

    Science.gov (United States)

    Reuzé, Sylvain; Orlhac, Fanny; Chargari, Cyrus; Nioche, Christophe; Limkin, Elaine; Riet, François; Escande, Alexandre; Haie-Meder, Christine; Dercle, Laurent; Gouy, Sébastien; Buvat, Irène; Deutsch, Eric; Robert, Charlotte

    2017-06-27

    To identify an imaging signature predicting local recurrence for locally advanced cervical cancer (LACC) treated by chemoradiation and brachytherapy from baseline 18F-FDG PET images, and to evaluate the possibility of gathering images from two different PET scanners in a radiomic study. 118 patients were included retrospectively. Two groups (G1, G2) were defined according to the PET scanner used for image acquisition. Eleven radiomic features were extracted from delineated cervical tumors to evaluate: (i) the predictive value of features for local recurrence of LACC, (ii) their reproducibility as a function of the scanner within a hepatic reference volume, (iii) the impact of voxel size on feature values. Eight features were statistically significant predictors of local recurrence in G1 (p features were significantly different between G1 and G2 in the liver. Spatial resampling was not sufficient to explain the stratification effect. This study showed that radiomic features could predict local recurrence of LACC better than SUVmax. Further investigation is needed before applying a model designed using data from one PET scanner to another.

  3. Prediction of fracture profile using digital image correlation

    Science.gov (United States)

    Chaitanya, G. M. S. K.; Sasi, B.; Kumar, Anish; Babu Rao, C.; Purnachandra Rao, B.; Jayakumar, T.

    2015-04-01

    Digital Image Correlation (DIC) based full field strain mapping methodology is used for mapping strain on an aluminum sample subjected to tensile deformation. The local strains on the surface of the specimen are calculated at different strain intervals. Early localization of strain is observed at a total strain of 0.050ɛ; itself, whereas a visually apparent localization of strain is observed at a total strain of 0.088ɛ;. Orientation of the line of fracture (12.0°) is very close to the orientation of locus of strain maxima (11.6°) computed from the strain mapping at 0.063ɛ itself. These results show the efficacy of the DIC based method to predict the location as well as the profile of the fracture, at an early stage.

  4. Early postoperative repair status after rotator cuff repair cannot be accurately classified using questionnaires of patient function and isokinetic strength evaluation.

    Science.gov (United States)

    Colliver, Jessica; Wang, Allan; Joss, Brendan; Ebert, Jay; Koh, Eamon; Breidahl, William; Ackland, Timothy

    2016-04-01

    This study investigated if patients with an intact tendon repair or partial-thickness retear early after rotator cuff repair display differences in clinical evaluations and whether early tendon healing can be predicted using these assessments. We prospectively evaluated 60 patients at 16 weeks after arthroscopic supraspinatus repair. Evaluation included the Oxford Shoulder Score, 11-item version of the Disabilities of the Arm, Shoulder and Hand, visual analog scale for pain, 12-item Short Form Health Survey, isokinetic strength, and magnetic resonance imaging (MRI). Independent t tests investigated clinical differences in patients based on the Sugaya MRI rotator cuff classification system (grades 1, 2, or 3). Discriminant analysis determined whether intact repairs (Sugaya grade 1) and partial-thickness retears (Sugaya grades 2 and 3) could be predicted. No differences (P repair was intact. The ability to discriminate between groups was enhanced with up to 5 variables entered; however, only 87% of the partial-retear group and 36% of the intact-repair group were correctly classified. No differences in clinical scores existed between patients stratified by the Sugaya MRI classification system at 16 weeks. An intact repair or partial-thickness retear could not be accurately predicted. Our results suggest that correct classification of healing in the early postoperative stages should involve imaging. Copyright © 2016 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

  5. The predictive value of endorectal 3 Tesla multiparametric magnetic resonance imaging for extraprostatic extension in patients with low, intermediate and high risk prostate cancer.

    Science.gov (United States)

    Somford, D M; Hamoen, E H; Fütterer, J J; van Basten, J P; Hulsbergen-van de Kaa, C A; Vreuls, W; van Oort, I M; Vergunst, H; Kiemeney, L A; Barentsz, J O; Witjes, J A

    2013-11-01

    We determined the positive and negative predictive values of multiparametric magnetic resonance imaging for extraprostatic extension at radical prostatectomy for different prostate cancer risk groups. We evaluated a cohort of 183 patients who underwent 3 Tesla multiparametric magnetic resonance imaging, including T2-weighted, diffusion weighted magnetic resonance imaging and dynamic contrast enhanced sequences, with an endorectal coil before radical prostatectomy. Pathological stage at radical prostatectomy was used as standard reference for extraprostatic extension. The cohort was classified into low, intermediate and high risk groups according to the D'Amico criteria. We recorded prevalence of extraprostatic extension at radical prostatectomy and determined sensitivity, specificity, positive predictive value and negative predictive value of multiparametric magnetic resonance imaging for extraprostatic extension in each group. Univariate and multivariate analyses were performed to identify predictors of extraprostatic extension at radical prostatectomy. The overall prevalence of extraprostatic extension at radical prostatectomy was 49.7% ranging from 24.7% to 77.1% between low and high risk categories. Overall staging accuracy of multiparametric magnetic resonance imaging for extraprostatic extension was 73.8%, with sensitivity, specificity, positive predictive value and negative predictive value of 58.2%, 89.1%, 84.1% and 68.3%, respectively. Positive predictive value of multiparametric magnetic resonance imaging for extraprostatic extension was best in the high risk cohort with 88.8%. Negative predictive value was highest in the low risk cohort with 87.7%. With an odds ratio of 10.3 multiparametric magnetic resonance imaging is by far the best preoperative predictor of extraprostatic extension at radical prostatectomy. For adequate patient counseling, knowledge of predictive values of multiparametric magnetic resonance imaging for extraprostatic extension is

  6. Discordant correlation of breast adenoid cystic carcinoma on imaging and pathology: A case report and literature review on surgical management

    Directory of Open Access Journals (Sweden)

    Zhiyan Yan

    2018-01-01

    Conclusion: ACC can be extensively infiltrative and present much larger than its radiological size, as reported in our case. Use of better imaging modalities judiciously, in these cases, are needed to more accurately predict the true pathological size of ACC to prevent inadequate surgery.

  7. Imaging of conjoined twins

    Energy Technology Data Exchange (ETDEWEB)

    McHugh, Kieran [Great Ormond Street Hospital for Children, Department of Radiology, London (United Kingdom); Kiely, Edward M.; Spitz, Lewis [Great Ormond Street Hospital for Children, Department of Surgery, London (United Kingdom)

    2006-09-15

    The incidence of conjoined twins is estimated to be around 1 in 250,000 live births. There is a distinct female predominance. In this paper the imaging of conjoined twins both antenatally and postnatally is reviewed, in particular taking into consideration recent advances with multidetector CT. Accurate counselling of parents regarding the likely outcome of the pregnancy and the likelihood of successful separation is dependent on good prenatal imaging with ultrasound and MRI. Planning of postnatal surgical separation is aided by accurate preoperative imaging which, depending on the conjoined area, will encompass many imaging modalities, but often relies heavily on CT scanning. (orig.)

  8. Imaging of conjoined twins

    International Nuclear Information System (INIS)

    McHugh, Kieran; Kiely, Edward M.; Spitz, Lewis

    2006-01-01

    The incidence of conjoined twins is estimated to be around 1 in 250,000 live births. There is a distinct female predominance. In this paper the imaging of conjoined twins both antenatally and postnatally is reviewed, in particular taking into consideration recent advances with multidetector CT. Accurate counselling of parents regarding the likely outcome of the pregnancy and the likelihood of successful separation is dependent on good prenatal imaging with ultrasound and MRI. Planning of postnatal surgical separation is aided by accurate preoperative imaging which, depending on the conjoined area, will encompass many imaging modalities, but often relies heavily on CT scanning. (orig.)

  9. Accurate modeling and evaluation of microstructures in complex materials

    Science.gov (United States)

    Tahmasebi, Pejman

    2018-02-01

    Accurate characterization of heterogeneous materials is of great importance for different fields of science and engineering. Such a goal can be achieved through imaging. Acquiring three- or two-dimensional images under different conditions is not, however, always plausible. On the other hand, accurate characterization of complex and multiphase materials requires various digital images (I) under different conditions. An ensemble method is presented that can take one single (or a set of) I(s) and stochastically produce several similar models of the given disordered material. The method is based on a successive calculating of a conditional probability by which the initial stochastic models are produced. Then, a graph formulation is utilized for removing unrealistic structures. A distance transform function for the Is with highly connected microstructure and long-range features is considered which results in a new I that is more informative. Reproduction of the I is also considered through a histogram matching approach in an iterative framework. Such an iterative algorithm avoids reproduction of unrealistic structures. Furthermore, a multiscale approach, based on pyramid representation of the large Is, is presented that can produce materials with millions of pixels in a matter of seconds. Finally, the nonstationary systems—those for which the distribution of data varies spatially—are studied using two different methods. The method is tested on several complex and large examples of microstructures. The produced results are all in excellent agreement with the utilized Is and the similarities are quantified using various correlation functions.

  10. Role of magnetic resonance imaging in patient selection for uterine artery embolization

    International Nuclear Information System (INIS)

    Cura, M.; Cura, A.; Bugnone, A.

    2006-01-01

    Uterine fibroids are common tumors of the female pelvis. Uterine artery embolization (UAE) is an effective treatment of symptomatic uterine leiomyoma in the appropriate candidates, reducing or eliminating leiomyoma-related symptoms of bleeding, bulk, and/or pain. Magnetic resonance imaging (MRI) can be used to assess women with symptoms potentially attributable to uterine leiomyomas, and help to determine who is an appropriate candidate for UAE. Because of soft tissue characterization, multiplanar imaging capabilities, and enhancement, MR imaging not only accurately detects and characterizes uterine leiomyomas but also may predict who will benefit from the embolization. MRI ability to detect coexistent uterine or pelvic pathology may change the diagnosis and treatment management of patients being evaluated for UAE

  11. Role of magnetic resonance imaging in patient selection for uterine artery embolization

    Energy Technology Data Exchange (ETDEWEB)

    Cura, M.; Cura, A.; Bugnone, A. [Univ. of Texas Health Science Center, San Antonio, TX (United States). Dept. of Radiology

    2006-12-15

    Uterine fibroids are common tumors of the female pelvis. Uterine artery embolization (UAE) is an effective treatment of symptomatic uterine leiomyoma in the appropriate candidates, reducing or eliminating leiomyoma-related symptoms of bleeding, bulk, and/or pain. Magnetic resonance imaging (MRI) can be used to assess women with symptoms potentially attributable to uterine leiomyomas, and help to determine who is an appropriate candidate for UAE. Because of soft tissue characterization, multiplanar imaging capabilities, and enhancement, MR imaging not only accurately detects and characterizes uterine leiomyomas but also may predict who will benefit from the embolization. MRI ability to detect coexistent uterine or pelvic pathology may change the diagnosis and treatment management of patients being evaluated for UAE.

  12. Imaging technologies to study the composition of live pigs: A review

    Energy Technology Data Exchange (ETDEWEB)

    Carabús, A.; Gispert, M.; Font-i-Furnols, M.

    2016-11-01

    Image techniques are increasingly being applied to livestock animals. This paper overviews recent advances in image processing analysis for live pigs, including ultrasound, visual image analysis by monitoring, dual-energy X-ray absorptiometry, magnetic resonance imaging and computed tomography. The methodology for live pigs evaluation, advantages and disadvantages of different devices, the variables and measurements analysed, the predictions obtained using these measurements and their accuracy are discussed in the present paper. Utilities of these technologies for livestock purposes are also reviewed. Computed tomography and magnetic resonance imaging yield useful results for the estimation of the amount of fat and lean mass either in live pigs or in carcasses. Ultrasound is not sufficiently accurate when high precision in estimating pig body composition is necessary but can provide useful information in agriculture to classify pigs for breeding purposes or before slaughter. Improvements in factors, such as the speed of scanning, cost and image accuracy and processing, would advance the application of image processing technologies in livestock animals. (Author)

  13. A model to predict multivessel coronary artery disease from the exercise thallium-201 stress test

    International Nuclear Information System (INIS)

    Pollock, S.G.; Abbott, R.D.; Boucher, C.A.; Watson, D.D.; Kaul, S.

    1991-01-01

    The aim of this study was to (1) determine whether nonimaging variables add to the diagnostic information available from exercise thallium-201 images for the detection of multivessel coronary artery disease; and (2) to develop a model based on the exercise thallium-201 stress test to predict the presence of multivessel disease. The study populations included 383 patients referred to the University of Virginia and 325 patients referred to the Massachusetts General Hospital for evaluation of chest pain. All patients underwent both cardiac catheterization and exercise thallium-201 stress testing between 1978 and 1981. In the University of Virginia cohort, at each level of thallium-201 abnormality (no defects, one defect, more than one defect), ST depression and patient age added significantly in the detection of multivessel disease. Logistic regression analysis using data from these patients identified three independent predictors of multivessel disease: initial thallium-201 defects, ST depression, and age. A model was developed to predict multivessel disease based on these variables. As might be expected, the risk of multivessel disease predicted by the model was similar to that actually observed in the University of Virginia population. More importantly, however, the model was accurate in predicting the occurrence of multivessel disease in the unrelated population studied at the Massachusetts General Hospital. It is, therefore, concluded that (1) nonimaging variables (age and exercise-induced ST depression) add independent information to thallium-201 imaging data in the detection of multivessel disease; and (2) a model has been developed based on the exercise thallium-201 stress test that can accurately predict the probability of multivessel disease in other populations

  14. Accurate lithography simulation model based on convolutional neural networks

    Science.gov (United States)

    Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

    2017-07-01

    Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

  15. Predictability of motor outcome according to the time of diffusion tensor imaging in patients with cerebral infarct

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Yong Hyun [Yeungnam College of Science and Technology, Department of Physical Therapy, Taegu (Korea, Republic of); Jeoung, Yong Jae [Yeungnam University, Department of Physical Medicine and Rehabilitation, College of Medicine, Taegu (Korea, Republic of); Lee, Jun [Yeungnam University, Department of Neurology, College of Medicine, Taegu (Korea, Republic of); Son, Su Min; Jang, Sung Ho [Yeungnam University 317-1, Department of Physical Medicine and Rehabilitation, College of Medicine, Taegu (Korea, Republic of); Kim, Saeyoon [Yeungnam University, Department of Pediatrics, College of Medicine, Taegu (Korea, Republic of); Kim, Chulseung [Medical Devices Clinical Trial Center of Yeungnam University Hospital, Taegu (Korea, Republic of)

    2012-07-15

    Predictability of diffusion tensor imaging tractography (DTT) for motor outcome can differ according to the time of DTT. We attempted to compare the predictability for motor outcome according to the time of diffusion tensor imaging (DTI) by analyzing the corticospinal tract (CST) integrity on DTT in patients with corona radiata (CR) infarct. Seventy-one consecutive hemiparetic patients with CR infarct were recruited. Motor function of the affected extremities was measured twice: at onset and at 6 months from onset. According to the time of DTI, patients were classified into two groups: the early scanning group (ES group) within 14 days since stroke onset; and the late scanning group (LS group) 15-28 days. Motor outcome was compared with the CST integrity on DTT. Motor prognosis was predicted from scan time of DTI and the CST integrity on DTT in the logistic regression model. According to separate regression analysis, the CST integrity of the late group was found to predict MI score (OR = 14.000, 95% CI = 3.194-61.362, p < 0.05), whereas the CST integrity of the early group was not found to predict MI score. In terms of both positive and negative predictabilities, we found that predictability of DTT for motor outcome was better in patients who were scanned later (15-28 days after onset) than in patients who were scanned earlier (1-14 days after onset). (orig.)

  16. Predictability of motor outcome according to the time of diffusion tensor imaging in patients with cerebral infarct

    International Nuclear Information System (INIS)

    Kwon, Yong Hyun; Jeoung, Yong Jae; Lee, Jun; Son, Su Min; Jang, Sung Ho; Kim, Saeyoon; Kim, Chulseung

    2012-01-01

    Predictability of diffusion tensor imaging tractography (DTT) for motor outcome can differ according to the time of DTT. We attempted to compare the predictability for motor outcome according to the time of diffusion tensor imaging (DTI) by analyzing the corticospinal tract (CST) integrity on DTT in patients with corona radiata (CR) infarct. Seventy-one consecutive hemiparetic patients with CR infarct were recruited. Motor function of the affected extremities was measured twice: at onset and at 6 months from onset. According to the time of DTI, patients were classified into two groups: the early scanning group (ES group) within 14 days since stroke onset; and the late scanning group (LS group) 15-28 days. Motor outcome was compared with the CST integrity on DTT. Motor prognosis was predicted from scan time of DTI and the CST integrity on DTT in the logistic regression model. According to separate regression analysis, the CST integrity of the late group was found to predict MI score (OR = 14.000, 95% CI = 3.194-61.362, p < 0.05), whereas the CST integrity of the early group was not found to predict MI score. In terms of both positive and negative predictabilities, we found that predictability of DTT for motor outcome was better in patients who were scanned later (15-28 days after onset) than in patients who were scanned earlier (1-14 days after onset). (orig.)

  17. Anatomically accurate, finite model eye for optical modeling.

    Science.gov (United States)

    Liou, H L; Brennan, N A

    1997-08-01

    There is a need for a schematic eye that models vision accurately under various conditions such as refractive surgical procedures, contact lens and spectacle wear, and near vision. Here we propose a new model eye close to anatomical, biometric, and optical realities. This is a finite model with four aspheric refracting surfaces and a gradient-index lens. It has an equivalent power of 60.35 D and an axial length of 23.95 mm. The new model eye provides spherical aberration values within the limits of empirical results and predicts chromatic aberration for wavelengths between 380 and 750 nm. It provides a model for calculating optical transfer functions and predicting optical performance of the eye.

  18. Breaking Snake Camouflage: Humans Detect Snakes More Accurately than Other Animals under Less Discernible Visual Conditions.

    Science.gov (United States)

    Kawai, Nobuyuki; He, Hongshen

    2016-01-01

    Humans and non-human primates are extremely sensitive to snakes as exemplified by their ability to detect pictures of snakes more quickly than those of other animals. These findings are consistent with the Snake Detection Theory, which hypothesizes that as predators, snakes were a major source of evolutionary selection that favored expansion of the visual system of primates for rapid snake detection. Many snakes use camouflage to conceal themselves from both prey and their own predators, making it very challenging to detect them. If snakes have acted as a selective pressure on primate visual systems, they should be more easily detected than other animals under difficult visual conditions. Here we tested whether humans discerned images of snakes more accurately than those of non-threatening animals (e.g., birds, cats, or fish) under conditions of less perceptual information by presenting a series of degraded images with the Random Image Structure Evolution technique (interpolation of random noise). We find that participants recognize mosaic images of snakes, which were regarded as functionally equivalent to camouflage, more accurately than those of other animals under dissolved conditions. The present study supports the Snake Detection Theory by showing that humans have a visual system that accurately recognizes snakes under less discernible visual conditions.

  19. Breaking Snake Camouflage: Humans Detect Snakes More Accurately than Other Animals under Less Discernible Visual Conditions.

    Directory of Open Access Journals (Sweden)

    Nobuyuki Kawai

    Full Text Available Humans and non-human primates are extremely sensitive to snakes as exemplified by their ability to detect pictures of snakes more quickly than those of other animals. These findings are consistent with the Snake Detection Theory, which hypothesizes that as predators, snakes were a major source of evolutionary selection that favored expansion of the visual system of primates for rapid snake detection. Many snakes use camouflage to conceal themselves from both prey and their own predators, making it very challenging to detect them. If snakes have acted as a selective pressure on primate visual systems, they should be more easily detected than other animals under difficult visual conditions. Here we tested whether humans discerned images of snakes more accurately than those of non-threatening animals (e.g., birds, cats, or fish under conditions of less perceptual information by presenting a series of degraded images with the Random Image Structure Evolution technique (interpolation of random noise. We find that participants recognize mosaic images of snakes, which were regarded as functionally equivalent to camouflage, more accurately than those of other animals under dissolved conditions. The present study supports the Snake Detection Theory by showing that humans have a visual system that accurately recognizes snakes under less discernible visual conditions.

  20. Early-postoperative magnetic resonance imaging in glial tumors: prediction of tumor regrowth and recurrence

    Energy Technology Data Exchange (ETDEWEB)

    Ekinci, Gazanfer; Akpinar, Ihsan N. E-mail: i.akpinar@mailcity.com; Baltacioglu, Feyyaz; Erzen, Canan; Kilic, Tuerker; Elmaci, Ilhan; Pamir, Necmettin

    2003-02-01

    Objective: This study investigated the value of early-postoperative magnetic resonance (EPMR) imaging in the detection of residual glial tumor and investigated the role of EPMR for the prediction of tumor regrowth and recurrence. Methods and materials: We retrospectively analyzed pre- and post-operative magnetic resonance imaging results from 50 adult patients who underwent surgical treatment for supratentorial glial tumor. There were glioblastoma multiforme in 25 patients, astrocytoma (grades II and III) in 11 patients, oligodendroglioma (grades II and III) in 9 patients, and oligoastrocytoma (grades II and III) in 5 patients. EPMR imaging was performed within 24 h after surgery. EPMR findings were compared with the neurosurgeon's intraoperative estimation of gross tumor removal. Patterns of contrast enhancement at the resection site, in residual and developing tumor tissue and blood at the resection site were evaluated on EPMR and in follow-up studies. 'Residual tumor' was defined as contrast enhancing mass at the operative site on EPMR. 'Regrowth' was defined as contrast enhancing mass detected on follow-up in the same location as the primary tumor. 'Recurrence' was defined as appearance of a mass lesion in the brain parenchyma distant from the resection bed during follow-up. Results: Nineteen patients showed no evidence of residual tumor, regrowth, or recurrence on EPMR or any of the later follow-up radiological examinations. EPMR identified 20 cases of residual tumor. Follow-up showed tumor regrowth in 10 patients, and tumor recurrence in 1 case. EPMR showed contrast enhancement of the resection bed in 45 of the 50 patients. Four of the 20 residual tumors showed a thick linear enhancement pattern, and the other 16 cases exhibited thick linear-nodular enhancement. No thin linear enhancement was observed in the residual tumor group. Nine of the 10-regrowth tumors showed a thick linear-nodular enhancement pattern, and one