Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Science.gov (United States)

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

Science.gov (United States)

Sato, João R; Moll, Jorge; Green, Sophie; Deakin, John F W; Thomaz, Carlos E; Zahn, Roland

2015-08-30

Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the high potential of our fMRI signature as a biomarker of MD vulnerability. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

Accelerated dynamic cardiac MRI exploiting sparse-Kalman-smoother self-calibration and reconstruction (k  −  t SPARKS)

International Nuclear Information System (INIS)

Park, Suhyung; Park, Jaeseok

2015-01-01

Accelerated dynamic MRI, which exploits spatiotemporal redundancies in k  −  t space and coil dimension, has been widely used to reduce the number of signal encoding and thus increase imaging efficiency with minimal loss of image quality. Nonetheless, particularly in cardiac MRI it still suffers from artifacts and amplified noise in the presence of time-drifting coil sensitivity due to relative motion between coil and subject (e.g. free breathing). Furthermore, a substantial number of additional calibrating signals is to be acquired to warrant accurate calibration of coil sensitivity. In this work, we propose a novel, accelerated dynamic cardiac MRI with sparse-Kalman-smoother self-calibration and reconstruction (k  −  t SPARKS), which is robust to time-varying coil sensitivity even with a small number of calibrating signals. The proposed k  −  t SPARKS incorporates Kalman-smoother self-calibration in k  −  t space and sparse signal recovery in x  −   f space into a single optimization problem, leading to iterative, joint estimation of time-varying convolution kernels and missing signals in k  −  t space. In the Kalman-smoother calibration, motion-induced uncertainties over the entire time frames were included in modeling state transition while a coil-dependent noise statistic in describing measurement process. The sparse signal recovery iteratively alternates with the self-calibration to tackle the ill-conditioning problem potentially resulting from insufficient calibrating signals. Simulations and experiments were performed using both the proposed and conventional methods for comparison, revealing that the proposed k  −  t SPARKS yields higher signal-to-error ratio and superior temporal fidelity in both breath-hold and free-breathing cardiac applications over all reduction factors. (paper)

Simultaneous auto-calibration and gradient delays estimation (SAGE) in non-Cartesian parallel MRI using low-rank constraints.

Science.gov (United States)

Jiang, Wenwen; Larson, Peder E Z; Lustig, Michael

2018-03-09

To correct gradient timing delays in non-Cartesian MRI while simultaneously recovering corruption-free auto-calibration data for parallel imaging, without additional calibration scans. The calibration matrix constructed from multi-channel k-space data should be inherently low-rank. This property is used to construct reconstruction kernels or sensitivity maps. Delays between the gradient hardware across different axes and RF receive chain, which are relatively benign in Cartesian MRI (excluding EPI), lead to trajectory deviations and hence data inconsistencies for non-Cartesian trajectories. These in turn lead to higher rank and corrupted calibration information which hampers the reconstruction. Here, a method named Simultaneous Auto-calibration and Gradient delays Estimation (SAGE) is proposed that estimates the actual k-space trajectory while simultaneously recovering the uncorrupted auto-calibration data. This is done by estimating the gradient delays that result in the lowest rank of the calibration matrix. The Gauss-Newton method is used to solve the non-linear problem. The method is validated in simulations using center-out radial, projection reconstruction and spiral trajectories. Feasibility is demonstrated on phantom and in vivo scans with center-out radial and projection reconstruction trajectories. SAGE is able to estimate gradient timing delays with high accuracy at a signal to noise ratio level as low as 5. The method is able to effectively remove artifacts resulting from gradient timing delays and restore image quality in center-out radial, projection reconstruction, and spiral trajectories. The low-rank based method introduced simultaneously estimates gradient timing delays and provides accurate auto-calibration data for improved image quality, without any additional calibration scans. © 2018 International Society for Magnetic Resonance in Medicine.

Decoder calibration with ultra small current sample set for intracortical brain-machine interface

Science.gov (United States)

Zhang, Peng; Ma, Xuan; Chen, Luyao; Zhou, Jin; Wang, Changyong; Li, Wei; He, Jiping

2018-04-01

Objective. Intracortical brain-machine interfaces (iBMIs) aim to restore efficient communication and movement ability for paralyzed patients. However, frequent recalibration is required for consistency and reliability, and every recalibration will require relatively large most current sample set. The aim in this study is to develop an effective decoder calibration method that can achieve good performance while minimizing recalibration time. Approach. Two rhesus macaques implanted with intracortical microelectrode arrays were trained separately on movement and sensory paradigm. Neural signals were recorded to decode reaching positions or grasping postures. A novel principal component analysis-based domain adaptation (PDA) method was proposed to recalibrate the decoder with only ultra small current sample set by taking advantage of large historical data, and the decoding performance was compared with other three calibration methods for evaluation. Main results. The PDA method closed the gap between historical and current data effectively, and made it possible to take advantage of large historical data for decoder recalibration in current data decoding. Using only ultra small current sample set (five trials of each category), the decoder calibrated using the PDA method could achieve much better and more robust performance in all sessions than using other three calibration methods in both monkeys. Significance. (1) By this study, transfer learning theory was brought into iBMIs decoder calibration for the first time. (2) Different from most transfer learning studies, the target data in this study were ultra small sample set and were transferred to the source data. (3) By taking advantage of historical data, the PDA method was demonstrated to be effective in reducing recalibration time for both movement paradigm and sensory paradigm, indicating a viable generalization. By reducing the demand for large current training data, this new method may facilitate the application

A novel approach to calibrate the Hemodynamic Model using functional Magnetic Resonance Imaging (fMRI) measurements

KAUST Repository

Khoram, Nafiseh

2016-01-21

Background The calibration of the hemodynamic model that describes changes in blood flow and blood oxygenation during brain activation is a crucial step for successfully monitoring and possibly predicting brain activity. This in turn has the potential to provide diagnosis and treatment of brain diseases in early stages. New Method We propose an efficient numerical procedure for calibrating the hemodynamic model using some fMRI measurements. The proposed solution methodology is a regularized iterative method equipped with a Kalman filtering-type procedure. The Newton component of the proposed method addresses the nonlinear aspect of the problem. The regularization feature is used to ensure the stability of the algorithm. The Kalman filter procedure is incorporated here to address the noise in the data. Results Numerical results obtained with synthetic data as well as with real fMRI measurements are presented to illustrate the accuracy, robustness to the noise, and the cost-effectiveness of the proposed method. Comparison with Existing Method(s) We present numerical results that clearly demonstrate that the proposed method outperforms the Cubature Kalman Filter (CKF), one of the most prominent existing numerical methods. Conclusion We have designed an iterative numerical technique, called the TNM-CKF algorithm, for calibrating the mathematical model that describes the single-event related brain response when fMRI measurements are given. The method appears to be highly accurate and effective in reconstructing the BOLD signal even when the measurements are tainted with high noise level (as high as 30%).

Hemodynamic modelling of BOLD fMRI - A machine learning approach

DEFF Research Database (Denmark)

Jacobsen, Danjal Jakup

2007-01-01

This Ph.D. thesis concerns the application of machine learning methods to hemodynamic models for BOLD fMRI data. Several such models have been proposed by different researchers, and they have in common a basis in physiological knowledge of the hemodynamic processes involved in the generation...... of the BOLD signal. The BOLD signal is modelled as a non-linear function of underlying, hidden (non-measurable) hemodynamic state variables. The focus of this thesis work has been to develop methods for learning the parameters of such models, both in their traditional formulation, and in a state space...... formulation. In the latter, noise enters at the level of the hidden states, as well as in the BOLD measurements themselves. A framework has been developed to allow approximate posterior distributions of model parameters to be learned from real fMRI data. This is accomplished with Markov chain Monte Carlo...

TRACEABILITY OF PRECISION MEASUREMENTS ON COORDINATE MEASURING MACHINES – TRACEABILITY, CALIBRATION AND PERFORMANCE VERIFICATION

DEFF Research Database (Denmark)

Bariani, Paolo; De Chiffre, Leonardo; Tosello, Guido

This document is used in connection with an exercise of 1 hour duration as a part of the course VISION ONLINE – One week course on Precision & Nanometrology. The exercise concerns establishment of traceability of measurements with optical coordinate machine by mean of using two different calibrated...

MRI of the Chest

Medline Plus

Full Text Available ... does not completely surround you. Some newer MRI machines have a larger diameter bore which can be ... size patients or patients with claustrophobia. Other MRI machines are open on the sides (open MRI). Open ...

Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging.

Science.gov (United States)

Shamir, Lior; Long, Joe

2016-01-01

While cognition is clearly affected by aging, it is unclear whether the process of brain aging is driven solely by accumulation of environmental damage, or involves biological pathways. We applied quantitative image analysis to profile the alteration of brain tissues during aging. A dataset of 463 brain MRI images taken from a cohort of 416 subjects was analyzed using a large set of low-level numerical image content descriptors computed from the entire brain MRI images. The correlation between the numerical image content descriptors and the age was computed, and the alterations of the brain tissues during aging were quantified and profiled using machine learning. The comprehensive set of global image content descriptors provides high Pearson correlation of ~0.9822 with the chronological age, indicating that the machine learning analysis of global features is sensitive to the age of the subjects. Profiling of the predicted age shows several periods of mild changes, separated by shorter periods of more rapid alterations. The periods with the most rapid changes were around the age of 55, and around the age of 65. The results show that the process of brain aging of is not linear, and exhibit short periods of rapid aging separated by periods of milder change. These results are in agreement with patterns observed in cognitive decline, mental health status, and general human aging, suggesting that brain aging might not be driven solely by accumulation of environmental damage. Code and data used in the experiments are publicly available.

Magnetic Resonance Imaging (MRI) -- Head

Medline Plus

Full Text Available ... does not completely surround you. Some newer MRI machines have a larger diameter bore which can be ... size patients or patients with claustrophobia. Other MRI machines are open on the sides (open MRI). Open ...

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

Science.gov (United States)

Nouretdinov, Ilia; Costafreda, Sergi G; Gammerman, Alexander; Chervonenkis, Alexey; Vovk, Vladimir; Vapnik, Vladimir; Fu, Cynthia H Y

2011-05-15

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction. Copyright © 2010 Elsevier Inc. All rights reserved.

NIRS report of utilization of MRI machine for research. Results in 2003

International Nuclear Information System (INIS)

2006-04-01

The report is an achievement of cooperative research and development by private and official facilities of the National Institute of Radiological Sciences (NIRS) MRI machine, and its applied and medical uses in 2003. Contained are the reports on the magnet (1 topic), antennae (4), physical mensurations (5), basic biological researches (6), basic studies on human body (6) and clinical studies (13), which are finally summarized in the list of the personnel, event calendar and published scientific papers. The basic studies by the MRI involve those of the brain damage by heavy particle irradiation, pediatric surgical diseases by MR-microscopy, implanted tumor volumetry in the rat, biodistribution of BPA- gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) for neutron capture therapy, ultra-high speed microscopic MRI measurement of microcirculation in the tumor, micro-imaging of human eye, hepatic glycogen content by MRS, flow analysis of cerebrospinal fluid, autopsy imaging system, numerical phantom of human body and so on. Clinical studies involve those of the drug metabolism and disposition, efficacy evaluation of radiotherapy, PET-CT-MRI image, schizophrenia, GSH detection, MP4A-PET image standardization, intracranial lymph systems, brain function, GSH in schizophrenia, obstructive hypertrophic cardiomyopathy, cholangiography, glycosaminoglycan in cartilage and high-speed imaging of prostate cancer by sensitivity encoding. (T.I.)

A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia

Directory of Open Access Journals (Sweden)

Honghui Yang

2010-10-01

Full Text Available We demonstrate a hybrid machine learning method to classify schizophrenia patients and healthy controls, using functional magnetic resonance imaging (fMRI and single nucleotide polymorphism (SNP data. The method consists of four stages: (1 SNPs with the most discriminating information between the healthy controls and schizophrenia patients are selected to construct a support vector machine ensemble (SNP-SVME. (2 Voxels in the fMRI map contributing to classification are selected to build another SVME (Voxel-SVME. (3 Components of fMRI activation obtained with independent component analysis (ICA are used to construct a single SVM classifier (ICA-SVMC. (4 The above three models are combined into a single module using a majority voting approach to make a final decision (Combined SNP-fMRI. The method was evaluated by a fully-validated leave-one-out method using 40 subjects (20 patients and 20 controls. The classification accuracy was: 0.74 for SNP-SVME, 0.82 for Voxel-SVME, 0.83 for ICA-SVMC, and 0.87 for Combined SNP-fMRI. Experimental results show that better classification accuracy was achieved by combining genetic and fMRI data than using either alone, indicating that genetic and brain function representing different, but partially complementary aspects, of schizophrenia etiopathology. This study suggests an effective way to reassess biological classification of individuals with schizophrenia, which is also potentially useful for identifying diagnostically important markers for the disorder.

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

Science.gov (United States)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

Science.gov (United States)

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.

Science.gov (United States)

Hassanpour, Saeed; Langlotz, Curtis P; Amrhein, Timothy J; Befera, Nicholas T; Lungren, Matthew P

2017-04-01

The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices. An NLP system that uses terms and patterns in manually classified narrative knee MRI reports was constructed. The NLP system was trained and tested on expert-classified knee MRI reports from two major health care organizations. Radiology reports were modeled in the training set as vectors, and a support vector machine framework was used to train the classifier. A separate test set from each organization was used to evaluate the performance of the system. We evaluated the performance of the system both within and across organizations. Standard evaluation metrics, such as accuracy, precision, recall, and F1 score (i.e., the weighted average of the precision and recall), and their respective 95% CIs were used to measure the efficacy of our classification system. The accuracy for radiology reports that belonged to the model's clinically significant concept classes after training data from the same institution was good, yielding an F1 score greater than 90% (95% CI, 84.6-97.3%). Performance of the classifier on cross-institutional application without institution-specific training data yielded F1 scores of 77.6% (95% CI, 69.5-85.7%) and 90.2% (95% CI, 84.5-95.9%) at the two organizations studied. The results show excellent accuracy by the NLP machine learning classifier in classifying free-text knee MRI reports, supporting the institution-independent reproducibility of knee MRI report classification. Furthermore, the machine learning classifier performed well on free-text knee MRI reports from another institution. These data support the feasibility of multiinstitutional classification of radiologic imaging text reports with a single machine learning classifier without requiring institution-specific training data.

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression

OpenAIRE

Sato, Jo?o R.; Moll, Jorge; Green, Sophie; Deakin, John F.W.; Thomaz, Carlos E.; Zahn, Roland

2015-01-01

Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the hi...

Standard practice of calibration of force-measuring instruments for verifying the force indication of testing machines

CERN Document Server

American Society for Testing and Materials. Philadelphia

2006-01-01

1.1 The purpose of this practice is to specify procedures for the calibration of force-measuring instruments. Procedures are included for the following types of instruments: 1.1.1 Elastic force-measuring instruments, and 1.1.2 Force-multiplying systems, such as balances and small platform scales. Note 1Verification by deadweight loading is also an acceptable method of verifying the force indication of a testing machine. Tolerances for weights for this purpose are given in Practices E 4; methods for calibration of the weights are given in NIST Technical Note 577, Methods of Calibrating Weights for Piston Gages. 1.2 The values stated in SI units are to be regarded as the standard. Other metric and inch-pound values are regarded as equivalent when required. 1.3 This practice is intended for the calibration of static force measuring instruments. It is not applicable for dynamic or high speed force calibrations, nor can the results of calibrations performed in accordance with this practice be assumed valid for...

Investigation into the accuracy of a proposed laser diode based multilateration machine tool calibration system

International Nuclear Information System (INIS)

Fletcher, S; Longstaff, A P; Myers, A

2005-01-01

Geometric and thermal calibration of CNC machine tools is required in modern machine shops with volumetric accuracy assessment becoming the standard machine tool qualification in many industries. Laser interferometry is a popular method of measuring the errors but this, and other alternatives, tend to be expensive, time consuming or both. This paper investigates the feasibility of using a laser diode based system that capitalises on the low cost nature of the diode to provide multiple laser sources for fast error measurement using multilateration. Laser diode module technology enables improved wavelength stability and spectral linewidth which are important factors for laser interferometry. With more than three laser sources, the set-up process can be greatly simplified while providing flexibility in the location of the laser sources improving the accuracy of the system

A novel approach to calibrate the Hemodynamic Model using functional Magnetic Resonance Imaging (fMRI) measurements

KAUST Repository

Khoram, Nafiseh; Zayane, Chadia; Djellouli, Rabia; Laleg-Kirati, Taous-Meriem

2016-01-01

We have designed an iterative numerical technique, called the TNM-CKF algorithm, for calibrating the mathematical model that describes the single-event related brain response when fMRI measurements are given. The method appears to be highly accurate and effective in reconstructing the BOLD signal even when the measurements are tainted with high noise level (as high as 30%).

Machine learning classifiers and fMRI: a tutorial overview.

Science.gov (United States)

Pereira, Francisco; Mitchell, Tom; Botvinick, Matthew

2009-03-01

Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).

NIRS report of utilization of MRI machine for research. Results in 2004

International Nuclear Information System (INIS)

2007-04-01

The report is an achievement of cooperative research and development by private and official facilities of the National Institute of Radiological Sciences (NIRS) MRI machine, and its applied and medical uses in 2004. Contained are the reports on the magnet (1 topic), RF coils (6), basic studies on measurements (4), biological studies on measurements (4) and clinical studies on measurements (18), which are finally summarized in the list of the personnel, event calendar and published scientific papers. The basic studies by the MRI involve those of metabolism and molecular transport measurements using MRI contrast agents, macro-analysis and micro-analysis of cerebral blood flow by CFD, samples for radiation dose measurements using polymer gel materials and the MRS method for quantification of substances in the body. Biological studies involve those of the brain damage by heavy particle irradiation, pediatric surgical diseases by MR-microscopy, PET data analysis of the monkey taking MPTP and multiple sclerosis mice. Clinical studies involve those of blood vessel coupling of cerebral nerve, micro-imaging of human eye, hepatic glycogen content by MRS, autopsy imaging, numerical phantom of human body, PET-CT-MRI image, schizophrenia, GSH detection, MP4A-PET image standardization, MR imaging of perivascular space, brain function, GSH in schizophrenia, occlusive arterial disease of lower extremity, cholangiography, glycosaminoglycan in cartilage, MR imaging of wrist joint, high-speed imaging of prostate cancer by sensitivity encoding and volumetry of rats' tissues. (J.P.N.)

WE-G-BRB-08: TG-51 Calibration of First Commercial MRI-Guided IMRT System in the Presence of 0.35 Tesla Magnetic Field.

Science.gov (United States)

Goddu, S; Green, O Pechenaya; Mutic, S

2012-06-01

The first real-time-MRI-guided radiotherapy system has been installed in a clinic and it is being evaluated. Presence of magnetic field (MF) during radiation output calibration may have implications on ionization measurements and there is a possibility that standard calibration protocols may not be suitable for dose measurements for such devices. In this study, we evaluated whether a standard calibration protocol (AAPM- TG-51) is appropriate for absolute dose measurement in presence of MF. Treatment delivery of the ViewRay (VR) system is via three 15,000Ci Cobalt-60 heads positioned 120-degrees apart and all calibration measurements were done in the presence of 0.35T MF. Two ADCL- calibrated ionization-chambers (Exradin A12, A16) were used for TG-51 calibration. Chambers were positioned at 5-cm depth, (SSD=105cm: VR's isocenter), and the MLC leaves were shaped to a 10.5cm × 10.5 cm field size. Percent-depth-dose (PDD) measurements were performed for 5 and 10 cm depths. Individual output of each head was measured using the AAPM- TG51 protocol. Calibration accuracy for each head was subsequently verified by Radiological Physics Center (RPC) TLD measurements. Measured ion-recombination (Pion) and polarity (Ppol) correction factors were less-than 1.002 and 1.006, respectively. Measured PDDs agreed with BJR-25 within ±0.2%. Maximum dose rates for the reference field size at VR's isocenter for heads 1, 2 and 3 were 1.445±0.005, 1.446±0.107, 1.431±0.006 Gy/minute, respectively. Our calibrations agreed with RPC- TLD measurements within ±1.3%, ±2.6% and ±2.0% for treatment-heads 1, 2 and 3, respectively. At the time of calibration, mean activity of the Co-60 sources was 10,800Ci±0.1%. This study shows that the TG- 51 calibration is feasible in the presence of 0.35T MF and the measurement agreement is within the range of results obtainable for conventional treatment machines. Drs. Green, Goddu, and Mutic served as scientific consultants for ViewRay, Inc. Dr. Mutic

Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers.

Science.gov (United States)

De Martino, Federico; Gentile, Francesco; Esposito, Fabrizio; Balsi, Marco; Di Salle, Francesco; Goebel, Rainer; Formisano, Elia

2007-01-01

We present a general method for the classification of independent components (ICs) extracted from functional MRI (fMRI) data sets. The method consists of two steps. In the first step, each fMRI-IC is associated with an IC-fingerprint, i.e., a representation of the component in a multidimensional space of parameters. These parameters are post hoc estimates of global properties of the ICs and are largely independent of a specific experimental design and stimulus timing. In the second step a machine learning algorithm automatically separates the IC-fingerprints into six general classes after preliminary training performed on a small subset of expert-labeled components. We illustrate this approach in a multisubject fMRI study employing visual structure-from-motion stimuli encoding faces and control random shapes. We show that: (1) IC-fingerprints are a valuable tool for the inspection, characterization and selection of fMRI-ICs and (2) automatic classifications of fMRI-ICs in new subjects present a high correspondence with those obtained by expert visual inspection of the components. Importantly, our classification procedure highlights several neurophysiologically interesting processes. The most intriguing of which is reflected, with high intra- and inter-subject reproducibility, in one IC exhibiting a transiently task-related activation in the 'face' region of the primary sensorimotor cortex. This suggests that in addition to or as part of the mirror system, somatotopic regions of the sensorimotor cortex are involved in disambiguating the perception of a moving body part. Finally, we show that the same classification algorithm can be successfully applied, without re-training, to fMRI collected using acquisition parameters, stimulation modality and timing considerably different from those used for training.

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

Science.gov (United States)

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

Advice taking from humans and machines: an fMRI and effective connectivity study

Directory of Open Access Journals (Sweden)

Kimberly Goodyear

2016-11-01

Full Text Available With new technological advances, advice can come from different sources such as machines or humans, but how individuals respond to such advice and the neural correlates involved need to be better understood. We combined functional MRI and multivariate Granger causality analysis with an X-ray luggage-screening task to investigate the neural basis and corresponding effective connectivity involved with advice utilization from agents framed as experts. Participants were asked to accept or reject good or bad advice from a human or machine agent with low reliability (high false alarm rate. We showed that unreliable advice decreased performance overall and participants interacting with the human agent had a greater depreciation of advice utilization during bad advice compared to the machine agent. These differences in advice utilization can be perceivably due to reevaluation of expectations arising from association of dispositional credibility for each agent. We demonstrated that differences in advice utilization engaged brain regions that may be associated with evaluation of personal characteristics and traits (precuneus, posterior cingulate cortex, temporoparietal junction and interoception (posterior insula. We found that the right posterior insula and left precuneus were the drivers of the advice utilization network that were reciprocally connected to each other and also projected to all other regions. Our behavioral and neuroimaging results have significant implications for society because of progressions in technology and increased interactions with machines.

Calibrators measurement system for headlamp tester of motor vehicle base on machine vision

Science.gov (United States)

Pan, Yue; Zhang, Fan; Xu, Xi-ping; Zheng, Zhe

2014-09-01

With the development of photoelectric detection technology, machine vision has a wider use in the field of industry. The paper mainly introduces auto lamps tester calibrator measuring system, of which CCD image sampling system is the core. Also, it shows the measuring principle of optical axial angle and light intensity, and proves the linear relationship between calibrator's facula illumination and image plane illumination. The paper provides an important specification of CCD imaging system. Image processing by MATLAB can get flare's geometric midpoint and average gray level. By fitting the statistics via the method of the least square, we can get regression equation of illumination and gray level. It analyzes the error of experimental result of measurement system, and gives the standard uncertainty of synthesis and the resource of optical axial angle. Optical axial angle's average measuring accuracy is controlled within 40''. The whole testing process uses digital means instead of artificial factors, which has higher accuracy, more repeatability and better mentality than any other measuring systems.

A test material for tissue characterisation and system calibration in MRI

International Nuclear Information System (INIS)

Walker, P.M.; Lerski, R.A.

1989-01-01

A tissue-equivalent test material for MR1 has been produced from a polysaccharide gel, agarose, containing gadolinium chloride chelated to EDTA. By varying the amounts of each constituent, the T 1 and T 2 of the material can be varied independently. As a result, the entire range of in vivo tissue relaxation times can be covered. Through the mathematical modelling of the 1 H relaxation theories for both the gel and chelated paramagnetic ion, it has been possible to create a material with relaxation properties and behaviour predictable as functions of both the Larmor frequency and temperature. The similarity of the material to in vivo tissues, in terms of its biological and physical NMR characteristics, makes it an excellent tissue-equivalent substance, in addition to being an accurate calibration standard for routine MRI. (author)

An innovative method for coordinate measuring machine one-dimensional self-calibration with simplified experimental process.

Science.gov (United States)

Fang, Cheng; Butler, David Lee

2013-05-01

In this paper, an innovative method for CMM (Coordinate Measuring Machine) self-calibration is proposed. In contrast to conventional CMM calibration that relies heavily on a high precision reference standard such as a laser interferometer, the proposed calibration method is based on a low-cost artefact which is fabricated with commercially available precision ball bearings. By optimizing the mathematical model and rearranging the data sampling positions, the experimental process and data analysis can be simplified. In mathematical expression, the samples can be minimized by eliminating the redundant equations among those configured by the experimental data array. The section lengths of the artefact are measured at arranged positions, with which an equation set can be configured to determine the measurement errors at the corresponding positions. With the proposed method, the equation set is short of one equation, which can be supplemented by either measuring the total length of the artefact with a higher-precision CMM or calibrating the single point error at the extreme position with a laser interferometer. In this paper, the latter is selected. With spline interpolation, the error compensation curve can be determined. To verify the proposed method, a simple calibration system was set up on a commercial CMM. Experimental results showed that with the error compensation curve uncertainty of the measurement can be reduced to 50%.

MRI of the Chest

Medline Plus

Full Text Available ... or patients with claustrophobia. Other MRI machines are open on the sides (open MRI). Open units are especially helpful for examining larger patients or those with claustrophobia. Newer open MRI units provide very high quality images for ...

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

Science.gov (United States)

Kim, Yong-Ku; Na, Kyoung-Sae

2018-01-03

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future. Copyright �

Absolute calibration in vivo measurement systems

International Nuclear Information System (INIS)

Kruchten, D.A.; Hickman, D.P.

1991-02-01

Lawrence Livermore National Laboratory (LLNL) is currently investigating a new method for obtaining absolute calibration factors for radiation measurement systems used to measure internally deposited radionuclides in vivo. Absolute calibration of in vivo measurement systems will eliminate the need to generate a series of human surrogate structures (i.e., phantoms) for calibrating in vivo measurement systems. The absolute calibration of in vivo measurement systems utilizes magnetic resonance imaging (MRI) to define physiological structure, size, and composition. The MRI image provides a digitized representation of the physiological structure, which allows for any mathematical distribution of radionuclides within the body. Using Monte Carlo transport codes, the emission spectrum from the body is predicted. The in vivo measurement equipment is calibrated using the Monte Carlo code and adjusting for the intrinsic properties of the detection system. The calibration factors are verified using measurements of existing phantoms and previously obtained measurements of human volunteers. 8 refs

Magnetic Resonance Imaging (MRI) -- Head

Medline Plus

Full Text Available ... or patients with claustrophobia. Other MRI machines are open on the sides (open MRI). Open units are especially helpful for examining larger patients or those with claustrophobia. Newer open MRI units provide very high quality images for ...

Investigation on calibration parameter of mammography calibration facilities at MINT

International Nuclear Information System (INIS)

Asmaliza Hashim; Wan Hazlinda Ismail; Md Saion Salikin; Muhammad Jamal Md Isa; Azuhar Ripin; Norriza Mohd Isa

2004-01-01

A mammography calibration facility has been established in the Medical Physics Laboratory, Malaysian Institute for Nuclear Technology Research (MINT). The calibration facility is established at the national level mainly to provide calibration services for radiation measuring test instruments or test tools used in quality assurance programme in mammography, which is being implemented in Malaysia. One of the accepted parameters that determine the quality of a radiation beam is the homogeneity coefficient. It is determined from the values of the 1 st and 2 nd Half Value Layer (HVL). In this paper, the consistency of the mammography machine beam qualities that is available in MINT, is investigated and presented. For calibration purposes, five radiation qualities namely 23, 25, 28, 30 and 35 kV, selectable from the control panel of the X-ray machine is used. Important parameters that are set for this calibration facility are exposure time, tube current, focal spot to detector distance (FDD) and beam size at specific distance. The values of homogeneity coefficient of this laboratory for the past few years tip to now be presented in this paper. Backscatter radiations are also considered in this investigation. (Author)

Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls

Directory of Open Access Journals (Sweden)

Deanna eGreenstein

2012-06-01

Full Text Available Introduction: Multivariate machine learning methods can be used to classify groups of schizophrenia patients and controls using structural magnetic resonance imaging (MRI. However, machine learning methods to date have not been extended beyond classification and contemporaneously applied in a meaningful way to clinical measures. We hypothesized that brain measures would classify groups, and that increased likelihood of being classified as a patient using regional brain measures would be positively related to illness severity, developmental delays and genetic risk. Methods: Using 74 anatomic brain MRI sub regions and Random Forest, we classified 98 COS patients and 99 age, sex, and ethnicity-matched healthy controls. We also used Random Forest to determine the likelihood of being classified as a schizophrenia patient based on MRI measures. We then explored relationships between brain-based probability of illness and symptoms, premorbid development, and presence of copy number variation associated with schizophrenia. Results: Brain regions jointly classified COS and control groups with 73.7% accuracy. Greater brain-based probability of illness was associated with worse functioning (p= 0.0004 and fewer developmental delays (p=0.02. Presence of copy number variation (CNV was associated with lower probability of being classified as schizophrenia (p=0.001. The regions that were most important in classifying groups included left temporal lobes, bilateral dorsolateral prefrontal regions, and left medial parietal lobes. Conclusions: Schizophrenia and control groups can be well classified using Random Forest and anatomic brain measures, and brain-based probability of illness has a positive relationship with illness severity and a negative relationship with developmental delays/problems and CNV-based risk.

Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

Science.gov (United States)

Dolz, Jose; Laprie, Anne; Ken, Soléakhéna; Leroy, Henri-Arthur; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

2016-01-01

To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

Machine Learning and Radiology

Science.gov (United States)

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Estimation of uncertainty of measurements of 3D mechanisms after kinematic calibration

International Nuclear Information System (INIS)

Takamasu, K; Sato, O; Shimojima, K; Takahashi, S; Furutani, R

2005-01-01

Calibration methods for 3D mechanisms are necessary to use the mechanisms as coordinate measuring machines. The calibration method of coordinate measuring machine using artifacts, the artifact calibration method, is proposed in taking account of traceability of the mechanism. There are kinematic parameters and form-deviation parameters in geometric parameters for describing the forward kinematic of the mechanism. In this article, the estimation methods of uncertainties using the calibrated coordinate measuring machine after the calibration are formulated. Firstly, the calculation method which takes out the values of kinematic parameters using least squares method is formulated. Secondly, the estimation value of uncertainty of the measuring machine is calculated using the error propagation method

Machine tool evaluation

International Nuclear Information System (INIS)

Lunsford, B.E.

1976-01-01

Continued improvement in numerical control (NC) units and the mechanical components used in the construction of today's machine tools, necessitate the use of more precise instrumentation to calibrate and determine the capabilities of these systems. It is now necessary to calibrate most tape-control lathes to a tool-path positioning accuracy of +-300 microinches in the full slide travel and, on some special turning and boring machines, a capability of +-100 microinches must be achieved. The use of a laser interferometer to determine tool-path capabilities is described

NIRS report of utilization of MRI machine for research. Results in 2005

International Nuclear Information System (INIS)

2007-04-01

The report is an achievement of cooperative research and development by private and official facilities of the National Institute of Radiological Sciences (NIRS) MRI machine, and its applied and medical uses in 2005. Contained are the reports on the magnet (1 topic), RF coils (5), basic studies on measurements (11), biological studies on measurements (4) and clinical studies on measurements (18), which are finally summarized in the list of the personnel, event calendar and published scientific papers. The basic studies by the MRI involve those of digestive tract's movement, samples for radiation dose measurements using polymer gel materials, disposition tracing of 5-FU by 19F chemical shift images, 19F images at 3T, 3T MRS, 13C measurement at 7T MR, spectrum at 7T MR, development of measurement system for elastic modulus distribution in living tissues, measurement of biological function by 170 MRI and measurement of acetylcholinesterase in brain. Biological studies involve those of the brain damage by heavy particle irradiation, functional brain mapping of monkey's abstract operation using PET, multiple sclerosis mice and development of a new cardiac function evaluation method. Clinical studies involve those of blood vessel coupling of cerebral nerve, micro-imaging of human eye, autopsy imaging, numerical phantom of human body, measurements of physiological parameters of brain, measurement of sugar metabolism function, radiation therapy evaluation method of brain tumors, metabolism analysis by 7T MR spectroscopy, statistical test of AChE activity, measurement of beta-amyloid in brain by Pittsburgh Compound-B, brain function, development of longitudinal relaxation time calculation software (T1Wizard), GSH in schizophrenia, evaluation method of forms and functions of hearts, occlusive arterial disease of lower extremity, MRI image of prostate using 3.0T, cholangiography, elucidation of activation mechanism of higher brain network by occlusal chew stimulation and MR

Machine learning and radiology.

Science.gov (United States)

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

Self-Calibrated In-Process Photogrammetry for Large Raw Part Measurement and Alignment before Machining.

Science.gov (United States)

Mendikute, Alberto; Yagüe-Fabra, José A; Zatarain, Mikel; Bertelsen, Álvaro; Leizea, Ibai

2017-09-09

Photogrammetry methods are being used more and more as a 3D technique for large scale metrology applications in industry. Optical targets are placed on an object and images are taken around it, where measuring traceability is provided by precise off-process pre-calibrated digital cameras and scale bars. According to the 2D target image coordinates, target 3D coordinates and camera views are jointly computed. One of the applications of photogrammetry is the measurement of raw part surfaces prior to its machining. For this application, post-process bundle adjustment has usually been adopted for computing the 3D scene. With that approach, a high computation time is observed, leading in practice to time consuming and user dependent iterative review and re-processing procedures until an adequate set of images is taken, limiting its potential for fast, easy-to-use, and precise measurements. In this paper, a new efficient procedure is presented for solving the bundle adjustment problem in portable photogrammetry. In-process bundle computing capability is demonstrated on a consumer grade desktop PC, enabling quasi real time 2D image and 3D scene computing. Additionally, a method for the self-calibration of camera and lens distortion has been integrated into the in-process approach due to its potential for highest precision when using low cost non-specialized digital cameras. Measurement traceability is set only by scale bars available in the measuring scene, avoiding the uncertainty contribution of off-process camera calibration procedures or the use of special purpose calibration artifacts. The developed self-calibrated in-process photogrammetry has been evaluated both in a pilot case scenario and in industrial scenarios for raw part measurement, showing a total in-process computing time typically below 1 s per image up to a maximum of 2 s during the last stages of the computed industrial scenes, along with a relative precision of 1/10,000 (e.g. 0.1 mm error in 1 m) with

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

Science.gov (United States)

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our

Model calibration and beam control systems for storage rings

International Nuclear Information System (INIS)

Corbett, W.J.; Lee, M.J.; Ziemann, V.

1993-04-01

Electron beam storage rings and linear accelerators are rapidly gaining worldwide popularity as scientific devices for the production of high-brightness synchrotron radiation. Today, everybody agrees that there is a premium on calibrating the storage ring model and determining errors in the machine as soon as possible after the beam is injected. In addition, the accurate optics model enables machine operators to predictably adjust key performance parameters, and allows reliable identification of new errors that occur during operation of the machine. Since the need for model calibration and beam control systems is common to all storage rings, software packages should be made that are portable between different machines. In this paper, we report on work directed toward achieving in-situ calibration of the optics model, detection of alignment errors, and orbit control techniques, with an emphasis on developing a portable system incorporating these tools

Investigation of a novel x-ray tube for the calibration of the x-ray crystal spectrometer in the KSTAR machine

International Nuclear Information System (INIS)

Bak, J.G.; Lee, S.G.

2007-01-01

A novel x-ray tube with a line filament has been developed for the in-situ calibration of the x-ray crystal spectrometer (XCS) in the KSTAR machine. The characteristics of the x-ray tube are investigated from the x-ray images obtained by using a pinhole and a CCD detector. It is found that the image has the width of about 0.1 mm, which is much improved as compared with the previous experimental results. In addition, there is a uniform region around the center of the image within its full length of 13.5 mm. This work may lead to the development of a novel x-ray tube with a line focus, which is required for the calibration of the XCS. Experimental results from the investigation of the x-ray tube are presented and the technical issues in a design of the in-situ calibration system using the x-ray tube for the KSTAR XCS are discussed. (author)

Testing the ghost with the machine

International Nuclear Information System (INIS)

De Zubicaray, G.

2002-01-01

Since its introduction during the 1990s, functional magnetic resonance imaging (fMRI) has been used to investigate brain activity occurring during a bewildering variety of sensory, motor and cognitive tasks. That is, a machine is being used to test 'the ghost in the machine' - the human mind. The use of imaging techniques to investigate these issues has even led to the emergence of a new scientific field called cognitive neuroscience. Currently, there are only a few groups in Australia actively publishing fMRI studies in the international literature, and the majority of these laboratories are clustered on the east coast. My own research with fMRI has focused on areas such as language and memory, with a special interest in how we solve competitive processes in our thinking

Power Doppler signal calibration between ultrasound machines by use of a capillary-flow phantom for pannus vascularity in rheumatoid finger joints: a basic study.

Science.gov (United States)

Sakano, Ryosuke; Kamishima, Tamotsu; Nishida, Mutsumi; Horie, Tatsunori

2015-01-01

Ultrasound allows the detection and grading of inflammation in rheumatology. Despite these advantages of ultrasound in the management of rheumatoid patients, it is well known that there are significant machine-to-machine disagreements regarding signal quantification. In this study, we tried to calibrate the power Doppler (PD) signal of two models of ultrasound machines by using a capillary-flow phantom. After flow velocity analysis in the perfusion cartridge at various injection rates (0.1-0.5 ml/s), we measured the signal count in the perfusion cartridge at various injection rates and pulse repetition frequencies (PRFs) by using PD, perfusing an ultrasound micro-bubble contrast agent diluted with normal saline simulating human blood. By use of the data from two models of ultrasound machines, Aplio 500 (Toshiba) and Avius (Hitachi Aloka), the quantitative PD (QPD) index [the summation of the colored pixels in a 1 cm × 1 cm rectangular region of interest (ROI)] was calculated via Image J (internet free software). We found a positive correlation between the injection rate and the flow velocity. In Aplio 500 and Avius, we found negative correlations between the PRF and the QPD index when the flow velocity was constant, and a positive correlation between flow velocity and the QPD index at constant PRF. The equation for the relationship of the PRF between Aplio 500 and Avius was: y = 0.023x + 0.36 [y = PRF of Avius (kHz), x = PRF of Aplio 500 (kHz)]. Our results suggested that the signal calibration of various models of ultrasound machines is possible by adjustment of the PRF setting.

Bench calibration of INDUS-2 beam position indicators

International Nuclear Information System (INIS)

Tyagi, Y.; Banerji, Anil; Kotaiah, S.

2005-01-01

A third generation synchrotron radiation source of energy 2.5 GeV named INDUS-2 at Centre for Advanced Technology (C.A.T), Indore (M.P) is in the advanced stage of construction. Accurate determination and correction of beam closed orbit in INDUS-2 machine within 100 of microns is a very desirable goal. Bench based calibration of Beam Position Indicators (BPI) play a very important and useful role during initial commissioning of electron machines. To precisely measure transverse position of electron beam in the Indus-2 storage ring, 56 Beam Position Indicators (BPI) will be installed in INDUS-2 machine. Out of 56 Beam Position Indicators 40 are of individual type whereas 16 are integrated with dipole vacuum chamber. The Beam Position Indicators are required to be calibrated before they can be installed. The calibration is done to determine electrical offset with respect to defined mechanical centre, to determine displacement sensitivities as well as non linearity's of BPI. Ideally when beam passes through the geometrical center of BPI's, all electrodes should have same signal strength. However due to different capacitance of electrodes and offset and drift in electronics, the electrical centre (mechanical x, y where all electrodes shows same signal strength) differs from mechanical centre of BPI. A fully automatic calibration system has been developed to carry out the calibration of Beam Position Indicators. A calibration software has been developed which has necessary utilities to process and display calibration data and results. This paper describes the calibration results of Indus-2 BPM. (author)

Machine learning based analytics of micro-MRI trabecular bone microarchitecture and texture in type 1 Gaucher disease.

Science.gov (United States)

Sharma, Gulshan B; Robertson, Douglas D; Laney, Dawn A; Gambello, Michael J; Terk, Michael

2016-06-14

Type 1 Gaucher disease (GD) is an autosomal recessive lysosomal storage disease, affecting bone metabolism, structure and strength. Current bone assessment methods are not ideal. Semi-quantitative MRI scoring is unreliable, not standardized, and only evaluates bone marrow. DXA BMD is also used but is a limited predictor of bone fragility/fracture risk. Our purpose was to measure trabecular bone microarchitecture, as a biomarker of bone disease severity, in type 1 GD individuals with different GD genotypes and to apply machine learning based analytics to discriminate between GD patients and healthy individuals. Micro-MR imaging of the distal radius was performed on 20 type 1 GD patients and 10 healthy controls (HC). Fifteen stereological and textural measures (STM) were calculated from the MR images. General linear models demonstrated significant differences between GD and HC, and GD genotypes. Stereological measures, main contributors to the first two principal components (PCs), explained ~50% of data variation and were significantly different between males and females. Subsequent PCs textural measures were significantly different between GD patients and HC individuals. Textural measures also significantly differed between GD genotypes, and distinguished between GD patients with normal and pathologic DXA scores. PCA and SVM predictive analyses discriminated between GD and HC with maximum accuracy of 73% and area under ROC curve of 0.79. Trabecular STM differences can be quantified between GD patients and HC, and GD sub-types using micro-MRI and machine learning based analytics. Work is underway to expand this approach to evaluate GD disease burden and treatment efficacy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using Active Learning for Speeding up Calibration in Simulation Models.

Science.gov (United States)

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Left ventricular volume measurement in mice by conductance catheter: evaluation and optimization of calibration

DEFF Research Database (Denmark)

Nielsen, Jan Møller; Kristiansen, Steen B; Ringgaard, Steffen

2007-01-01

in mice (n = 52) with a Millar CC (SPR-839) and compared with MRI-derived volumes (V(MRI)). Significant correlations between V(CC) and V(MRI) [end-diastolic volume (EDV): R(2) = 0.85, P 2) = 0.88, P ... in the pulmonary artery was used to calibrate for parallel conductance and volume conversion was done by individual cylinder calibration. However, a significant underestimation was observed [EDV = -17.3 microl (-22.7 to -11.9 microl); ESV = -8.8 microl (-12.5 to -5.1 microl)]. Intravenous injection....... The dual-frequency method for estimation of parallel conductance failed to produce V(CC) that correlated with V(MRI). We conclude that selection of the calibration procedure for the CC has significant implications for the accuracy and precision of volume estimation and pressure-volume loop...

Towards real-time plan adaptation for MRI-guided radiotherapy

NARCIS (Netherlands)

Kontaxis, Charis

2017-01-01

The introduction of hybrid MRI and linear accelerator (MRI-linac) machines enables the online volumetric imaging during radiation delivery with the superior soft tissue contrast of the diagnostic quality MRI. In this context, conventional radiotherapy workflow will gradually transfer from an offline

Calibration of helical tomotherapy machine using EPR/alanine dosimetry.

Science.gov (United States)

Perichon, Nicolas; Garcia, Tristan; François, Pascal; Lourenço, Valérie; Lesven, Caroline; Bordy, Jean-Marc

2011-03-01

Current codes of practice for clinical reference dosimetry of high-energy photon beams in conventional radiotherapy recommend using a 10 x 10 cm2 square field, with the detector at a reference depth of 10 cm in water and 100 cm source to surface distance (SSD) (AAPM TG-51) or 100 cm source-to-axis distance (SAD) (IAEA TRS-398). However, the maximum field size of a helical tomotherapy (HT) machine is 40 x 5 cm2 defined at 85 cm SAD. These nonstandard conditions prevent a direct implementation of these protocols. The purpose of this study is twofold: To check the absorbed dose in water and dose rate calibration of a tomotherapy unit as well as the accuracy of the tomotherapy treatment planning system (TPS) calculations for a specific test case. Both topics are based on the use of electron paramagnetic resonance (EPR) using alanine as transfer dosimeter between the Laboratoire National Henri Becquerel (LNHB) 60Co-gamma-ray reference beam and the Institut Curie's HT beam. Irradiations performed in the LNHB reference 60Co-gamma-ray beam allowed setting up the calibration method, which was then implemented and tested at the LNHB 6 MV linac x-ray beam, resulting in a deviation of 1.6% (at a 1% standard uncertainty) relative to the reference value determined with the standard IAEA TRS-398 protocol. HT beam dose rate estimation shows a difference of 2% with the value stated by the manufacturer at a 2% standard uncertainty. A 4% deviation between measured dose and the calculation from the tomotherapy TPS was found. The latter was originated by an inadequate representation of the phantom CT-scan values and, consequently, mass densities within the phantom. This difference has been explained by the mass density values given by the CT-scan and used by the TPS which were not the true ones. Once corrected using Monte Carlo N-Particle simulations to validate the accuracy of this process, the difference between corrected TPS calculations and alanine measured dose values was then

A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.

Science.gov (United States)

Saha, Ashirbani; Harowicz, Michael R; Wang, Weiyao; Mazurowski, Maciej A

2018-05-01

To determine whether multivariate machine learning models of algorithmically assessed magnetic resonance imaging (MRI) features from breast cancer patients are associated with Oncotype DX (ODX) test recurrence scores. A set of 261 female patients with invasive breast cancer, pre-operative dynamic contrast enhanced magnetic resonance (DCE-MR) images and available ODX score at our institution was identified. A computer algorithm extracted a comprehensive set of 529 features from the DCE-MR images of these patients. The set of patients was divided into a training set and a test set. Using the training set we developed two machine learning-based models to discriminate (1) high ODX scores from intermediate and low ODX scores, and (2) high and intermediate ODX scores from low ODX scores. The performance of these models was evaluated on the independent test set. High against low and intermediate ODX scores were predicted by the multivariate model with AUC 0.77 (95% CI 0.56-0.98, p replacement of ODX with imaging alone.

Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Yang, Xiaofeng [Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia (United States); Wu, Ning; Cheng, Guanghui [Radiation Oncology, Jilin University, Chuangchun, Jilin (China); Zhou, Zhengyang [Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing (China); Yu, David S.; Beitler, Jonathan J.; Curran, Walter J. [Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia (United States); Liu, Tian, E-mail: tliu34@emory.edu [Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia (United States)

2014-12-01

Purpose: To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). Methods and Materials: The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RT MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Results: Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. Conclusions: We have validated

MRI of the Chest

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Full Text Available ... The magnetic field is produced by passing an electric current through wire coils in most MRI units. Other coils, located in the machine and in some cases, placed around the part ...

Identifying individuals at high risk of psychosis: predictive utility of Support Vector Machine using structural and functional MRI data

Directory of Open Access Journals (Sweden)

Isabel eValli

2016-04-01

Full Text Available The identification of individuals at high risk of developing psychosis is entirely based on clinical assessment, associated with limited predictive potential. There is therefore increasing interest in the development of biological markers that could be used in clinical practice for this purpose. We studied 25 individuals with an At Risk Mental State for psychosis and 25 healthy controls using structural MRI, and functional MRI in conjunction with a verbal memory task. Data were analysed using a standard univariate analysis, and with Support Vector Machine (SVM, a multivariate pattern recognition technique that enables statistical inferences to be made at the level of the individual, yielding results with high translational potential. The application of SVM to structural MRI data permitted the identification of individuals at high risk of psychosis with a sensitivity of 68% and a specificity of 76%, resulting in an accuracy of 72% (p<0.001. Univariate volumetric between-group differences did not reach statistical significance. In contrast, the univariate fMRI analysis identified between-group differences (p<0.05 corrected while the application of SVM to the same data did not. Since SVM is well suited at identifying the pattern of abnormality that distinguishes two groups, whereas univariate methods are more likely to identify regions that individually are most different between two groups, our results suggest the presence of focal functional abnormalities in the context of a diffuse pattern of structural abnormalities in individuals at high clinical risk of psychosis.

MRI of the Chest

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Full Text Available ... will hear and feel loud tapping or thumping sounds when the coils that generate the radiofrequency pulses ... use headphones to reduce the intensity of the sounds made by the MRI machine. You may be ...

MRI of the Chest

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Full Text Available ... it is useful to bring that to the attention of the scheduler before the exam and bring ... bore which can be more comfortable for larger size patients or patients with claustrophobia. Other MRI machines ...

Magnetic Resonance Imaging (MRI) -- Head

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Full Text Available ... The magnetic field is produced by passing an electric current through wire coils in most MRI units. Other coils, located in the machine and in some cases, placed around the part ...

Z-correction, a method for achieving ultraprecise self-calibration on large area coordinate measurement machines for photomasks

Science.gov (United States)

Ekberg, Peter; Stiblert, Lars; Mattsson, Lars

2014-05-01

High-quality photomasks are a prerequisite for the production of flat panel TVs, tablets and other kinds of high-resolution displays. During the past years, the resolution demand has become more and more accelerated, and today, the high-definition standard HD, 1920 × 1080 pixels2, is well established, and already the next-generation so-called ultra-high-definition UHD or 4K display is entering the market. Highly advanced mask writers are used to produce the photomasks needed for the production of such displays. The dimensional tolerance in X and Y on absolute pattern placement on these photomasks, with sizes of square meters, has been in the range of 200-300 nm (3σ), but is now on the way to be <150 nm (3σ). To verify these photomasks, 2D ultra-precision coordinate measurement machines are used with even tighter tolerance requirements. The metrology tool MMS15000 is today the world standard tool used for the verification of large area photomasks. This paper will present a method called Z-correction that has been developed for the purpose of improving the absolute X, Y placement accuracy of features on the photomask in the writing process. However, Z-correction is also a prerequisite for achieving X and Y uncertainty levels <90 nm (3σ) in the self-calibration process of the MMS15000 stage area of 1.4 × 1.5 m2. When talking of uncertainty specifications below 200 nm (3σ) of such a large area, the calibration object used, here an 8-16 mm thick quartz plate of size approximately a square meter, cannot be treated as a rigid body. The reason for this is that the absolute shape of the plate will be affected by gravity and will therefore not be the same at different places on the measurement machine stage when it is used in the self-calibration process. This mechanical deformation will stretch or compress the top surface (i.e. the image side) of the plate where the pattern resides, and therefore spatially deform the mask pattern in the X- and Y-directions. Errors due

Z-correction, a method for achieving ultraprecise self-calibration on large area coordinate measurement machines for photomasks

International Nuclear Information System (INIS)

Ekberg, Peter; Stiblert, Lars; Mattsson, Lars

2014-01-01

High-quality photomasks are a prerequisite for the production of flat panel TVs, tablets and other kinds of high-resolution displays. During the past years, the resolution demand has become more and more accelerated, and today, the high-definition standard HD, 1920 × 1080 pixels 2 , is well established, and already the next-generation so-called ultra-high-definition UHD or 4K display is entering the market. Highly advanced mask writers are used to produce the photomasks needed for the production of such displays. The dimensional tolerance in X and Y on absolute pattern placement on these photomasks, with sizes of square meters, has been in the range of 200–300 nm (3σ), but is now on the way to be <150 nm (3σ). To verify these photomasks, 2D ultra-precision coordinate measurement machines are used with even tighter tolerance requirements. The metrology tool MMS15000 is today the world standard tool used for the verification of large area photomasks. This paper will present a method called Z-correction that has been developed for the purpose of improving the absolute X, Y placement accuracy of features on the photomask in the writing process. However, Z-correction is also a prerequisite for achieving X and Y uncertainty levels <90 nm (3σ) in the self-calibration process of the MMS15000 stage area of 1.4 × 1.5 m 2 . When talking of uncertainty specifications below 200 nm (3σ) of such a large area, the calibration object used, here an 8–16 mm thick quartz plate of size approximately a square meter, cannot be treated as a rigid body. The reason for this is that the absolute shape of the plate will be affected by gravity and will therefore not be the same at different places on the measurement machine stage when it is used in the self-calibration process. This mechanical deformation will stretch or compress the top surface (i.e. the image side) of the plate where the pattern resides, and therefore spatially deform the mask pattern in the X- and Y

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

Science.gov (United States)

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

Regional Reproducibility of BOLD Calibration Parameter M, OEF and Resting-State CMRO2 Measurements with QUO2 MRI.

Directory of Open Access Journals (Sweden)

Isabelle Lajoie

Full Text Available The current generation of calibrated MRI methods goes beyond simple localization of task-related responses to allow the mapping of resting-state cerebral metabolic rate of oxygen (CMRO2 in micromolar units and estimation of oxygen extraction fraction (OEF. Prior to the adoption of such techniques in neuroscience research applications, knowledge about the precision and accuracy of absolute estimates of CMRO2 and OEF is crucial and remains unexplored to this day. In this study, we addressed the question of methodological precision by assessing the regional inter-subject variance and intra-subject reproducibility of the BOLD calibration parameter M, OEF, O2 delivery and absolute CMRO2 estimates derived from a state-of-the-art calibrated BOLD technique, the QUantitative O2 (QUO2 approach. We acquired simultaneous measurements of CBF and R2* at rest and during periods of hypercapnia (HC and hyperoxia (HO on two separate scan sessions within 24 hours using a clinical 3 T MRI scanner. Maps of M, OEF, oxygen delivery and CMRO2, were estimated from the measured end-tidal O2, CBF0, CBFHC/HO and R2*HC/HO. Variability was assessed by computing the between-subject coefficients of variation (bwCV and within-subject CV (wsCV in seven ROIs. All tests GM-averaged values of CBF0, M, OEF, O2 delivery and CMRO2 were: 49.5 ± 6.4 mL/100 g/min, 4.69 ± 0.91%, 0.37 ± 0.06, 377 ± 51 μmol/100 g/min and 143 ± 34 μmol/100 g/min respectively. The variability of parameter estimates was found to be the lowest when averaged throughout all GM, with general trends toward higher CVs when averaged over smaller regions. Among the MRI measurements, the most reproducible across scans was R2*0 (wsCVGM = 0.33% along with CBF0 (wsCVGM = 3.88% and R2*HC (wsCVGM = 6.7%. CBFHC and R2*HO were found to have a higher intra-subject variability (wsCVGM = 22.4% and wsCVGM = 16% respectively, which is likely due to propagation of random measurement errors, especially for CBFHC due to the

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

Science.gov (United States)

Zimmerman, Naomi; Presto, Albert A.; Kumar, Sriniwasa P. N.; Gu, Jason; Hauryliuk, Aliaksei; Robinson, Ellis S.; Robinson, Allen L.; Subramanian, R.

2018-01-01

Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that

Machine Learning Principles Can Improve Hip Fracture Prediction

DEFF Research Database (Denmark)

Kruse, Christian; Eiken, Pia; Vestergaard, Peter

2017-01-01

Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data.......89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an “xgbTree” model. Machine learning can improve hip fracture...... prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration....

Magnetic Resonance Imaging (MRI) -- Head

Medline Plus

Full Text Available ... will hear and feel loud tapping or thumping sounds when the coils that generate the radiofrequency pulses ... use headphones to reduce the intensity of the sounds made by the MRI machine. You may be ...

Magnetic Resonance Imaging (MRI) -- Head

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Full Text Available ... it is useful to bring that to the attention of the scheduler before the exam and bring ... bore which can be more comfortable for larger size patients or patients with claustrophobia. Other MRI machines ...

Machine learning and medical imaging

CERN Document Server

Shen, Dinggang; Sabuncu, Mert

2016-01-01

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, a...

An Individual Claims History Simulation Machine

Directory of Open Access Journals (Sweden)

Andrea Gabrielli

2018-03-01

Full Text Available The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic scenario generator that is based on real non-life insurance data. This stochastic simulation machine allows everyone to simulate their own synthetic insurance portfolio of individual claims histories and back-test thier preferred claims reserving method.

Calibration services for medical applications of radiation

Energy Technology Data Exchange (ETDEWEB)

DeWerd, L.A.

1993-12-31

Calibration services for the medical community applications of radiation involve measuring radiation precisely and having traceability to the National Institute of Standards and Technology (NIST). Radiation therapy applications involve the use of ionization chambers and electrometers for external beams and well-type ionization chamber systems as well as radioactive sources for brachytherapy. Diagnostic x-ray applications involve ionization chamber systems and devices to measure other parameters of the x-ray machine, such as non-invasive kVp meters. Calibration laboratories have been established to provide radiation calibration services while maintaining traceability to NIST. New radiation applications of the medical community spur investigation to provide the future calibration needs.

Calibration services for medical applications of radiation

International Nuclear Information System (INIS)

DeWerd, L.A.

1993-01-01

Calibration services for the medical community applications of radiation involve measuring radiation precisely and having traceability to the National Institute of Standards and Technology (NIST). Radiation therapy applications involve the use of ionization chambers and electrometers for external beams and well-type ionization chamber systems as well as radioactive sources for brachytherapy. Diagnostic x-ray applications involve ionization chamber systems and devices to measure other parameters of the x-ray machine, such as non-invasive kVp meters. Calibration laboratories have been established to provide radiation calibration services while maintaining traceability to NIST. New radiation applications of the medical community spur investigation to provide the future calibration needs

Individual dosimetry and calibration

International Nuclear Information System (INIS)

Hoefert, M.; Nielsen, M.

1996-01-01

In 1995 both the Individual Dosimetry and Calibration Sections worked under the condition of a status quo and concentrated fully on the routine part of their work. Nevertheless, the machine for printing the bar code which will be glued onto the film holder and hence identify the people when entering into high radiation areas was put into operation and most of the holders were equipped with the new identification. As far as the Calibration Section is concerned the project of the new source control system that is realized by the Technical Support Section was somewhat accelerated

Calibration of working standard ionization chambers and dose standardization

International Nuclear Information System (INIS)

Abd Elmahoud, A. A. B.

2011-01-01

Measurements were performed for the calibration of two working standard ionization chambers in the secondary standard dosimetry laboratory of Sudan. 600 cc cylindrical former type and 1800 cc cylindrical radical radiation protection level ionization chambers were calibrated against 1000 cc spherical reference standard ionization chamber. The chamber were calibrated at X-ray narrow spectrum series with beam energies ranged from (33-116 KeV) in addition to 1''3''7''Cs beam with 662 KeV energy. The chambers 0.6 cc and 0.3 cc therapy level ionization were used for dose standardization and beam output calibrations of cobalt-60 radiotherapy machine located at the National Cancer Institute, University of Gazira. Concerning beam output measurements for 6''0''Co radiotherapy machine, dosimetric measurements were performed in accordance with the relevant per IAEA dosimetry protocols TRS-277 and TRS-398. The kinetic energy released per unit mass in air (air kerma) were obtained by multiplying the corrected electrometer reading (nC/min) by the calibration factors (Gy/n C) of the chambers from given in the calibration certificate. The uncertainty of measurements of air kerma were calculated for the all ionization chambers (combined uncertainty) the calibration factors of these ionization chambers then were calculated by comparing the reading of air kerma of secondary standard ionization chambers to than from radical and farmer chambers. The result of calibration working standard ionization chambers showed different calibration factors ranged from 0.99 to 1.52 for different radiation energies and these differences were due to chambers response and specification. The absorbed dose to to water calculated for therapy ionization chamber using two code of practice TRS-277 and TRS-398 as beam output for 6''0''Co radiotherapy machine and it can be used as a reference for future beam output calibration in radiotherapy dosimetry. The measurement of absorbed dose to water showed that the

Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis and machine learning techniques

Science.gov (United States)

Rathore, Saima; Bakas, Spyridon; Akbari, Hamed; Shukla, Gaurav; Rozycki, Martin; Davatzikos, Christos

2018-02-01

There is mounting evidence that assessment of multi-parametric magnetic resonance imaging (mpMRI) profiles can noninvasively predict survival in many cancers, including glioblastoma. The clinical adoption of mpMRI as a prognostic biomarker, however, depends on its applicability in a multicenter setting, which is hampered by inter-scanner variations. This concept has not been addressed in existing studies. We developed a comprehensive set of within-patient normalized tumor features such as intensity profile, shape, volume, and tumor location, extracted from multicenter mpMRI of two large (npatients=353) cohorts, comprising the Hospital of the University of Pennsylvania (HUP, npatients=252, nscanners=3) and The Cancer Imaging Archive (TCIA, npatients=101, nscanners=8). Inter-scanner harmonization was conducted by normalizing the tumor intensity profile, with that of the contralateral healthy tissue. The extracted features were integrated by support vector machines to derive survival predictors. The predictors' generalizability was evaluated within each cohort, by two cross-validation configurations: i) pooled/scanner-agnostic, and ii) across scanners (training in multiple scanners and testing in one). The median survival in each configuration was used as a cut-off to divide patients in long- and short-survivors. Accuracy (ACC) for predicting long- versus short-survivors, for these configurations was ACCpooled=79.06% and ACCpooled=84.7%, ACCacross=73.55% and ACCacross=74.76%, in HUP and TCIA datasets, respectively. The hazard ratio at 95% confidence interval was 3.87 (2.87-5.20, P<0.001) and 6.65 (3.57-12.36, P<0.001) for HUP and TCIA datasets, respectively. Our findings suggest that adequate data normalization coupled with machine learning classification allows robust prediction of survival estimates on mpMRI acquired by multiple scanners.

Calibration assembly for nuclear reactor vessel inspection apparatus

International Nuclear Information System (INIS)

Elsner, H.J.

1981-01-01

A removable calibration assembly can be utilized to verify the angular mounting of transducers in an array employed in an ultrasonic inpsection apparatus, to calibrate one axis of movement of the array with reference to a starting point, or to measure and calibrate the speed per unit of distance of the transducer's ultrasonic beam in the operating medium. The calibration assembly includes both a large and small reflecting surface separated by known distances, and several large cones, the tips of which are machined or adjusted to angles at which certain of the transducers are to be mounted. Clamping means for securing the calibration assembly to the inspection apparatus at a predetermined orientation is provided

MRI of the Chest

Medline Plus

Full Text Available ... types of clips used for brain aneurysms some types of metal coils placed within blood vessels nearly all cardiac defibrillators and pacemakers You ... a preferred imaging test. MR imaging can assess blood flow without ... the opening of certain types of MRI machines. The presence of an implant ...

Procedures for the definitive calibration of radiotherapy equipment

International Nuclear Information System (INIS)

1993-01-01

Recommendations from the Institute of Physical Sciences in Medicine are given for the definition calibration of external beam radiotherapy treatment machines and radiation dose measuring equipment used in radiotherapy. (UK)

Early studies of instant-fMRI for routine examination

International Nuclear Information System (INIS)

Sakurai, Yuuki; Harada, Kuniaki; Nagahama, Hiroshi; Akatsuka, Yoshihiro; Shinozaki, Jun

2010-01-01

Authors are developing a low-burden, short-time acquisition method of functional magnetic resonance imaging (fMRI) with 3T machine, named ''Instant-fMRI'', aiming for its application to routine examinations, of which results of early studies on identification of the language hemisphere are reported. Subjects were 10 healthy volunteers (8 males, 2 females, mean age 34.2 y, 8 right-handers) and 5 right-hander patients with brain tumor (4 males, 1 female, mean age 50 y). The machine was GE Signa HDx 3.0T ver. 14, using 8 channel head coil. For Instant-fMRI, T1-weighted imaging sequence for mapping was in fast spoiled gradient recalled acquisition in the steady state (fSPGR) mode (scan time: 1 min 44 sec) and fMRI sequence, in GRE-EPI (scan time: 1 min), which thus required only about 3 min in total. Reference was defined to be the anterior-posterior commissure line, to which parallel sections involving centriciput and cerebellum were acquired. Rest (30 sec)-task (shiritori language game, 30 sec) cycle was to be one in instant-fMRI in contrast to three in the conventional fMRI. Volunteers received both instant-fMRI and conventional fMRI and patients, the former alone. Data were analyzed by GE Brain Wave PA. Right and left hemisphere of the left and right hander, respectively, was identified to be activated by instant-fMRI in 9 of 10 volunteers and in all patients, and by the conventional fMRI, in all volunteers. The instant-fMRI can be a useful examination of other brain functions as well as identifying the language field when acquisition parameters for desired diagnostic purpose are optimized. (T.T.)

IPEM Report No 84: Guidelines for the Testing and Calibration of Physiotherapy Ultrasound Machines

Energy Technology Data Exchange (ETDEWEB)

Duck, Francis A

2003-01-21

Institute of Physics and Engineering in Medicine York: IPEM (2001) 67 pp, 20.00 British Pounds, ISBN: 0-904181-98-7. This 67-page soft-cover report has been prepared by an expert group within the Institute of Physics and Engineering in Medicine (IPEM) to provide detailed guidance on the testing and calibration of ultrasound therapy devices. Physiotherapists have long used external energy sources to supplement massage and manipulation for musculo-skeletal therapy. Whilst, recently, there has been a general reduction in use of electrotherapy using radio-frequency radiation, microwaves and interferential devices, ultrasound therapy still remains central to physiotherapy practice. There is now robust evidence that, at least for some injuries, ultrasound accelerates the repair of injured tissue. This has been particularly well demonstrated for bone fracture repair. Managed therapy relies on well-calibrated radiation sources. Unfortunately, there has been less than adequate management of ultrasound output in the past. Several well-documented situations have occurred for which faulty ultrasound equipment has continued in clinical use, either generating no output at all, or, more seriously, operating at maximum power all the time. This report reminds those responsible for managing ultrasound equipment of the need for testing and calibration, and gives detailed advice on procedures for carrying this out. These details are contained in 12 annexes forming the majority of the text. Each annex presents a self-contained aspect of one aspect of testing or calibration, such as the measurement of acoustic power, or an example protocol for machine testing. Each is sufficiently detailed to allow novice technical staff to follow the recommended procedure, or to allow a department to plan to introduce ultrasound field calibration into its practice. Other annexes contain useful additional material, not included in the earlier IPSM Report 58. A procedure for the measurement of effective

MRI, geometric distortion of the image and stereotaxy

International Nuclear Information System (INIS)

Derosier, C.; Delegue, G.; Munier, T.; Pharaboz, C.; Cosnard, G.

1991-01-01

The MRI technology may be the starting-point of geometric distortion. The mathematical preciseness of a spatial location may be disturbed and alter the guidance of a MRI interventional act, especially in stereotactic brain biopsy. A review of the literature shows errors of 1 to 1.5 mm. Our results show an error of 0.16±0.66. The control of quality: homogeneity and calibration of magnetic-field gradients, permit an improve of the ballistic preciseness and give permission to realize the guidance of a stereotactic brain biopsy with the alone MRI

The KLOE online calibration system

International Nuclear Information System (INIS)

Pasqualucci, E.; Alexander, G.; Aloisio, A.

2001-01-01

Based on all the features of the KLOE online software, the online calibration system performs current calibration quality checking in real time and starts automatically new calibration procedures when needed. A calibration manager process controls the system, implementing the interface to the online system, receiving information from the run control and translating its state transitions to a separate state machine. It acts as a 'calibration run controller' and performs failure recovery when requested by a set of process checkers. The core of the system is a multi-threaded OO histogram server that receives histogramming commands by remote processes and operates on local ROOT histograms. A client library and C, fortran and C++ application interface libraries allow the user to connect and define his own histogram or read histograms owned by others using an book-like interface. Several calibration processes running in parallel in a distributed, multiplatform environment can fill the same histograms, allowing fast external information check. A monitor thread allow remote browsing for visual inspection. Pre-filtered data are read in non-privileged spy mode from the data acquisition system via the Kloe Integrated Dataflow. The main characteristics of the system are presented

Trends in magnetic resonance imaging. Technical trends in MRI, noise reduction and fast imaging

International Nuclear Information System (INIS)

Sugimoto, Hiroshi

2007-01-01

At MRI examination, patients suffer the machine noise and long tight lying as well as an oppressive feeling. This paper describes the technological efforts against the former two. The noise is generated from the force (thumb-ward) to vibrate the magnetic field gradient coil according to the left-hand rule. Authors have developed a MRI machine (Pianissimo) where the coil is placed in vacuum and its actual noise level is found reduced from 105 - 112 to 84 dB(A) at 1.5T. Fast imaging to shorten the imaging time is attained by combination of parallel imaging where MR signals are into multiple high frequency receiver coils, and the usual pulse sequence imaging, which results in the increased encoding in a given time. Together with these, MR angiography and diffusion weighted imaging of abdomen for cancer examination are becoming popular as an additional MRI diagnosis, also acceptable to patients. Future progress of MRI machines conceivably accompanies the unavoidable noise increase and possibly significant magnetic effects on human body, and efforts for their reduction will be continued at patients' viewpoint. (T.I.)

Machine learning based analysis of cardiovascular images

NARCIS (Netherlands)

Wolterink, JM

2017-01-01

Cardiovascular diseases (CVDs), including coronary artery disease (CAD) and congenital heart disease (CHD) are the global leading cause of death. Computed tomography (CT) and magnetic resonance imaging (MRI) allow non-invasive imaging of cardiovascular structures. This thesis presents machine

Machine for development impact tests in sports seats and similar

International Nuclear Information System (INIS)

Gonçalves, R M

2015-01-01

This paper describes the stages of development of a machine to perform impact tests in sport seats, seats for spectators and multiple seats. This includes reviews and recommendations for testing laboratories that have needs similar to the laboratory where unfolded this process.The machine was originally developed seeking to meet certain impact tests in accordance with the NBR15925 standards; 15878 and 16031. The process initially included the study of the rules and the election of the tests for which the machine could be developed and yet all reports and outcome of interaction with service providers and raw materials.For operating facility, it was necessary to set entirely the machine control, which included the concept of dialogue with operator, the design of the menu screens and the procedures for submission and registration of results. To ensure reliability in the process, the machine has been successfully calibrated according to the requirements of the Brazilian network of calibration.The criticism to this enterprise covers the technical and economic aspects involved and points out the main obstacles that were needed to overcome. (paper)

Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks.

Science.gov (United States)

Kasabov, Nikola K; Doborjeh, Maryam Gholami; Doborjeh, Zohreh Gholami

2017-04-01

This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1]. The method consists of several steps: mapping spatial coordinates of fMRI data into a 3-D SNN cube (SNNc) that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3-D SNNc of spatiotemporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimization; and 3-D visualization and model interpretation. Two benchmark case study problems and data are used to illustrate the proposed methodology-fMRI data collected from subjects when reading affirmative or negative sentences and another one-on reading a sentence or seeing a picture. The learned connections in the SNNc represent dynamic spatiotemporal relationships derived from the fMRI data. They can reveal new information about the brain functions under different conditions. The proposed methodology allows for the first time to analyze dynamic functional and structural connectivity of a learned SNN model from fMRI data. This can be used for a better understanding of brain activities and also for online generation of appropriate neurofeedback to subjects for improved brain functions. For example, in this paper, tracing the 3-D SNN model connectivity enabled us for the first time to capture prominent brain functional pathways evoked in language comprehension. We found stronger spatiotemporal interaction between left dorsolateral prefrontal cortex and left temporal while reading a negated sentence. This observation is obviously distinguishable from the patterns generated by either reading affirmative sentences or seeing pictures. The proposed NeuCube-based methodology offers also a superior classification accuracy

Magnetic Resonance Imaging (MRI) -- Head

Medline Plus

Full Text Available ... types of clips used for brain aneurysms some types of metal coils placed within blood vessels nearly all cardiac defibrillators and pacemakers You ... called MR angiography (MRA) provides detailed images of blood vessels in the ... the opening of certain types of MRI machines. The presence of an implant ...

A self-calibrating robot based upon a virtual machine model of parallel kinematics

DEFF Research Database (Denmark)

Pedersen, David Bue; Eiríksson, Eyþór Rúnar; Hansen, Hans Nørgaard

2016-01-01

A delta-type parallel kinematics system for Additive Manufacturing has been created, which through a probing system can recognise its geometrical deviations from nominal and compensate for these in the driving inverse kinematic model of the machine. Novelty is that this model is derived from...... a virtual machine of the kinematics system, built on principles from geometrical metrology. Relevant mathematically non-trivial deviations to the ideal machine are identified and decomposed into elemental deviations. From these deviations, a routine is added to a physical machine tool, which allows...

Predicting subject-driven actions and sensory experience in a virtual world with relevance vector machine regression of fMRI data.

Science.gov (United States)

Valente, Giancarlo; De Martino, Federico; Esposito, Fabrizio; Goebel, Rainer; Formisano, Elia

2011-05-15

In this work we illustrate the approach of the Maastricht Brain Imaging Center to the PBAIC 2007 competition, where participants had to predict, based on fMRI measurements of brain activity, subject driven actions and sensory experience in a virtual world. After standard pre-processing (slice scan time correction, motion correction), we generated rating predictions based on linear Relevance Vector Machine (RVM) learning from all brain voxels. Spatial and temporal filtering of the time series was optimized rating by rating. For some of the ratings (e.g. Instructions, Hits, Faces, Velocity), linear RVM regression was accurate and very consistent within and between subjects. For other ratings (e.g. Arousal, Valence) results were less satisfactory. Our approach ranked overall second. To investigate the role of different brain regions in ratings prediction we generated predictive maps, i.e. maps of the weighted contribution of each voxel to the predicted rating. These maps generally included (but were not limited to) "specialized" regions which are consistent with results from conventional neuroimaging studies and known functional neuroanatomy. In conclusion, Sparse Bayesian Learning models, such as RVM, appear to be a valuable approach to the multivariate regression of fMRI time series. The implementation of the Automatic Relevance Determination criterion is particularly suitable and provides a good generalization, despite the limited number of samples which is typically available in fMRI. Predictive maps allow disclosing multi-voxel patterns of brain activity that predict perceptual and behavioral subjective experience. Copyright © 2010 Elsevier Inc. All rights reserved.

Replica calibration artefacts for optical 3D scanning of micro parts

DEFF Research Database (Denmark)

De Chiffre, Leonardo; Carmignato, S.; Cantatore, Angela

2009-01-01

This work deals with development of calibration artefacts produced by using hard replica materials, achieving high quality geometrical reproduction of suitable reference artefacts, high stability, and high surface cooperativeness. An investigation was carried out using a replica material for dental...... applications to reproduce the geometry of a step artefact, a miniature step gauge, and a curve standard for optical measuring machines. The replica artefacts were calibrated using a tactile coordinate measuring machine and measured on two different optical scanners. Replication quality and applicability...... of the artefacts to verify the accuracy of optical measurements as well as thermal expansion coefficient and stability of the replica artefacts over time were documented....

Support vector machine-based classification of neuroimages in Alzheimer’s disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals

Directory of Open Access Journals (Sweden)

Luiz K. Ferreira

2017-10-01

Full Text Available Objective: To conduct the first support vector machine (SVM-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI, F-fluorodeoxyglucose-positron emission tomography (FDG-PET and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT in Alzheimer’s disease (AD. Method: Brain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20 and healthy elderly controls (n=18. SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation. Results: The accuracy obtained using PET and SPECT data were similar. PET accuracy was 68∼71% and area under curve (AUC 0.77∼0.81; SPECT accuracy was 68∼74% and AUC 0.75∼0.79, and both had better performance than analysis with T1-MRI data (accuracy of 58%, AUC 0.67. The addition of PET or SPECT to MRI produced higher accuracy indices (68∼74%; AUC: 0.74∼0.82 than T1-MRI alone, but these were not clearly superior to the isolated neurofunctional modalities. Conclusion: In line with previous evidence, FDG-PET and rCBF-SPECT more accurately identified patients with AD than T1-MRI, and the addition of either PET or SPECT to T1-MRI data yielded increased accuracy. The comparable SPECT and PET performances, directly demonstrated for the first time in the present study, support the view that rCBF-SPECT still has a role to play in AD diagnosis.

Binary pressure-sensitive paint measurements using miniaturised, colour, machine vision cameras

Science.gov (United States)

Quinn, Mark Kenneth

2018-05-01

Recent advances in machine vision technology and capability have led to machine vision cameras becoming applicable for scientific imaging. This study aims to demonstrate the applicability of machine vision colour cameras for the measurement of dual-component pressure-sensitive paint (PSP). The presence of a second luminophore component in the PSP mixture significantly reduces its inherent temperature sensitivity, increasing its applicability at low speeds. All of the devices tested are smaller than the cooled CCD cameras traditionally used and most are of significantly lower cost, thereby increasing the accessibility of such technology and techniques. Comparisons between three machine vision cameras, a three CCD camera, and a commercially available specialist PSP camera are made on a range of parameters, and a detailed PSP calibration is conducted in a static calibration chamber. The findings demonstrate that colour machine vision cameras can be used for quantitative, dual-component, pressure measurements. These results give rise to the possibility of performing on-board dual-component PSP measurements in wind tunnels or on real flight/road vehicles.

Evaluating Statistical Process Control (SPC) techniques and computing the uncertainty of force calibrations

Science.gov (United States)

Navard, Sharon E.

1989-01-01

In recent years there has been a push within NASA to use statistical techniques to improve the quality of production. Two areas where statistics are used are in establishing product and process quality control of flight hardware and in evaluating the uncertainty of calibration of instruments. The Flight Systems Quality Engineering branch is responsible for developing and assuring the quality of all flight hardware; the statistical process control methods employed are reviewed and evaluated. The Measurement Standards and Calibration Laboratory performs the calibration of all instruments used on-site at JSC as well as those used by all off-site contractors. These calibrations must be performed in such a way as to be traceable to national standards maintained by the National Institute of Standards and Technology, and they must meet a four-to-one ratio of the instrument specifications to calibrating standard uncertainty. In some instances this ratio is not met, and in these cases it is desirable to compute the exact uncertainty of the calibration and determine ways of reducing it. A particular example where this problem is encountered is with a machine which does automatic calibrations of force. The process of force calibration using the United Force Machine is described in detail. The sources of error are identified and quantified when possible. Suggestions for improvement are made.

Breast cancer molecular subtype classifier that incorporates MRI features.

Science.gov (United States)

Sutton, Elizabeth J; Dashevsky, Brittany Z; Oh, Jung Hun; Veeraraghavan, Harini; Apte, Aditya P; Thakur, Sunitha B; Morris, Elizabeth A; Deasy, Joseph O

2016-07-01

To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes. This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.4%), ERPR-/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test. Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122-129. © 2016 Wiley Periodicals, Inc.

Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data

DEFF Research Database (Denmark)

Madsen, Kristoffer Hougaard; Krohne, Laerke G; Cai, Xin-Lu

2018-01-01

Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While...... the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning....... We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives....

Calibration Device Designed for proof ring used in SCC Experiment

Science.gov (United States)

Hu, X. Y.; Kang, Z. Y.; Yu, Y. L.

2017-11-01

In this paper, a calibration device for proof ring used in SCC (Stress Corrosion Cracking) experiment was designed. A compact size loading device was developed to replace traditional force standard machine or a long screw nut. The deformation of the proof ring was measured by a CCD (Charge-Coupled Device) during the calibration instead of digital caliper or a dial gauge. The calibration device was verified at laboratory that the precision of force loading is ±0.1% and the precision of deformation measurement is ±0.002mm.

Design and calibration of a scanning tunneling microscope for large machined surfaces

Energy Technology Data Exchange (ETDEWEB)

Grigg, D.A.; Russell, P.E.; Dow, T.A.

1988-12-01

During the last year the large sample STM has been designed, built and used for the observation of several different samples. Calibration of the scanner for prope dimensional interpretation of surface features has been a chief concern, as well as corrections for non-linear effects such as hysteresis during scans. Several procedures used in calibration and correction of piezoelectric scanners used in the laboratorys STMs are described.

Organizing Knowing in Interdisciplinary MRI Practice

DEFF Research Database (Denmark)

Yoshinaka, Yutaka

's CT-scan and X-ray machines, were to take on MRI scanning, on an albeit rotational basis. Opening up to a broader group of operators to the scanning practice was to allow for organizational flexibility and a broader basis for competence building among radiology staff, where different occupational......This paper addresses organizational knowledge practices pertaining to the interdisciplinary work of Magnetic Resonance Imaging (MRI) at a hospital radiology department. The setting occasions an interesting venue for exploring domestication of MRI as it unfolds in distributed settings of collective......, facilitating, and yet, in tension with, efforts at organizational transformation – its occasioning(s), mediations and contingent effects. The case study is based on direct observation and interviews, exploring and drawing upon the idea of different units of analysis as a methodological means to address...

Study of on-machine error identification and compensation methods for micro machine tools

International Nuclear Information System (INIS)

Wang, Shih-Ming; Yu, Han-Jen; Lee, Chun-Yi; Chiu, Hung-Sheng

2016-01-01

Micro machining plays an important role in the manufacturing of miniature products which are made of various materials with complex 3D shapes and tight machining tolerance. To further improve the accuracy of a micro machining process without increasing the manufacturing cost of a micro machine tool, an effective machining error measurement method and a software-based compensation method are essential. To avoid introducing additional errors caused by the re-installment of the workpiece, the measurement and compensation method should be on-machine conducted. In addition, because the contour of a miniature workpiece machined with a micro machining process is very tiny, the measurement method should be non-contact. By integrating the image re-constructive method, camera pixel correction, coordinate transformation, the error identification algorithm, and trajectory auto-correction method, a vision-based error measurement and compensation method that can on-machine inspect the micro machining errors and automatically generate an error-corrected numerical control (NC) program for error compensation was developed in this study. With the use of the Canny edge detection algorithm and camera pixel calibration, the edges of the contour of a machined workpiece were identified and used to re-construct the actual contour of the work piece. The actual contour was then mapped to the theoretical contour to identify the actual cutting points and compute the machining errors. With the use of a moving matching window and calculation of the similarity between the actual and theoretical contour, the errors between the actual cutting points and theoretical cutting points were calculated and used to correct the NC program. With the use of the error-corrected NC program, the accuracy of a micro machining process can be effectively improved. To prove the feasibility and effectiveness of the proposed methods, micro-milling experiments on a micro machine tool were conducted, and the results

Absolute calibration of in vivo measurement systems using magnetic resonance imaging and Monte Carlo computations

International Nuclear Information System (INIS)

Mallett, M.W.

1991-01-01

Lawrence Livermore National Laboratory (LLNL) is currently investigating a new method for obtaining absolute calibration factors for radiation measurement systems used to measure internally deposited radionuclides in vivo. This method uses magnetic resonance imaging (MRI) to determine the anatomical makeup of an individual. A new MRI technique is also employed that is capable of resolving the fat and water content of the human tissue. This anatomical and biochemical information is used to model a mathematical phantom. Monte Carlo methods are then used to simulate the transport of radiation throughout the phantom. By modeling the detection equipment of the in vivo measurement system into the code, calibration factors are generated that are specific to the individual. Furthermore, this method eliminates the need for surrogate human structures in the calibration process. A demonstration of the proposed method is being performed using a fat/water matrix

Base for a remote quality control system for magnetic resonance images machines

International Nuclear Information System (INIS)

Gonzalez Dalmau, Evelio R; Cabal Mirabal, Carlos; Noda Guerra, Manuel

2014-01-01

The medical images systems convert characteristic of the tissues in gray levels or color, using a physical method and a specific mathematical transformation. In Magnetic Resonance Images (MRI) these levels have a multi-parametric dependence, this a reason of their strong presence in the daily clinical practice. This technological complexity, the high costs and the importance that have these study for the patient's life, confer to the Quality Control (QC) human, technological, economic and juridical implications. Several international groups dedicated to the QC in MRI and diversity of approaches to carry out the tests of acceptance and periodic control of the quality exist. The characterization is habitually carried out, with global methods that don't allow a detailed quantitative parametric study. A novel system of quantitative control was developed based on quantitative describers by slices and temporal. This system is formed for: 1) standard methodology of acquisition of the experimental data, 2) subsystem of functions and programs developed in MatLab, 3) subsystem of graphics and reports, and 4) the expert. It is used successfully in the characterization and the periodic control of MRI machines of several magnetic fields in Cuba and in Venezuela. They were defined and established quantitative descriptors for MRI machines. The software flexibility allows carry out the QC to any machine facilitating the standardization and its use in multi-center studies. The retrospective and predictive value of the system was demonstrated. They feel the bases for the remote realization of the test

An Introduction to Normalization and Calibration Methods in Functional MRI

Science.gov (United States)

Liu, Thomas T.; Glover, Gary H.; Mueller, Bryon A.; Greve, Douglas N.; Brown, Gregory G.

2013-01-01

In functional magnetic resonance imaging (fMRI), the blood oxygenation level dependent (BOLD) signal is often interpreted as a measure of neural activity. However, because the BOLD signal reflects the complex interplay of neural, vascular, and metabolic processes, such an interpretation is not always valid. There is growing evidence that changes…

Nano Mechanical Machining Using AFM Probe

Science.gov (United States)

Mostofa, Md. Golam

and burr formations through intermittent cutting. Combining the AFM probe based machining with vibration-assisted machining enhanced nano mechanical machining processes by improving the accuracy, productivity and surface finishes. In this study, several scratching tests are performed with a single crystal diamond AFM probe to investigate the cutting characteristics and model the ploughing cutting forces. Calibration of the probe for lateral force measurements, which is essential, is also extended through the force balance method. Furthermore, vibration-assisted machining system is developed and applied to fabricate different materials to overcome some of the limitations of the AFM probe based single point nano mechanical machining. The novelty of this study includes the application of vibration-assisted AFM probe based nano scale machining to fabricate micro/nano scale features, calibration of an AFM by considering different factors, and the investigation of the nano scale material removal process from a different perspective.

Comparison of phase-constrained parallel MRI approaches: Analogies and differences.

Science.gov (United States)

Blaimer, Martin; Heim, Marius; Neumann, Daniel; Jakob, Peter M; Kannengiesser, Stephan; Breuer, Felix A

2016-03-01

Phase-constrained parallel MRI approaches have the potential for significantly improving the image quality of accelerated MRI scans. The purpose of this study was to investigate the properties of two different phase-constrained parallel MRI formulations, namely the standard phase-constrained approach and the virtual conjugate coil (VCC) concept utilizing conjugate k-space symmetry. Both formulations were combined with image-domain algorithms (SENSE) and a mathematical analysis was performed. Furthermore, the VCC concept was combined with k-space algorithms (GRAPPA and ESPIRiT) for image reconstruction. In vivo experiments were conducted to illustrate analogies and differences between the individual methods. Furthermore, a simple method of improving the signal-to-noise ratio by modifying the sampling scheme was implemented. For SENSE, the VCC concept was mathematically equivalent to the standard phase-constrained formulation and therefore yielded identical results. In conjunction with k-space algorithms, the VCC concept provided more robust results when only a limited amount of calibration data were available. Additionally, VCC-GRAPPA reconstructed images provided spatial phase information with full resolution. Although both phase-constrained parallel MRI formulations are very similar conceptually, there exist important differences between image-domain and k-space domain reconstructions regarding the calibration robustness and the availability of high-resolution phase information. © 2015 Wiley Periodicals, Inc.

A Fabry-Perot Interferometry Based MRI-Compatible Miniature Uniaxial Force Sensor for Percutaneous Needle Placement

Science.gov (United States)

Shang, Weijian; Su, Hao; Li, Gang; Furlong, Cosme; Fischer, Gregory S.

2014-01-01

Robot-assisted surgical procedures, taking advantage of the high soft tissue contrast and real-time imaging of magnetic resonance imaging (MRI), are developing rapidly. However, it is crucial to maintain tactile force feedback in MRI-guided needle-based procedures. This paper presents a Fabry-Perot interference (FPI) based system of an MRI-compatible fiber optic sensor which has been integrated into a piezoelectrically actuated robot for prostate cancer biopsy and brachytherapy in 3T MRI scanner. The opto-electronic sensing system design was minimized to fit inside an MRI-compatible robot controller enclosure. A flexure mechanism was designed that integrates the FPI sensor fiber for measuring needle insertion force, and finite element analysis was performed for optimizing the correct force-deformation relationship. The compact, low-cost FPI sensing system was integrated into the robot and calibration was conducted. The root mean square (RMS) error of the calibration among the range of 0–10 Newton was 0.318 Newton comparing to the theoretical model which has been proven sufficient for robot control and teleoperation. PMID:25126153

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.

Science.gov (United States)

Schnyer, David M; Clasen, Peter C; Gonzalez, Christopher; Beevers, Christopher G

2017-06-30

Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n =25) and healthy controls (n =25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Development of the laser alignment system with PSD used for shaft calibration

Science.gov (United States)

Jiao, Guohua; Li, Yulin; Hu, Baowen

2006-02-01

Shaft calibration is an important technique during installation and maintenance of a rotating machine. It requires unique and high-precision measurement instruments with calculation capability, and relies on experience on heavy, high-speed, or high-temperature machines. A high-precision laser alignment system has been designed using PSD (Position Sensing Detector) to change traditional manual way of shaft calibration and to make the measurement easier and more accurate. The system is comprised of two small measuring units (Laser transmitter and detector) and a hand operated control unit or a PC. Such a laser alignment system has been used in some actual shaft alignment with offset resolution 1.5μm and angular resolution 0.1°.

Validation of calibration procedures for freeform parts on CMMs

DEFF Research Database (Denmark)

Savio, Enrico; De Chiffre, Leonardo

2003-01-01

The paper describes the validation of a new method for establishment of traceability of freeform measurements on coordinate measuring machines currently being considered for development as a new ISO standard. The method deals with calibration by: i) repeated measurements of a given uncalibrated...

Large-scale Machine Learning in High-dimensional Datasets

DEFF Research Database (Denmark)

Hansen, Toke Jansen

Over the last few decades computers have gotten to play an essential role in our daily life, and data is now being collected in various domains at a faster pace than ever before. This dissertation presents research advances in four machine learning fields that all relate to the challenges imposed...... are better at modeling local heterogeneities. In the field of machine learning for neuroimaging, we introduce learning protocols for real-time functional Magnetic Resonance Imaging (fMRI) that allow for dynamic intervention in the human decision process. Specifically, the model exploits the structure of f...

Propofol Drip Infusion Anesthesia for MRI Scanning: Two Case Reports

OpenAIRE

Sasao-Takano, Mami; Misumi, Kan; Suzuki, Masayuki; Kamiya, Yoko; Noguchi, Izumi; Kawahara, Hiroshi

2013-01-01

The magnetic resonance imaging (MRI) room is a special environment. The required intense magnetic fields create unique problems with the use of standard anesthesia machines, syringe pumps, and physiologic monitors. We have recently experienced 2 oral maxillofacial surgery cases requiring MRI: a 15-year-old boy with developmental disability and a healthy 5-year-old boy. The patients required complete immobilization during the scanning for obtaining high-quality images for the best diagnosis. A...

Classifying cognitive profiles using machine learning with privileged information in Mild Cognitive Impairment

Directory of Open Access Journals (Sweden)

Hanin Hamdan Alahmadi

2016-11-01

Full Text Available Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalised Matrix Learning Vector Quantization (GMLVQ classifiers to discriminate patients with Mild Cognitive Impairment (MCI from healthy controls based on their cognitive skills. Further, we adopted a ``Learning with privileged information'' approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants.MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls based on the learning performance and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on the learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1 when overall fMRI signal for structured stimuli is

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

Science.gov (United States)

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Evaluation of the accuracy of volume determination on the orbit and eyeball using MRI

International Nuclear Information System (INIS)

Chau, Anson; Fung, Karl; Yap, Maurice

2005-01-01

Purpose: This study reports a calibration carried out on phantoms simulating the orbit and eyeball to evaluate the accuracy of volumetric determination using MRI DICOM (Digital Imaging and Communication in Medicine) images. Methods: Ten tailor-made spherical silicon balls ranging in size from 5 to 14 cm 3 and 10 silicon moulds of orbits from 10 human dried skulls were used to simulate the eyes and orbits, respectively. The CISS (Constructive interference in steady state, TR/TE = 16/8 ms) T2-weighted sequence was taken using a Siemens MRI scanner. The volume of each phantom was computed and compared with the known physical volumes. Results: The computed and physical volumes were highly correlated for both eyeball (r = 0.997) and the orbit (r = 0.992) phantoms. Coefficients of variation of the computed and physical volumes were low. Consequently, it is possible to apply a calibration value to the computed volume to derive the physical volume with a high level of confidence. Conclusion: We conclude that with appropriate calibration, it is viable to use MRI DICOM images to derive the volume of the eyeball and the orbit

Evaluation of the accuracy of volume determination on the orbit and eyeball using MRI

Energy Technology Data Exchange (ETDEWEB)

Chau, Anson [Department of Optometry and Radiography, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (China); Fung, Karl [Department of Optometry and Radiography, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (China)]. E-mail: orkarl@polyu.edu.hk; Yap, Maurice [Department of Optometry and Radiography, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (China)

2005-02-01

Purpose: This study reports a calibration carried out on phantoms simulating the orbit and eyeball to evaluate the accuracy of volumetric determination using MRI DICOM (Digital Imaging and Communication in Medicine) images. Methods: Ten tailor-made spherical silicon balls ranging in size from 5 to 14 cm{sup 3} and 10 silicon moulds of orbits from 10 human dried skulls were used to simulate the eyes and orbits, respectively. The CISS (Constructive interference in steady state, TR/TE = 16/8 ms) T2-weighted sequence was taken using a Siemens MRI scanner. The volume of each phantom was computed and compared with the known physical volumes. Results: The computed and physical volumes were highly correlated for both eyeball (r = 0.997) and the orbit (r = 0.992) phantoms. Coefficients of variation of the computed and physical volumes were low. Consequently, it is possible to apply a calibration value to the computed volume to derive the physical volume with a high level of confidence. Conclusion: We conclude that with appropriate calibration, it is viable to use MRI DICOM images to derive the volume of the eyeball and the orbit.

Advanced energy conversion & mechatronics systems

NARCIS (Netherlands)

Lomonova, E.A.

2015-01-01

Ultra-high precision systems are encountered in high-tech industrial applications including semiconductor lithography equipment, pick-and-place machines for the manufacturing of electronic components, microsurgery equipment, MRI equipment and calibration devices in electron microscopes. The

Multicentre structural and functional MRI

OpenAIRE

Gountouna, Viktoria-Eleni

2014-01-01

Neuroimaging techniques are likely to continue to improve our understanding of the brain in health and disease, but studies tend to be small, based in one imaging centre and of uncertain generalisability. Multicentre imaging studies therefore have great appeal but it is not yet clear under which circumstances data from different scanners can be combined. The successful harmonisation of multiple Magnetic Resonance Imaging (MRI) machines will increase study power, flexibility and...

An Improved Optimization Method for the Relevance Voxel Machine

DEFF Research Database (Denmark)

Ganz, Melanie; Sabuncu, M. R.; Van Leemput, Koen

2013-01-01

In this paper, we will re-visit the Relevance Voxel Machine (RVoxM), a recently developed sparse Bayesian framework used for predicting biological markers, e.g., presence of disease, from high-dimensional image data, e.g., brain MRI volumes. The proposed improvement, called IRVoxM, mitigates the ...

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy.

Science.gov (United States)

Mani, Subramani; Chen, Yukun; Li, Xia; Arlinghaus, Lori; Chakravarthy, A Bapsi; Abramson, Vandana; Bhave, Sandeep R; Levy, Mia A; Xu, Hua; Yankeelov, Thomas E

2013-01-01

To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC.

Development and application of PET-MRI image fusion technology

International Nuclear Information System (INIS)

Song Jianhua; Zhao Jinhua; Qiao Wenli

2011-01-01

The emerging and growing in popularity of PET-CT scanner brings us the convenience and cognizes the advantages such as diagnosis, staging, curative effect evaluation and prognosis for malignant tumor. And the PET-MRI installing maybe a new upsurge when the machine gradually mature, because of the MRI examination without the radiation exposure and with the higher soft tissue resolution. This paper summarized the developing course of image fusion technology and some researches of clinical application about PET-MRI at present, in order to help people to understand the functions and know its wide application of the upcoming new instrument, mainly focuses the application on the central nervous system and some soft tissue lesions. And before PET-MRI popularization, people can still carry out some researches of various image fusion and clinical application on the current equipment. (authors)

Camera calibration based on the back projection process

Science.gov (United States)

Gu, Feifei; Zhao, Hong; Ma, Yueyang; Bu, Penghui

2015-12-01

Camera calibration plays a crucial role in 3D measurement tasks of machine vision. In typical calibration processes, camera parameters are iteratively optimized in the forward imaging process (FIP). However, the results can only guarantee the minimum of 2D projection errors on the image plane, but not the minimum of 3D reconstruction errors. In this paper, we propose a universal method for camera calibration, which uses the back projection process (BPP). In our method, a forward projection model is used to obtain initial intrinsic and extrinsic parameters with a popular planar checkerboard pattern. Then, the extracted image points are projected back into 3D space and compared with the ideal point coordinates. Finally, the estimation of the camera parameters is refined by a non-linear function minimization process. The proposed method can obtain a more accurate calibration result, which is more physically useful. Simulation and practical data are given to demonstrate the accuracy of the proposed method.

Calibration procedures of the Tore-Supra infrared endoscopes

Science.gov (United States)

Desgranges, C.; Jouve, M.; Balorin, C.; Reichle, R.; Firdaouss, M.; Lipa, M.; Chantant, M.; Gardarein, J. L.; Saille, A.; Loarer, T.

2018-01-01

Five endoscopes equipped with infrared cameras working in the medium infrared range (3-5 μm) are installed on the controlled thermonuclear fusion research device Tore-Supra. These endoscopes aim at monitoring the plasma facing components surface temperature to prevent their overheating. Signals delivered by infrared cameras through endoscopes are analysed and used on the one hand through a real time feedback control loop acting on the heating systems of the plasma to decrease plasma facing components surface temperatures when necessary, on the other hand for physics studies such as determination of the incoming heat flux . To ensure these two roles a very accurate knowledge of the absolute surface temperatures is mandatory. Consequently the infrared endoscopes must be calibrated through a very careful procedure. This means determining their transmission coefficients which is a delicate operation. Methods to calibrate infrared endoscopes during the shutdown period of the Tore-Supra machine will be presented. As they do not allow determining the possible transmittances evolution during operation an in-situ method is presented. It permits the validation of the calibration performed in laboratory as well as the monitoring of their evolution during machine operation. This is possible by the use of the endoscope shutter and a dedicated plasma scenario developed to heat it. Possible improvements of this method are briefly evoked.

Calibration of Cryogenic Thermometers for the LHC

CERN Document Server

Balle, Ch; Vauthier, N; Thermeau, JP

2008-01-01

6000 cryogenic temperature sensors of resistive type covering the range from room temperature down to 1.6 K are installed on the LHC machine. In order to meet the stringent requirements on temperature control of the superconducting magnets, each single sensor needs to be calibrated individually. In the framework of a special contribution, IPN (Institut de Physique Nucléaire) in Orsay, France built and operated a calibration facility with a throughput of 80 thermometers per week. After reception from the manufacturer, the thermometer is first assembled onto a support specific to the measurement environment, and then thermally cycled ten times and calibrated at least once from 1.6 to 300 K. The procedure for each of these interventions includes various measurements and the acquired data is recorded in an ORACLE®-database. Furthermore random calibrations on some samples are executed at CERN to crosscheck the coherence between the approximation data obtained by both IPN and CERN. In the range of 1.5 K to 30 K...

Small animal simultaneous PET/MRI: initial experiences in a 9.4 T microMRI

Energy Technology Data Exchange (ETDEWEB)

Maramraju, Sri Harsha; Ravindranath, Bosky; Vaska, Paul; Schlyer, David J [Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY (United States); Smith, S David; Schulz, Daniela [Medical Department, Brookhaven National Laboratory, Upton, NY (United States); Junnarkar, Sachin S; Rescia, Sergio [Instrumentation Division, Brookhaven National Laboratory, Upton, NY (United States); Stoll, Sean; Purschke, Martin L; Woody, Craig L [Physics Department, Brookhaven National Laboratory, Upton, NY (United States); Southekal, Sudeepti [Brigham and Women' s Hospital, Boston, MA (United States); Pratte, Jean-Francois, E-mail: schlyer@bnl.gov [Universite de Sherbrooke, Sherbrooke, Quebec (Canada)

2011-04-21

We developed a non-magnetic positron-emission tomography (PET) device based on the rat conscious animal PET that operates in a small-animal magnetic resonance imaging (MRI) scanner, thereby enabling us to carry out simultaneous PET/MRI studies. The PET detector comprises 12 detector blocks, each being a 4 x 8 array of lutetium oxyorthosilicate crystals (2.22 x 2.22 x 5 mm{sup 3}) coupled to a matching non-magnetic avalanche photodiode array. The detector blocks, housed in a plastic case, form a 38 mm inner diameter ring with an 18 mm axial extent. Custom-built MRI coils fit inside the positron-emission tomography (PET) device, operating in transceiver mode. The PET insert is integrated with a Bruker 9.4 T 210 mm clear-bore diameter MRI scanner. We acquired simultaneous PET/MR images of phantoms, of in vivo rat brain, and of cardiac-gated mouse heart using [{sup 11}C]raclopride and 2-deoxy-2-[{sup 18}F]fluoro-d-glucose PET radiotracers. There was minor interference between the PET electronics and the MRI during simultaneous operation, and small effects on the signal-to-noise ratio in the MR images in the presence of the PET, but no noticeable visual artifacts. Gradient echo and high-duty-cycle spin echo radio frequency (RF) pulses resulted in a 7% and a 28% loss in PET counts, respectively, due to high PET counts during the RF pulses that had to be gated out. The calibration of the activity concentration of PET data during MR pulsing is reproducible within less than 6%. Our initial results demonstrate the feasibility of performing simultaneous PET and MRI studies in adult rats and mice using the same PET insert in a small-bore 9.4 T MRI.

Exploration of machine learning techniques in predicting multiple sclerosis disease course

OpenAIRE

Zhao, Yijun; Healy, Brian C.; Rotstein, Dalia; Guttmann, Charles R. G.; Bakshi, Rohit; Weiner, Howard L.; Brodley, Carla E.; Chitnis, Tanuja

2017-01-01

Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 1693 CLIMB study patients were classified as increased EDSS?1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up. Results Baseline data...

Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI.

Science.gov (United States)

Parekh, Vishwa S; Jacobs, Michael A

2017-01-01

Radiomics deals with the high throughput extraction of quantitative textural information from radiological images that not visually perceivable by radiologists. However, the biological correlation between radiomic features and different tissues of interest has not been established. To that end, we present the radiomic feature mapping framework to generate radiomic MRI texture image representations called the radiomic feature maps (RFM) and correlate the RFMs with quantitative texture values, breast tissue biology using quantitative MRI and classify benign from malignant tumors. We tested our radiomic feature mapping framework on a retrospective cohort of 124 patients (26 benign and 98 malignant) who underwent multiparametric breast MR imaging at 3 T. The MRI parameters used were T1-weighted imaging, T2-weighted imaging, dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted imaging (DWI). The RFMs were computed by convolving MRI images with statistical filters based on first order statistics and gray level co-occurrence matrix features. Malignant lesions demonstrated significantly higher entropy on both post contrast DCE-MRI (Benign-DCE entropy: 5.72 ± 0.12, Malignant-DCE entropy: 6.29 ± 0.06, p  = 0.0002) and apparent diffusion coefficient (ADC) maps as compared to benign lesions (Benign-ADC entropy: 5.65 ± 0.15, Malignant ADC entropy: 6.20 ± 0.07, p  = 0.002). There was no significant difference between glandular tissue entropy values in the two groups. Furthermore, the RFMs from DCE-MRI and DWI demonstrated significantly different RFM curves for benign and malignant lesions indicating their correlation to tumor vascular and cellular heterogeneity respectively. There were significant differences in the quantitative MRI metrics of ADC and perfusion. The multiview IsoSVM model classified benign and malignant breast tumors with sensitivity and specificity of 93 and 85%, respectively, with an AUC of 0.91.

Using human brain activity to guide machine learning.

Science.gov (United States)

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Lactate quantification by proton magnetic resonance spectroscopy using a clinical MRI machine: a basic study

International Nuclear Information System (INIS)

Isobe, T.; Muraishi, H.; Matsumura, A.; Kawamura, H.; Shibata, Y.; Anno, I.; Minami, M.

2007-01-01

The purpose of this study was to establish quantification method of lactate concentration by proton magnetic resonance spectroscopy (MRS) carried out using a conventional 1.5-T MRI machine. We used a lactate phantom with known concentrations (1, 1.5, 3, 6, 12 and 14 mmol/L). As a clinical example, a patient with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) was evaluated. Proton MRS was carried out using a clinical 1.5-T super-conducting magnetic resonance whole-body system. Data were acquired by point resolved spectroscopy. A coupling constant of J = 7.35 Hz (2/7 = 272 ms) and two long in-phase echo time of 272 ms and 544 ms were used to calculate the T2 relaxation time. The tissue water signal was used as an internal standard to quantify lactate. The correlation coefficient R between the calculated lactate concentrations and the known concentration of lactate was 0.99 with a constant factor of 0.32 (1/3.14). In patients with MELAS, the lactate concentration measured by MRS was 6.2 mmol/kg wet weight, which is similar to the value obtained in previous studies. In the present study, we have established a reliable method for lactate quantification in a phantom study and have shown a sample of clinical case of MELAS

Thoughts turned into high-level commands: Proof-of-concept study of a vision-guided robot arm driven by functional MRI (fMRI) signals.

Science.gov (United States)

Minati, Ludovico; Nigri, Anna; Rosazza, Cristina; Bruzzone, Maria Grazia

2012-06-01

Previous studies have demonstrated the possibility of using functional MRI to control a robot arm through a brain-machine interface by directly coupling haemodynamic activity in the sensory-motor cortex to the position of two axes. Here, we extend this work by implementing interaction at a more abstract level, whereby imagined actions deliver structured commands to a robot arm guided by a machine vision system. Rather than extracting signals from a small number of pre-selected regions, the proposed system adaptively determines at individual level how to map representative brain areas to the input nodes of a classifier network. In this initial study, a median action recognition accuracy of 90% was attained on five volunteers performing a game consisting of collecting randomly positioned coloured pawns and placing them into cups. The "pawn" and "cup" instructions were imparted through four mental imaginery tasks, linked to robot arm actions by a state machine. With the current implementation in MatLab language the median action recognition time was 24.3s and the robot execution time was 17.7s. We demonstrate the notion of combining haemodynamic brain-machine interfacing with computer vision to implement interaction at the level of high-level commands rather than individual movements, which may find application in future fMRI approaches relevant to brain-lesioned patients, and provide source code supporting further work on larger command sets and real-time processing. Copyright © 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

Diffusion-weighted MRI of the liver—Interpretative pearls and pitfalls

International Nuclear Information System (INIS)

Culverwell, A.D.; Sheridan, M.B.; Guthrie, J.A.; Scarsbrook, A.F.

2013-01-01

Diffusion-weighted magnetic resonance imaging (DW MRI) is an established technique in neuroradiology and more recently has emerged as a useful adjunct to various oncological applications of MRI. It has an expanding role in the evaluation of liver lesions, offers higher detection rates for small lesions, and can increase confidence in differentiating between benign and malignant lesions. Other applications include assessment of tumour response to therapy, differentiating tumour from bland thrombus, and assessment of liver fibrosis. DW sequences can be performed on most modern MRI machines with relative ease, in a short time period and without the need for contrast medium. DW MRI can be of value in the detection and characterization of hepatic lesions but there are pitfalls, which can potentially cause interpretative difficulty. This article will review the rationale for DW MRI in liver imaging, demonstrate the clinical utility of the technique in a spectrum of hepatic diseases, and illustrate key interpretative pearls and pitfalls

Ray-based calibration for the micro optical metrology system

Science.gov (United States)

Yin, Yongkai; Wang, Meng; Li, Ameng; Liu, Xiaoli; Peng, Xiang

2014-05-01

Fringe projection 3D microscopy (FP-3DM) plays an important role in micro-machining and micro-fabrication. FP-3DM may be realized with quite different arrangements and principles, which make people confused to select an appropriate one for their specific application. This paper introduces the ray-based general imaging model to describe the FP-3DM, which has the potential to get a unified expression for different system arrangements. Meanwhile the dedicated calibration procedure is also presented to realize quantitative 3D imaging. The validity and accuracy of proposed calibration approach is demonstrated with experiments.

Machine-Specific Magnetic Resonance Imaging Quality Control Procedures for Stereotactic Radiosurgery Treatment Planning.

Science.gov (United States)

Fatemi, Ali; Taghizadeh, Somayeh; Yang, Claus Chunli; R Kanakamedala, Madhava; Morris, Bart; Vijayakumar, Srinivasan

2017-12-18

Purpose Magnetic resonance (MR) images are necessary for accurate contouring of intracranial targets, determination of gross target volume and evaluation of organs at risk during stereotactic radiosurgery (SRS) treatment planning procedures. Many centers use magnetic resonance imaging (MRI) simulators or regular diagnostic MRI machines for SRS treatment planning; while both types of machine require two stages of quality control (QC), both machine- and patient-specific, before use for SRS, no accepted guidelines for such QC currently exist. This article describes appropriate machine-specific QC procedures for SRS applications. Methods and materials We describe the adaptation of American College of Radiology (ACR)-recommended QC tests using an ACR MRI phantom for SRS treatment planning. In addition, commercial Quasar MRID 3D and Quasar GRID 3D phantoms were used to evaluate the effects of static magnetic field (B 0 ) inhomogeneity, gradient nonlinearity, and a Leksell G frame (SRS frame) and its accessories on geometrical distortion in MR images. Results QC procedures found in-plane distortions (Maximum = 3.5 mm, Mean = 0.91 mm, Standard deviation = 0.67 mm, >2.5 mm (%) = 2) in X-direction (Maximum = 2.51 mm, Mean = 0.52 mm, Standard deviation = 0.39 mm, > 2.5 mm (%) = 0) and in Y-direction (Maximum = 13. 1 mm , Mean = 2.38 mm, Standard deviation = 2.45 mm, > 2.5 mm (%) = 34) in Z-direction and < 1 mm distortion at a head-sized region of interest. MR images acquired using a Leksell G frame and localization devices showed a mean absolute deviation of 2.3 mm from isocenter. The results of modified ACR tests were all within recommended limits, and baseline measurements have been defined for regular weekly QC tests. Conclusions With appropriate QC procedures in place, it is possible to routinely obtain clinically useful MR images suitable for SRS treatment planning purposes. MRI examination for SRS planning can benefit from the improved localization and planning

Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

Directory of Open Access Journals (Sweden)

Ricardo Andres Pizarro

2016-12-01

Full Text Available High-resolution three-dimensional magnetic resonance imaging (3D-MRI is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM algorithm in the quality assessment of structural brain images, using global and region of interest (ROI automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

Science.gov (United States)

Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S

2016-01-01

High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

Methodological developments of low field MRI: Elasto-graphy, MRI-ultrasound interaction and dynamic nuclear polarization

International Nuclear Information System (INIS)

Madelin, Guillaume

2005-01-01

This thesis deals with two aspects of low field (0.2 T) Magnetic Resonance Imaging (MRI): the research of new contrasts due to the interaction between Nuclear Magnetic Resonance (NMR) and acoustics (elasto-graphy, spin-phonon interaction) and enhancement of the signal-to-noise ratio by Dynamic Nuclear Polarization (DNP). Magnetic Resonance Elasto-graphy (MRE) allows to assess some viscoelastic properties of tissues by visualization of the propagation of low frequency acoustic strain waves. A review on MRE is given, as well as a study on local measurement of the acoustic absorption coefficient. The next part is dedicated to MRI-ultrasound interaction. First, the ultrasonic transducer was calibrated for power and acoustic field using the comparison of two methods: the radiation force method (balance method) and laser interferometry. Then, we tried to modify the T1 contrast of tissues by spin-phonon interaction due to the application of ultrasound at the resonance frequency at 0.2 T, which is about 8.25 MHz. No modification of T1 contrast has been obtained, but the acoustic streaming phenomenon has been observed in liquids. MRI visualization of this streaming could make possible to calibrate transducers as well as to assess some mechanical properties of viscous fluids. The goal of the last part was to set up DNP experiments at 0.2 T in order to enhance the NMR signal. This double resonance method is based on the polarization transfer of unpaired electrons of free radicals to the surrounding protons of water. This transfer occurs by cross relaxation during the saturation of an electronic transition using Electronic Paramagnetic Resonance (EPR). Two EPR cavities operating at 5.43 GHz have been tested on oxo-TEMPO free radicals (nitroxide). An enhancement of the NMR signal by a factor 30 was obtained during these preliminary experiments. (author)

Robot calibration with a photogrammetric on-line system using reseau scanning cameras

Science.gov (United States)

Diewald, Bernd; Godding, Robert; Henrich, Andreas

1994-03-01

The possibility for testing and calibration of industrial robots becomes more and more important for manufacturers and users of such systems. Exacting applications in connection with the off-line programming techniques or the use of robots as measuring machines are impossible without a preceding robot calibration. At the LPA an efficient calibration technique has been developed. Instead of modeling the kinematic behavior of a robot, the new method describes the pose deviations within a user-defined section of the robot's working space. High- precision determination of 3D coordinates of defined path positions is necessary for calibration and can be done by digital photogrammetric systems. For the calibration of a robot at the LPA a digital photogrammetric system with three Rollei Reseau Scanning Cameras was used. This system allows an automatic measurement of a large number of robot poses with high accuracy.

Phased transducer array for acoustic energy harvesting inside an MRI machine

NARCIS (Netherlands)

Klymko, V.; Roes, M.G.L.; Duivenbode, van J.; Lomonova, E.

2013-01-01

In this study, an array of piezoelectric speakers is used to focus acoustic energy on a single transducer that acts as a harvester. The transmitting transducers are located along a curve that fits inside the magnetic resonance interferometer (MRI) torus interior. The numerical results for the

Study on on-machine defects measuring system on high power laser optical elements

Science.gov (United States)

Luo, Chi; Shi, Feng; Lin, Zhifan; Zhang, Tong; Wang, Guilin

2017-10-01

The influence of surface defects on high power laser optical elements will cause some harm to the performances of imaging system, including the energy consumption and the damage of film layer. To further increase surface defects on high power laser optical element, on-machine defects measuring system was investigated. Firstly, the selection and design are completed by the working condition analysis of the on-machine defects detection system. By designing on processing algorithms to realize the classification recognition and evaluation of surface defects. The calibration experiment of the scratch was done by using the self-made standard alignment plate. Finally, the detection and evaluation of surface defects of large diameter semi-cylindrical silicon mirror are realized. The calibration results show that the size deviation is less than 4% that meet the precision requirement of the detection of the defects. Through the detection of images the on-machine defects detection system can realize the accurate identification of surface defects.

Model Calibration of Exciter and PSS Using Extended Kalman Filter

Energy Technology Data Exchange (ETDEWEB)

Kalsi, Karanjit; Du, Pengwei; Huang, Zhenyu

2012-07-26

Power system modeling and controls continue to become more complex with the advent of smart grid technologies and large-scale deployment of renewable energy resources. As demonstrated in recent studies, inaccurate system models could lead to large-scale blackouts, thereby motivating the need for model calibration. Current methods of model calibration rely on manual tuning based on engineering experience, are time consuming and could yield inaccurate parameter estimates. In this paper, the Extended Kalman Filter (EKF) is used as a tool to calibrate exciter and Power System Stabilizer (PSS) models of a particular type of machine in the Western Electricity Coordinating Council (WECC). The EKF-based parameter estimation is a recursive prediction-correction process which uses the mismatch between simulation and measurement to adjust the model parameters at every time step. Numerical simulations using actual field test data demonstrate the effectiveness of the proposed approach in calibrating the parameters.

Comparison of three methods for the calibration of cobalt-60 teletherapy machine

International Nuclear Information System (INIS)

Adewole, O.O.; Akinlade, B.I.; Oyekunle, O.E.; Ejeh, J.

2011-01-01

Two methods of indirect determination of dose rate (machine output) from the Cobalt-60 Teletherapy machines has been reviewed and compared with conventional measurement with dosimetry devices. The dose rate were determined by: (i) Conventional measurement (ii) Application of the law of radioactive decay and (iii) Assumption of 1 % radioactive decomposition per month. The dose rate at the depth of maximum dose (Z m ax), collimator size of 10cm x 10cm and Source to Skin Distance (SSD) of 80cm obtained from these methods were 203.7200cGy/min, 203.8090cGy/min and 203.9530cGy/min respectively. The ratio of dose rate obtained from measurement to that from calculations is within the tolerance value of 2%.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.

Science.gov (United States)

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2015-11-01

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease. Copyright © 2015 International Federation of Clinical Neurophysiology. All rights reserved.

Mathematical calibration procedure of a capacitive sensor-based indexed metrology platform

International Nuclear Information System (INIS)

Brau-Avila, A; Valenzuela-Galvan, M; Herrera-Jimenez, V M; Santolaria, J; Aguilar, J J; Acero, R

2017-01-01

The demand for faster and more reliable measuring tasks for the control and quality assurance of modern production systems has created new challenges for the field of coordinate metrology. Thus, the search for new solutions in coordinate metrology systems and the need for the development of existing ones still persists. One example of such a system is the portable coordinate measuring machine (PCMM), the use of which in industry has considerably increased in recent years, mostly due to its flexibility for accomplishing in-line measuring tasks as well as its reduced cost and operational advantages compared to traditional coordinate measuring machines. Nevertheless, PCMMs have a significant drawback derived from the techniques applied in the verification and optimization procedures of their kinematic parameters. These techniques are based on the capture of data with the measuring instrument from a calibrated gauge object, fixed successively in various positions so that most of the instrument measuring volume is covered, which results in time-consuming, tedious and expensive verification and optimization procedures. In this work the mathematical calibration procedure of a capacitive sensor-based indexed metrology platform (IMP) is presented. This calibration procedure is based on the readings and geometric features of six capacitive sensors and their targets with nanometer resolution. The final goal of the IMP calibration procedure is to optimize the geometric features of the capacitive sensors and their targets in order to use the optimized data in the verification procedures of PCMMs. (paper)

Mathematical calibration procedure of a capacitive sensor-based indexed metrology platform

Science.gov (United States)

Brau-Avila, A.; Santolaria, J.; Acero, R.; Valenzuela-Galvan, M.; Herrera-Jimenez, V. M.; Aguilar, J. J.

2017-03-01

The demand for faster and more reliable measuring tasks for the control and quality assurance of modern production systems has created new challenges for the field of coordinate metrology. Thus, the search for new solutions in coordinate metrology systems and the need for the development of existing ones still persists. One example of such a system is the portable coordinate measuring machine (PCMM), the use of which in industry has considerably increased in recent years, mostly due to its flexibility for accomplishing in-line measuring tasks as well as its reduced cost and operational advantages compared to traditional coordinate measuring machines. Nevertheless, PCMMs have a significant drawback derived from the techniques applied in the verification and optimization procedures of their kinematic parameters. These techniques are based on the capture of data with the measuring instrument from a calibrated gauge object, fixed successively in various positions so that most of the instrument measuring volume is covered, which results in time-consuming, tedious and expensive verification and optimization procedures. In this work the mathematical calibration procedure of a capacitive sensor-based indexed metrology platform (IMP) is presented. This calibration procedure is based on the readings and geometric features of six capacitive sensors and their targets with nanometer resolution. The final goal of the IMP calibration procedure is to optimize the geometric features of the capacitive sensors and their targets in order to use the optimized data in the verification procedures of PCMMs.

Modeling and Calibration of a Novel One-Mirror Galvanometric Laser Scanner

Directory of Open Access Journals (Sweden)

Chengyi Yu

2017-01-01

Full Text Available A laser stripe sensor has limited application when a point cloud of geometric samples on the surface of the object needs to be collected, so a galvanometric laser scanner is designed by using a one-mirror galvanometer element as its mechanical device to drive the laser stripe to sweep along the object. A novel mathematical model is derived for the proposed galvanometer laser scanner without any position assumptions and then a model-driven calibration procedure is proposed. Compared with available model-driven approaches, the influence of machining and assembly errors is considered in the proposed model. Meanwhile, a plane-constraint-based approach is proposed to extract a large number of calibration points effectively and accurately to calibrate the galvanometric laser scanner. Repeatability and accuracy of the galvanometric laser scanner are evaluated on the automobile production line to verify the efficiency and accuracy of the proposed calibration method. Experimental results show that the proposed calibration approach yields similar measurement performance compared with a look-up table calibration method.

Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.

Science.gov (United States)

Hojjati, Seyed Hani; Ebrahimzadeh, Ata; Khazaee, Ali; Babajani-Feremi, Abbas

2017-04-15

We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI). Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features. Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD. To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC. Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion. Copyright © 2017 Elsevier B.V. All rights reserved.

The impact of inspired oxygen levels on calibrated fMRI measurements of M, OEF and resting CMRO2 using combined hypercapnia and hyperoxia.

Science.gov (United States)

Lajoie, Isabelle; Tancredi, Felipe B; Hoge, Richard D

2017-01-01

Recent calibrated fMRI techniques using combined hypercapnia and hyperoxia allow the mapping of resting cerebral metabolic rate of oxygen (CMRO2) in absolute units, oxygen extraction fraction (OEF) and calibration parameter M (maximum BOLD). The adoption of such technique necessitates knowledge about the precision and accuracy of the model-derived parameters. One of the factors that may impact the precision and accuracy is the level of oxygen provided during periods of hyperoxia (HO). A high level of oxygen may bring the BOLD responses closer to the maximum M value, and hence reduce the error associated with the M interpolation. However, an increased concentration of paramagnetic oxygen in the inhaled air may result in a larger susceptibility area around the frontal sinuses and nasal cavity. Additionally, a higher O2 level may generate a larger arterial blood T1 shortening, which require a bigger cerebral blood flow (CBF) T1 correction. To evaluate the impact of inspired oxygen levels on M, OEF and CMRO2 estimates, a cohort of six healthy adults underwent two different protocols: one where 60% of O2 was administered during HO (low HO or LHO) and one where 100% O2 was administered (high HO or HHO). The QUantitative O2 (QUO2) MRI approach was employed, where CBF and R2* are simultaneously acquired during periods of hypercapnia (HC) and hyperoxia, using a clinical 3 T scanner. Scan sessions were repeated to assess repeatability of results at the different O2 levels. Our T1 values during periods of hyperoxia were estimated based on an empirical ex-vivo relationship between T1 and the arterial partial pressure of O2. As expected, our T1 estimates revealed a larger T1 shortening in arterial blood when administering 100% O2 relative to 60% O2 (T1LHO = 1.56±0.01 sec vs. T1HHO = 1.47±0.01 sec, P < 4*10-13). In regard to the susceptibility artifacts, the patterns and number of affected voxels were comparable irrespective of the O2 concentration. Finally, the model

The impact of inspired oxygen levels on calibrated fMRI measurements of M, OEF and resting CMRO2 using combined hypercapnia and hyperoxia.

Directory of Open Access Journals (Sweden)

Isabelle Lajoie

Full Text Available Recent calibrated fMRI techniques using combined hypercapnia and hyperoxia allow the mapping of resting cerebral metabolic rate of oxygen (CMRO2 in absolute units, oxygen extraction fraction (OEF and calibration parameter M (maximum BOLD. The adoption of such technique necessitates knowledge about the precision and accuracy of the model-derived parameters. One of the factors that may impact the precision and accuracy is the level of oxygen provided during periods of hyperoxia (HO. A high level of oxygen may bring the BOLD responses closer to the maximum M value, and hence reduce the error associated with the M interpolation. However, an increased concentration of paramagnetic oxygen in the inhaled air may result in a larger susceptibility area around the frontal sinuses and nasal cavity. Additionally, a higher O2 level may generate a larger arterial blood T1 shortening, which require a bigger cerebral blood flow (CBF T1 correction. To evaluate the impact of inspired oxygen levels on M, OEF and CMRO2 estimates, a cohort of six healthy adults underwent two different protocols: one where 60% of O2 was administered during HO (low HO or LHO and one where 100% O2 was administered (high HO or HHO. The QUantitative O2 (QUO2 MRI approach was employed, where CBF and R2* are simultaneously acquired during periods of hypercapnia (HC and hyperoxia, using a clinical 3 T scanner. Scan sessions were repeated to assess repeatability of results at the different O2 levels. Our T1 values during periods of hyperoxia were estimated based on an empirical ex-vivo relationship between T1 and the arterial partial pressure of O2. As expected, our T1 estimates revealed a larger T1 shortening in arterial blood when administering 100% O2 relative to 60% O2 (T1LHO = 1.56±0.01 sec vs. T1HHO = 1.47±0.01 sec, P < 4*10-13. In regard to the susceptibility artifacts, the patterns and number of affected voxels were comparable irrespective of the O2 concentration. Finally, the

Calibration Measurements of the LHC Beam Dumping System Extraction Kicker Magnets

CERN Document Server

Uythoven, J; Ducimetière, L; Goddard, B; Gräwer, G; Olivieri, F; Pereira, L; Vossenberg, Eugène B

2006-01-01

The LHC beam dumping system must protect the LHC machine from damage by reliably and safely extracting and absorbing the circulating beams when requested. Two sets of 15 extraction kicker magnets form the main active part of this system. They have been produced, tested and calibrated by measuring the integrated magnetic field and the magnet current at different beam energies. The calibration data have been analysed, and the critical parameters are compared with the specifications. Implications for the configuration, control and operation of the beam dumping system are discussed.

MRI in assessing children with learning disability, focal findings, and reduced automaticity.

Science.gov (United States)

Urion, David K; Huff, Hanalise V; Carullo, Maria Paulina

2015-08-18

In children with clinically diagnosed learning disabilities with focal findings on neurologic or neuropsychological evaluations, there is a hypothesized association between disorders in automaticity and focal structural abnormalities observed in brain MRIs. We undertook a retrospective analysis of cases referred to a tertiary-hospital-based learning disabilities program. Individuals were coded as having a focal deficit if either neurologic or neuropsychological evaluation demonstrated focal dysfunction. Those with abnormal MRI findings were categorized based on findings. Children with abnormalities from each of these categories were compared in terms of deficits in automaticity, as measured by the tasks of Rapid Automatized Naming, Rapid Alternating Stimulus Naming, or the timed motor performance battery from the Physical and Neurological Examination for Soft Signs. Data were compared in children with and without disorders of automaticity regarding type of brain structure abnormality. Of the 1,587 children evaluated, 127 had a focal deficit. Eighty-seven had a brain MRI (52 on 1.5-tesla machines and 35 on 3.0-tesla machines). Forty of these images were found to be abnormal. These children were compared with a clinic sample of 150 patients with learning disabilities and no focal findings on examination, who also had undergone MRI. Only 5 of the latter group had abnormalities on MRI. Reduced verbal automaticity was associated with cerebellar abnormalities, whereas reduced automaticity on motor or motor and verbal tasks was associated with white matter abnormalities. Reduced automaticity of retrieval and slow timed motor performance appear to be highly associated with MRI findings. © 2015 American Academy of Neurology.

Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.

Science.gov (United States)

De Looze, Céline; Beausang, Alan; Cryan, Jane; Loftus, Teresa; Buckley, Patrick G; Farrell, Michael; Looby, Seamus; Reilly, Richard; Brett, Francesca; Kearney, Hugh

2018-05-16

Machine learning methods have been introduced as a computer aided diagnostic tool, with applications to glioma characterisation on MRI. Such an algorithmic approach may provide a useful adjunct for a rapid and accurate diagnosis of a glioma. The aim of this study is to devise a machine learning algorithm that may be used by radiologists in routine practice to aid diagnosis of both: WHO grade and IDH mutation status in de novo gliomas. To evaluate the status quo, we interrogated the accuracy of neuroradiology reports in relation to WHO grade: grade II 96.49% (95% confidence intervals [CI] 0.88, 0.99); III 36.51% (95% CI 0.24, 0.50); IV 72.9% (95% CI 0.67, 0.78). We derived five MRI parameters from the same diagnostic brain scans, in under two minutes per case, and then supplied these data to a random forest algorithm. Machine learning resulted in a high level of accuracy in prediction of tumour grade: grade II/III; area under the receiver operating characteristic curve (AUC) = 98%, sensitivity = 0.82, specificity = 0.94; grade II/IV; AUC = 100%, sensitivity = 1.0, specificity = 1.0; grade III/IV; AUC = 97%, sensitivity = 0.83, specificity = 0.97. Furthermore, machine learning also facilitated the discrimination of IDH status: AUC of 88%, sensitivity = 0.81, specificity = 0.77. These data demonstrate the ability of machine learning to accurately classify diffuse gliomas by both WHO grade and IDH status from routine MRI alone-without significant image processing, which may facilitate usage as a diagnostic adjunct in clinical practice.

Automated Localization of Multiple Pelvic Bone Structures on MRI.

Science.gov (United States)

Onal, Sinan; Lai-Yuen, Susana; Bao, Paul; Weitzenfeld, Alfredo; Hart, Stuart

2016-01-01

In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are at present identified manually on MRI to locate reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures automatically. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this paper, we present a model that combines support vector machines and nonlinear regression capturing global and local information to automatically identify the bounding boxes of bone structures on MRI. The model identifies the location of the pelvic bone structures by establishing the association between their relative locations and using local information such as texture features. Results show that the proposed method is able to locate the bone structures of interest accurately (dice similarity index >0.75) in 87-91% of the images. This research aims to enable accurate, consistent, and fully automated localization of bone structures on MRI to facilitate and improve the diagnosis of health conditions such as female POP.

Propofol drip infusion anesthesia for MRI scanning: two case reports.

Science.gov (United States)

Sasao-Takano, Mami; Misumi, Kan; Suzuki, Masayuki; Kamiya, Yoko; Noguchi, Izumi; Kawahara, Hiroshi

2013-01-01

The magnetic resonance imaging (MRI) room is a special environment. The required intense magnetic fields create unique problems with the use of standard anesthesia machines, syringe pumps, and physiologic monitors. We have recently experienced 2 oral maxillofacial surgery cases requiring MRI: a 15-year-old boy with developmental disability and a healthy 5-year-old boy. The patients required complete immobilization during the scanning for obtaining high-quality images for the best diagnosis. Anesthesia was started in the MRI scanning room. An endotracheal intubation was performed after induction with intravenous administration of muscle relaxant. Total intravenous anesthesia via propofol drip infusion (4-7 mg/kg/h) was used during the scanning. Standard physiologic monitors were used during scan pauses, but special monitors were used during scanning. In MRI scanning for oral maxillofacial surgery, general anesthesia, with the added advantage of having a secured airway, is recommended as a safe alternative to sedation especially in cases of patients with disability and precooperative chidren.

Primary Central Nervous System Lymphoma: Incorporating MRI in the Planning of Treatment Strategies

International Nuclear Information System (INIS)

Eloraby, A.; Zaki, I.

2001-01-01

Primary lymphoma of the central nervous system is becoming increasingly encountered secondary to the acquired immune-deficiency disorders. MRI is rapidly evolving diagnostic tool in the management of the lymphomatous CNS primary infiltrates. Methods and materials: 40 patients of the National Cancer Institute of Cairo University were studied by medium and high power MRI machines before and after intra-venous contrast enhancement. Results: The cerebral lesions exhibited specific diagnostic criteria regarding the anatomical configuration, signal pattern, peri-focal oedema and response to steroids, such manifestations made. Conclusion: MRI a highly reliable tool in the management of the disease. The work proved that spinal cord primary lymphoma is a rare entity

Hydraulic Power Plant Machine Dynamic Diagnosis

Directory of Open Access Journals (Sweden)

Hans Günther Poll

2006-01-01

Full Text Available A method how to perform an entire structural and hydraulic diagnosis of prototype Francis power machines is presented and discussed in this report. Machine diagnosis of Francis units consists on a proper evaluation of acquired mechanical, thermal and hydraulic data obtained in different operating conditions of several rotary and non rotary machine components. Many different physical quantities of a Francis machine such as pressure, strains, vibration related data, water flow, air flow, position of regulating devices and displacements are measured in a synchronized way so that a relation of cause an effect can be developed for each operating condition and help one to understand all phenomena that are involved with such kind of machine. This amount of data needs to be adequately post processed in order to allow correct interpretation of the machine dynamics and finally these data must be compared with the expected calculated data not only to fine tuning the calculation methods but also to accomplish fully understanding of the influence of the water passages on such machines. The way how the power plant owner has to operate its Francis machines, many times also determined by a central dispatcher, has a high influence on the fatigue life time of the machine components. The diagnostic method presented in this report helps one to understand the importance of adequate operation to allow a low maintenance cost for the entire power plant. The method how to acquire these quantities is discussed in details together with the importance of correct sensor balancing, calibration and adequate correlation with the physical quantities. Typical results of the dynamic machine behavior, with adequate interpretation, obtained in recent measurement campaigns of some important hydraulic turbines were presented. The paper highlights the investigation focus of the hydraulic machine behavior and how to tailor the measurement strategy to accomplish all goals. Finally some

Transfer standard device to improve the traceable calibration of physiotherapy ultrasound machines

NARCIS (Netherlands)

Hekkenberg, R.T.; Richards, A.; Beissner, K.; Zeqiri, B.; Bezemer, R.A.; Hodnett, M.; Prout, G.; Cantrall, C.

2006-01-01

Ultrasound (US) physiotherapy as a clinical treatment is extremely common in the Western world. Internationally, regulation to ensure safe application of US physiotherapy by regular calibration ranges from nil to mandatory. The need for a portable power standard (PPS) has been addressed within a

Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision.

Science.gov (United States)

Tu, Junchao; Zhang, Liyan

2018-01-12

A new solution to the problem of galvanometric laser scanning (GLS) system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN）to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM). By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision

Directory of Open Access Journals (Sweden)

Junchao Tu

2018-01-01

Full Text Available A new solution to the problem of galvanometric laser scanning (GLS system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN）to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM. By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

Verification of optical coordinate measuring machines along the vertical measurement axis

DEFF Research Database (Denmark)

Morace, Renata Erica; Hansen, Hans Nørgaard; De Chiffre, Leonardo

2005-01-01

This paper deals with the performance verification of optical coordinate measuring machines (CMMs) equipped with video probes along the vertical measurement axis. The aim of this work was to investigate the capability of artefacts like gauge blocks and angle blocks for calibrating, verifying...

MRI EVALUATION OF INTERNAL DERANGEMENT OF KNEE

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Ashok Srikar Chowdhary

2018-01-01

Full Text Available BACKGROUND Internal derangement of knee means loss of normal knee function due to ligament or meniscal injuries. MRI is a routinely utilised noninvasive modality for evaluation of various knee disorders including internal derangement. MRI provides excellent soft tissue contrast and multiplanar images when compared to other musculoskeletal imaging modalities. The aim of the study is to study the demographic profile of patients presenting with internal derangement of knee, identify the various ligament and meniscal injuries causing internal derangement of knee and describe the MRI features of the ligament and meniscal injuries. MATERIALS AND METHODS This study was undertaken from January 2016 to mid-December 2017 in the Department of Radiodiagnosis, MVJ Medical College and Research Hospital, Hoskote. The study population consisted of 108 patients with internal derangement of knee who underwent MRI of knee. All the MRI scans of the knee in this study were performed using Siemens Magnetom Essenza (A Tim+Dot system MR machine with a 1.5 tesla field strength magnet using a flex coil. RESULTS The study population consisted of 108 patients comprising of 90 males and 18 females. The age of the patients ranged from 16 to 67 years. Majority of the patients belonged to the age group of 21-30 years constituting about 41% of the total study population. Anterior cruciate ligament injury was the commonest followed by medial and lateral meniscus tears. Flap tear was the commonest type of meniscal tear. Posterior horn of the meniscus was the commonest tear site. CONCLUSION MRI is the investigation of choice in evaluating internal derangement of knee. MRI can accurately diagnose ligament and meniscal injuries and guide arthroscopy.

A TMS coil positioning/holding system for MR image-guided TMS interleaved with fMRI.

Science.gov (United States)

Bohning, Daryl E; Denslow, S; Bohning, P A; Walker, J A; George, M S

2003-11-01

Transcranial magnetic stimulation (TMS) can be interleaved with fMRI to visualize regional brain activity in response to direct, non-invasive, cortical stimulation, making it a promising tool for studying brain function. A major practical difficulty is accurately positioning the TMS coil within the MRI scanner for stimulating a particular area of brain cortex. The objective of this work was to design and build a self-contained hardware/software system for MR-guided TMS coil positioning in interleaved TMS/fMRI studies. A compact, manually operated, articulated TMS coil positioner/holder with 6 calibrated degrees of freedom was developed for use inside a cylindrical RF head coil, along with a software package for transforming between MR image coordinates, MR scanner space coordinates, and positioner/holder settings. Phantom calibration studies gave an accuracy for positioning within setups of dx=+/-1.9 mm, dy=+/-1.4 mm, dz=+/-0.8 mm and a precision for multiple setups of dx=+/-0.8 mm, dy=+/-0.1 mm, dz=+/-0.1 mm. This self-contained, integrated MR-guided TMS system for interleaved TMS/fMRI studies provides fast, accurate location of motor cortex stimulation sites traditionally located functionally, and a means of consistent, anatomy-based TMS coil positioning for stimulation of brain areas without overt response.

Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning

Directory of Open Access Journals (Sweden)

Shuihua Wang

2016-10-01

Full Text Available (Aim Sensorineural hearing loss (SNHL is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. (Materials We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany. The subjects contain 14 patients with right-sided hearing loss (RHL, 15 patients with left-sided hearing loss (LHL, and 20 healthy controls (HC. (Method We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM. (Results The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. (Conclusions This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

Machine Learning for Neuroimaging with Scikit-Learn

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Alexandre eAbraham

2014-02-01

Full Text Available Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn.

Science.gov (United States)

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

Science.gov (United States)

Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M; Gaw, Nathan; Dueck, Amylou C; Smith, Kris A; Nakaji, Peter; Plasencia, Jonathan; Ranjbar, Sara; Price, Stephen J; Tran, Nhan; Loftus, Joseph; Jenkins, Robert; O'Neill, Brian P; Elmquist, William; Baxter, Leslie C; Gao, Fei; Frakes, David; Karis, John P; Zwart, Christine; Swanson, Kristin R; Sarkaria, Jann; Wu, Teresa; Mitchell, J Ross; Li, Jing

2015-01-01

Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs heterogeneity to identify regional tumor-rich biopsy targets.

EASYTRAC Project: Work package 6.4 Reversal technique to calibrate gear and thread standards

DEFF Research Database (Denmark)

Carmignato, Simone; De Chiffre, Leonardo; Larsen, Erik

This report was produced as a part of the project EASYTRAC, an EU project under the programme Competitive and Sustainable Growth: Contract No. G6RD-CT-2000-00188, coordinated by UNIMETRIK S.A. (Spain). The project is concerned with low uncertainty calibrations on coordinate measuring machines (CM...... (PTB) - Germany and Tampere University of Technology (TUT) - Finland. The present report describes feasibility and experimental results of a reversal and substitute element technique application for thread calibration on CMMs....

Standard practices for verification of displacement measuring systems and devices used in material testing machines

CERN Document Server

American Society for Testing and Materials. Philadelphia

2005-01-01

1.1 These practices cover procedures and requirements for the calibration and verification of displacement measuring systems by means of standard calibration devices for static and quasi-static testing machines. This practice is not intended to be complete purchase specifications for testing machines or displacement measuring systems. Displacement measuring systems are not intended to be used for the determination of strain. See Practice E83. 1.2 These procedures apply to the verification of the displacement measuring systems associated with the testing machine, such as a scale, dial, marked or unmarked recorder chart, digital display, etc. In all cases the buyer/owner/user must designate the displacement-measuring system(s) to be verified. 1.3 The values stated in either SI units or inch-pound units are to be regarded separately as standard. The values stated in each system may not be exact equivalents; therefore, each system shall be used independently of the other. Combining values from the two systems m...

Tracked ultrasound calibration studies with a phantom made of LEGO bricks

Science.gov (United States)

Soehl, Marie; Walsh, Ryan; Rankin, Adam; Lasso, Andras; Fichtinger, Gabor

2014-03-01

In this study, spatial calibration of tracked ultrasound was compared by using a calibration phantom made of LEGO® bricks and two 3-D printed N-wire phantoms. METHODS: The accuracy and variance of calibrations were compared under a variety of operating conditions. Twenty trials were performed using an electromagnetic tracking device with a linear probe and three trials were performed using varied probes, varied tracking devices and the three aforementioned phantoms. The accuracy and variance of spatial calibrations found through the standard deviation and error of the 3-D image reprojection were used to compare the calibrations produced from the phantoms. RESULTS: This study found no significant difference between the measured variables of the calibrations. The average standard deviation of multiple 3-D image reprojections with the highest performing printed phantom and those from the phantom made of LEGO® bricks differed by 0.05 mm and the error of the reprojections differed by 0.13 mm. CONCLUSION: Given that the phantom made of LEGO® bricks is significantly less expensive, more readily available, and more easily modified than precision-machined N-wire phantoms, it prompts to be a viable calibration tool especially for quick laboratory research and proof of concept implementations of tracked ultrasound navigation.

Dosimetry and Calibration Section

International Nuclear Information System (INIS)

Otto, T.

1999-01-01

The Dosimetry and Calibration Section fulfils two tasks within CERN's Radiation Protection Group: the Individual Dosimetry Service monitors more than 5000 persons potentially exposed to ionizing radiation on the CERN sites, and the Calibration Laboratory verifies throughout the year, at regular intervals, over 1000 instruments, monitors, and electronic dosimeters used by RP Group. The establishment of a Quality Assurance System for the Individual Dosimetry Service, a requirement of the new Swiss Ordinance for personal dosimetry, put a considerable workload on the section. Together with an external consultant it was decided to identify and then describe the different 'processes' of the routine work performed in the dosimetry service. The resulting Quality Manual was submitted to the Federal Office for Public Health in Bern in autumn. The CERN Individual Dosimetry Service will eventually be officially endorsed after a successful technical test in March 1999. On the technical side, the introduction of an automatic development machine for gamma films was very successful. It processes the dosimetric films without an operator being present, and its built-in regeneration mechanism keeps the concentration of the processing chemicals at a constant level

Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

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Shuibo Hu

2018-03-01

Full Text Available The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs, in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD high performance liquid chromatography (HPLC database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS, artificial neural networks (ANN, support vector machine (SVM and random forests (RF, and feature selection techniques, including genetic algorithm (GA, successive projection algorithm (SPA and recursive feature elimination based on support vector machine (SVM-RFE, for inferring PSCs from remote sensing data. Results showed that: (1 SVM-RFE worked better in selecting sensitive features; (2 RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3 machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4 sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5 the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing.

New algorithms for motion error detection of numerical control machine tool by laser tracking measurement on the basis of GPS principle

Science.gov (United States)

Wang, Jindong; Chen, Peng; Deng, Yufen; Guo, Junjie

2018-01-01

As a three-dimensional measuring instrument, the laser tracker is widely used in industrial measurement. To avoid the influence of angle measurement error on the overall measurement accuracy, the multi-station and time-sharing measurement with a laser tracker is introduced on the basis of the global positioning system (GPS) principle in this paper. For the proposed method, how to accurately determine the coordinates of each measuring point by using a large amount of measured data is a critical issue. Taking detecting motion error of a numerical control machine tool, for example, the corresponding measurement algorithms are investigated thoroughly. By establishing the mathematical model of detecting motion error of a machine tool with this method, the analytical algorithm concerning on base station calibration and measuring point determination is deduced without selecting the initial iterative value in calculation. However, when the motion area of the machine tool is in a 2D plane, the coefficient matrix of base station calibration is singular, which generates a distortion result. In order to overcome the limitation of the original algorithm, an improved analytical algorithm is also derived. Meanwhile, the calibration accuracy of the base station with the improved algorithm is compared with that with the original analytical algorithm and some iterative algorithms, such as the Gauss-Newton algorithm and Levenberg-Marquardt algorithm. The experiment further verifies the feasibility and effectiveness of the improved algorithm. In addition, the different motion areas of the machine tool have certain influence on the calibration accuracy of the base station, and the corresponding influence of measurement error on the calibration result of the base station depending on the condition number of coefficient matrix are analyzed.

Improvement of the repeatability of parallel transmission at 7T using interleaved acquisition in the calibration scan.

Science.gov (United States)

Kameda, Hiroyuki; Kudo, Kohsuke; Matsuda, Tsuyoshi; Harada, Taisuke; Iwadate, Yuji; Uwano, Ikuko; Yamashita, Fumio; Yoshioka, Kunihiro; Sasaki, Makoto; Shirato, Hiroki

2017-12-04

Respiration-induced phase shift affects B 0 /B 1 + mapping repeatability in parallel transmission (pTx) calibration for 7T brain MRI, but is improved by breath-holding (BH). However, BH cannot be applied during long scans. To examine whether interleaved acquisition during calibration scanning could improve pTx repeatability and image homogeneity. Prospective. Nine healthy subjects. 7T MRI with a two-channel RF transmission system was used. Calibration scanning for B 0 /B 1 + mapping was performed under sequential acquisition/free-breathing (Seq-FB), Seq-BH, and interleaved acquisition/FB (Int-FB) conditions. The B 0 map was calculated with two echo times, and the B 1 + map was obtained using the Bloch-Siegert method. Actual flip-angle imaging (AFI) and gradient echo (GRE) imaging were performed using pTx and quadrature-Tx (qTx). All scans were acquired in five sessions. Repeatability was evaluated using intersession standard deviation (SD) or coefficient of variance (CV), and in-plane homogeneity was evaluated using in-plane CV. A paired t-test with Bonferroni correction for multiple comparisons was used. The intersession CV/SDs for the B 0 /B 1 + maps were significantly smaller in Int-FB than in Seq-FB (Bonferroni-corrected P FB, Seq-BH, and qTx than in Seq-FB (Bonferroni-corrected P FB, Int-FB, and Seq-BH were significantly smaller than in qTx (Bonferroni-corrected P < 0.01 for all). Using interleaved acquisition during calibration scans of pTx for 7T brain MRI improved the repeatability of B 0 /B 1 + mapping, AFI, and GRE images, without BH. 1 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Clinical application of in vivo dosimetry for external telecobalt machine

International Nuclear Information System (INIS)

Mohammed, H. H. M.

2011-01-01

In external beam radiotherapy quality assurance is carried out on the individual components of treatment chain. The patient simulating device, planning system and treatment machine are tested regularly according to set protocols developed by national and international organizations. Even thought these individual systems are not tested for errors which can be made in the transfer between the systems. The best quality assurance for the treatment planning chain. In vivo dosimetry is used as a quality assurance tool for verifying dosimetry as either the entrance or exit surface of the patient undergoing external beam radiotherapy. It is a proven reliable method of checking overall treatment accuracy, allowing verification of dosimetry and dose calculation as well as patient treatment setup. Accurate in vivo dosimetry is carried out if diodes and thermoluminescence dosimeters (TLDs). the main detector types in use for in vivo dosimetry, are carefully calibrated and the factors influencing their sensitivity are taken into account. The aim of this study was to verify the response of TLDs type (LiF: Mg, Cu, p) use in radiotherapy, to establish calibration procedure for TLDs and to evaluate entrance dose obtained by the treatment planning system with measured dose using thermoluminescence detectors. Calibration of TLDs was done using Cobalt-60 teletherapy machine, linearity and calibration factors were determined. Measurements were performed in random phantom for breast irradiation (for the breast irradiation ( For the breast irradiation technique considered, wedge field was used). All TLDs were processed and analyzed at RICK. In vivo dosimetry represents a technique that has been widely employed to evaluate the dose to the patient mainly in radiotherapy. Thermoluminescent dosimeters are considered the gold stander for in vivo dosimetry and do not require cables for measurements which makes them ideal for mail based studies and have no dose rate or temperature dependence

Calibration factor or calibration coefficient?

International Nuclear Information System (INIS)

Meghzifene, A.; Shortt, K.R.

2002-01-01

Full text: The IAEA/WHO network of SSDLs was set up in order to establish links between SSDL members and the international measurement system. At the end of 2001, there were 73 network members in 63 Member States. The SSDL network members provide calibration services to end-users at the national or regional level. The results of the calibrations are summarized in a document called calibration report or calibration certificate. The IAEA has been using the term calibration certificate and will continue using the same terminology. The most important information in a calibration certificate is a list of calibration factors and their related uncertainties that apply to the calibrated instrument for the well-defined irradiation and ambient conditions. The IAEA has recently decided to change the term calibration factor to calibration coefficient, to be fully in line with ISO [ISO 31-0], which recommends the use of the term coefficient when it links two quantities A and B (equation 1) that have different dimensions. The term factor should only be used for k when it is used to link the terms A and B that have the same dimensions A=k.B. However, in a typical calibration, an ion chamber is calibrated in terms of a physical quantity such as air kerma, dose to water, ambient dose equivalent, etc. If the chamber is calibrated together with its electrometer, then the calibration refers to the physical quantity to be measured per electrometer unit reading. In this case, the terms referred have different dimensions. The adoption by the Agency of the term coefficient to express the results of calibrations is consistent with the 'International vocabulary of basic and general terms in metrology' prepared jointly by the BIPM, IEC, ISO, OIML and other organizations. The BIPM has changed from factor to coefficient. The authors believe that this is more than just a matter of semantics and recommend that the SSDL network members adopt this change in terminology. (author)

Development of basic system for sensor calibration support in nuclear power plants

International Nuclear Information System (INIS)

Kusumi, Naohiro; Ohga, Yukiharu; Fukuda, Mitsuko; Ishizaki, Yuuichi; Koyama, Mikio; Maeda, Akihiko

2004-01-01

It is strongly desirable to reduce maintenance costs and shorten the time of periodic inspections in nuclear power plants. Therefore, it is important to reduce the amount of maintenance work during the inspection. In Japan, sensor calibration is usually performed at every periodic inspection, and the sensor calibration requires a large amount of work. A system for sensor calibration support has been developed to reduce sensor calibration work. The system is composed of two subsystems: a statistical analysis subsystem and a drift detection subsystem, as well as a human-machine interface, which offers support information. The statistical analysis subsystem supports the decision of the sensor calibration intervals based on the statistical analysis of sensor calibration data. There is the possibility that sensor drift increases beyond an allowance value before the sensor calibration intervals determined by the statistical analysis subsystem because of malfunctions, etc. To cope with this, the drift detection subsystem detects the sensor drift online during the plant operation. By combining the statistical analysis subsystem and the drift detection subsystem, a reliable sensor calibration support system is realized. The basic system composed of two subsystems was developed and evaluated using real plant data. The results showed that the sensor calibration intervals can be extended beyond current intervals and that the system is capable of detecting the sensor drift online. (author)

Exploiting the ALICE HLT for PROOF by scheduling of Virtual Machines

International Nuclear Information System (INIS)

Meoni, Marco; Boettger, Stefan; Zelnicek, Pierre; Kebschull, Udo; Lindenstruth, Volker

2011-01-01

The HLT (High-Level Trigger) group of the ALICE experiment at the LHC has prepared a virtual Parallel ROOT Facility (PROOF) enabled cluster (HAF - HLT Analysis Facility) for fast physics analysis, detector calibration and reconstruction of data samples. The HLT-Cluster currently consists of 2860 CPU cores and 175TB of storage. Its purpose is the online filtering of the relevant part of data produced by the particle detector. However, data taking is not running continuously and exploiting unused cluster resources for other applications is highly desirable and improves the usage-cost ratio of the HLT cluster. As such, unused computing resources are dedicated to a PROOF-enabled virtual cluster available to the entire collaboration. This setup is especially aimed at the prototyping phase of analyses that need a high number of development iterations and a short response time, e.g. tuning of analysis cuts, calibration and alignment. HAF machines are enabled and disabled upon user request to start or complete analysis tasks. This is achieved by a virtual machine scheduling framework which dynamically assigns and migrates virtual machines running PROOF workers to unused physical resources. Using this approach we extend the HLT usage scheme to running both online and offline computing, thereby optimizing the resource usage.

Machine tool metrology an industrial handbook

CERN Document Server

Smith, Graham T

2016-01-01

Maximizing reader insights into the key scientific disciplines of Machine Tool Metrology, this text will prove useful for the industrial-practitioner and those interested in the operation of machine tools. Within this current level of industrial-content, this book incorporates significant usage of the existing published literature and valid information obtained from a wide-spectrum of manufacturers of plant, equipment and instrumentation before putting forward novel ideas and methodologies. Providing easy to understand bullet points and lucid descriptions of metrological and calibration subjects, this book aids reader understanding of the topics discussed whilst adding a voluminous-amount of footnotes utilised throughout all of the chapters, which adds some additional detail to the subject. Featuring an extensive amount of photographic-support, this book will serve as a key reference text for all those involved in the field. .

Reliably detectable flaw size for NDE methods that use calibration

Science.gov (United States)

Koshti, Ajay M.

2017-04-01

Probability of detection (POD) analysis is used in assessing reliably detectable flaw size in nondestructive evaluation (NDE). MIL-HDBK-1823 and associated mh18232 POD software gives most common methods of POD analysis. In this paper, POD analysis is applied to an NDE method, such as eddy current testing, where calibration is used. NDE calibration standards have known size artificial flaws such as electro-discharge machined (EDM) notches and flat bottom hole (FBH) reflectors which are used to set instrument sensitivity for detection of real flaws. Real flaws such as cracks and crack-like flaws are desired to be detected using these NDE methods. A reliably detectable crack size is required for safe life analysis of fracture critical parts. Therefore, it is important to correlate signal responses from real flaws with signal responses form artificial flaws used in calibration process to determine reliably detectable flaw size.

Decoding subjective mental states from fMRI activity patterns

International Nuclear Information System (INIS)

Tamaki, Masako; Kamitani, Yukiyasu

2011-01-01

In recent years, functional magnetic resonance imaging (fMRI) decoding has emerged as a powerful tool to read out detailed stimulus features from multi-voxel brain activity patterns. Moreover, the method has been extended to perform a primitive form of 'mind-reading,' by applying a decoder 'objectively' trained using stimulus features to more 'subjective' conditions. In this paper, we first introduce basic procedures for fMRI decoding based on machine learning techniques. Second, we discuss the source of information used for decoding, in particular, the possibility of extracting information from subvoxel neural structures. We next introduce two experimental designs for decoding subjective mental states: the 'objective-to-subjective design' and the 'subjective-to-subjective design.' Then, we illustrate recent studies on the decoding of a variety of mental states, such as, attention, awareness, decision making, memory, and mental imagery. Finally, we discuss the challenges and new directions of fMRI decoding. (author)

Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.

Science.gov (United States)

Chyzhyk, Darya; Graña, Manuel; Öngür, Döst; Shinn, Ann K

2015-05-01

Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that

Design and development of semi-automatic radiation test and calibration facility

International Nuclear Information System (INIS)

Yadav, Ashok Kumar; Chouhan, V.K.; Narayan, Pradeep

2008-01-01

Semi-automatic gamma radiation test and calibration facility have been designed, developed and commissioned at Defence Laboratory Jodhpur (DLJ). The facility comprises of medium and high dose rate range setup using 30 Ci Cobalt-60 source, in a portable remotely operated Techops camera and a 15000 Ci 60 Co source in a Tele-therapy machine. The radiation instruments can be positioned at any desired position using a computer controlled positioner having three translational and one rotational motion. User friendly software helps in positioning the Device Under Test (DUT) at any desired dose rate or distance and acquire the data automatically. The servo and stepper motor controlled positioner helps in achieving the required precision and accuracy for the radiation calibration of the instruments. This paper describes the semi-automatic radiation test and calibration facility commissioned at DLJ. (author)

SU-E-I-65: Estimation of Tagging Efficiency in Pseudo-Continuous Arterial Spin Labeling (pCASL) MRI

Energy Technology Data Exchange (ETDEWEB)

Jen, M [Chang Gung University, Taoyuan City, Taiwan (China); Yan, F; Tseng, Y; Chen, C [Taipei Medical University - Shuang Ho Hospital, Ministry of Health and Welf, New Taipei City, Taiwan (China); Lin, C [GE Healthcare, Taiwan (China); GE Healthcare China, Beijing (China); Liu, H [UT MD Anderson Cancer Center, Houston, TX (United States)

2015-06-15

Purpose: pCASL was recommended as a potent approach for absolute cerebral blood flow (CBF) quantification in clinical practice. However, uncertainties of tagging efficiency in pCASL remain an issue. This study aimed to estimate tagging efficiency by using short quantitative pulsed ASL scan (FAIR-QUIPSSII) and compare resultant CBF values with those calibrated by using 2D Phase Contrast (PC) MRI. Methods: Fourteen normal volunteers participated in this study. All images, including whole brain (WB) pCASL, WB FAIR-QUIPSSII and single-slice 2D PC, were collected on a 3T clinical MRI scanner with a 8-channel head coil. DeltaM map was calculated by averaging the subtraction of tag/control pairs in pCASL and FAIR-QUIPSSII images and used for CBF calculation. Tagging efficiency was then calculated by the ratio of mean gray matter CBF obtained from pCASL and FAIR-QUIPSSII. For comparison, tagging efficiency was also estimated with 2D PC, a previously established method, by contrast WB CBF in pCASL and 2D PC. Feasibility of estimation from a short FAIR-QUIPSSII scan was evaluated by number of averages required for obtaining a stable deltaM value. Setting deltaM calculated by maximum number of averaging (50 pairs) as reference, stable results were defined within ±10% variation. Results: Tagging efficiencies obtained by 2D PC MRI (0.732±0.092) were significantly lower than which obtained by FAIRQUIPPSSII (0.846±0.097) (P<0.05). Feasibility results revealed that four pairs of images in FAIR-QUIPPSSII scan were sufficient to obtain a robust calibration of less than 10% differences from using 50 pairs. Conclusion: This study found that reliable estimation of tagging efficiency could be obtained by a few pairs of FAIR-QUIPSSII images, which suggested that calibration scan in a short duration (within 30s) was feasible. Considering recent reports concerning variability of PC MRI-based calibration, this study proposed an effective alternative for CBF quantification with pCASL.

Functional MRI of the patellofemoral joint: comparison of ultrafast MRI, motion-triggered cine MRI and static MRI

Energy Technology Data Exchange (ETDEWEB)

Muhle, C. [Klinik fuer Radiologische Diagnostik, Univ. Kiel (Germany); Brossmann, J. [Klinik fuer Radiologische Diagnostik, Univ. Kiel (Germany); Melchert, U.H. [Klinik fuer Radiologische Diagnostik, Univ. Kiel (Germany); Schroeder, C. [Radiologische Abt., Universitaets-Kinderklinik, Christian-Albrechts-Universitaet, Kiel (Germany); Boer, R. de [Philips Medical Systems, Best (Netherlands); Spielmann, R.P. [Klinik fuer Radiologische Diagnostik, Univ. Kiel (Germany); Heller, M. [Klinik fuer Radiologische Diagnostik, Univ. Kiel (Germany)

1995-12-31

To evaluate the feasibility and usefulness of ultrafast MRI (u), patellar tracking from 30 of flexion to knee extension (0 ) was analysed and compared with motion-triggered cine MRI (m) and a static MRI technique (s). The different imaging methods were compared in respect of the patellofemoral relationship, the examination time and image quality. Eight healthy subjects and four patients (in total 18 joints) with patellar subluxation or luxation were examined. Significant differences between the static MRI series without quadriceps contraction and the functional MRI studies (motion-triggered cine MRI and ultrafast MRI) were found for the patellar tilt angle. In the dynamic joint studies there was no statistical difference of the regression coefficients between the motion-triggered cine MRI studies and the ultrafast MRI studies. The findings of the functional MRI studies compared with the static MRI images were significantly different for the lateralisation of the patella, expressed by the lateral patellar displacement and bisect offset. No significant differences in patellar lateralisation were found between motion-triggered cine MRI and ultrafast MRI. Ultrafast MRI was superior to motion-triggered cine MRI in terms of the reduction in imaging time and improvement of the image quality. (orig.)

Functional MRI of the patellofemoral joint: comparison of ultrafast MRI, motion-triggered cine MRI and static MRI

International Nuclear Information System (INIS)

Muhle, C.; Brossmann, J.; Melchert, U.H.; Schroeder, C.; Boer, R. de; Spielmann, R.P.; Heller, M.

1995-01-01

To evaluate the feasibility and usefulness of ultrafast MRI (u), patellar tracking from 30 of flexion to knee extension (0 ) was analysed and compared with motion-triggered cine MRI (m) and a static MRI technique (s). The different imaging methods were compared in respect of the patellofemoral relationship, the examination time and image quality. Eight healthy subjects and four patients (in total 18 joints) with patellar subluxation or luxation were examined. Significant differences between the static MRI series without quadriceps contraction and the functional MRI studies (motion-triggered cine MRI and ultrafast MRI) were found for the patellar tilt angle. In the dynamic joint studies there was no statistical difference of the regression coefficients between the motion-triggered cine MRI studies and the ultrafast MRI studies. The findings of the functional MRI studies compared with the static MRI images were significantly different for the lateralisation of the patella, expressed by the lateral patellar displacement and bisect offset. No significant differences in patellar lateralisation were found between motion-triggered cine MRI and ultrafast MRI. Ultrafast MRI was superior to motion-triggered cine MRI in terms of the reduction in imaging time and improvement of the image quality. (orig.)

An Overview of Techniques for Cardiac Left Ventricle Segmentation on Short-Axis MRI

Directory of Open Access Journals (Sweden)

Krasnobaev Arseny

2016-01-01

Full Text Available Nowadays, heart diseases are the leading cause of death. Left ventricle segmentation of a human heart in magnetic resonance images (MRI is a crucial step in both cardiac diseases diagnostics and heart internal structure reconstruction. It allows estimating such important parameters as ejection faction, left ventricle myocardium mass, stroke volume, etc. In addition, left ventricle segmentation helps to construct the personalized heart computational models in order to conduct the numerical simulations. At present, the fully automated cardiac segmentation methods still do not meet the accuracy requirements. We present an overview of left ventricle segmentation algorithms on short-axis MRI. A wide variety of completely different approaches are used for cardiac segmentation, including machine learning, graph-based methods, deformable models, and low-level heuristics. The current state-of-the-art technique is a combination of deformable models with advanced machine learning methods, such as deep learning or Markov random fields. We expect that approaches based on deep belief networks are the most promising ones because the main training process of networks with this architecture can be performed on the unlabelled data. In order to improve the quality of left ventricle segmentation algorithms, we need more datasets with labelled cardiac MRI data in open access.

Exploration of machine learning techniques in predicting multiple sclerosis disease course.

Directory of Open Access Journals (Sweden)

Yijun Zhao

Full Text Available To explore the value of machine learning methods for predicting multiple sclerosis disease course.1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening or not (non-worsening at up to five years after baseline visit. Support vector machines (SVM were used to build the classifier, and compared to logistic regression (LR using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%. Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

On the characterization of single-event related brain activity from functional Magnetic Resonance Imaging (fMRI) measurements

KAUST Repository

Khoram, Nafiseh

2014-08-01

We propose an efficient numerical technique for calibrating the mathematical model that describes the singleevent related brain response when fMRI measurements are given. This method employs a regularized Newton technique in conjunction with a Kalman filtering procedure. We have applied this method to estimate the biophysiological parameters of the Balloon model that describes the hemodynamic brain responses. Illustrative results obtained with both synthetic and real fMRI measurements are presented. © 2014 IEEE.

On the characterization of single-event related brain activity from functional Magnetic Resonance Imaging (fMRI) measurements

KAUST Repository

Khoram, Nafiseh; Zayane, Chadia; Laleg-Kirati, Taous-Meriem; Djellouli, Rabia

2014-01-01

We propose an efficient numerical technique for calibrating the mathematical model that describes the singleevent related brain response when fMRI measurements are given. This method employs a regularized Newton technique in conjunction with a

IAEA workshop/seminar on calibration procedures in dosimetry, Quito, 6-24 October 1986

International Nuclear Information System (INIS)

1987-01-01

The International Atomic Energy Agency in co-operation with the Ecuadorian Atomic Energy Commission organized a workshop and seminar on calibration procedures in dosimetry at the SSDL Quito, 6 to 24 October 1986. All calibration laboratories in the Latin American region were invited to participate. The purpose of the workshop were calibration exercises with therapy-level and protection-level secondary standards at various calibration qualities, discussions on progress made in the different SSDLs in the region and delivering lectures on pertinent subjects. A total of 15 Secondary Standards (10 therapy-level and 5 protection-level) were brought along by the participants and 35 calibration comparisons were performed with those having a valid calibration factor. Thirty-three determinations of calibration factors were performed for secondary standards not having had a calibration before. Twelve different calibration qualities were available (Cobalt-60 and X-rays) and Agency's Secondary Standards traceable to BIPM were the reference standards. The participants were divided into two working groups, each one week and each group into two sub-groups. Both irradiation bunkers were used simultaneously. The one houses the teletherapy Cobalt-60 unit and the protection-level Cobalt-60 irradiator, the other one the constant potential X-ray machine with maximum generating potential of 320 KV and suitable for both therapy-level as well as protection-level calibrations. Due to the heavy workload and limited time available some nightshifts were required to accomplish the requested calibration comparisons

Evaluating the Efficiency of a Multi-core Aware Multi-objective Optimization Tool for Calibrating the SWAT Model

Energy Technology Data Exchange (ETDEWEB)

Zhang, X. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Izaurralde, R. C. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zong, Z. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zhao, K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Thomson, A. M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2012-08-20

The efficiency of calibrating physically-based complex hydrologic models is a major concern in the application of those models to understand and manage natural and human activities that affect watershed systems. In this study, we developed a multi-core aware multi-objective evolutionary optimization algorithm (MAMEOA) to improve the efficiency of calibrating a worldwide used watershed model (Soil and Water Assessment Tool (SWAT)). The test results show that MAMEOA can save about 1-9%, 26-51%, and 39-56% time consumed by calibrating SWAT as compared with sequential method by using dual-core, quad-core, and eight-core machines, respectively. Potential and limitations of MAMEOA for calibrating SWAT are discussed. MAMEOA is open source software.

Machine Learning Control For Highly Reconfigurable High-Order Systems

Science.gov (United States)

2015-01-02

calibration and applications,” Mechatronics and Embedded Systems and Applications (MESA), 2010 IEEE/ASME International Conference on, IEEE, 2010, pp. 38–43...AFRL-OSR-VA-TR-2015-0012 MACHINE LEARNING CONTROL FOR HIGHLY RECONFIGURABLE HIGH-ORDER SYSTEMS John Valasek TEXAS ENGINEERING EXPERIMENT STATION...DIMENSIONAL RECONFIGURABLE SYSTEMS FA9550-11-1-0302 Period of Performance 1 July 2011 – 29 September 2014 John Valasek Aerospace Engineering

Modeling of the integrity of machining surfaces: application to the case of 15-5 PH stainless steel finish turning

International Nuclear Information System (INIS)

Mondelin, A.

2012-01-01

During machining, extreme conditions of pressure, temperature and strain appear in the cutting zone. In this thermo-mechanical context, the link between the cutting conditions (cutting speed, lubrication, feed rate, wear, tool coating...) and the machining surface integrity represents a major scientific target. This PhD study is a part of a global project called MIFSU (Modeling of the Integrity and Fatigue resistance of Machining Surfaces) and it focuses on the finish turning of the 15-5PH (a martensitic stainless steel used for parts of helicopter rotor). Firstly, material behavior has been studied in order to provide data for machining simulations. Stress-free dilatometry tests were conducted to obtain the austenitization kinetics of 15-5PH steel for high heating rates (up to 11,000 degrees C/s). Then, parameters of Leblond metallurgical model have been calibrated. In addition, dynamic compression tests (de/dt ranging from 0.01 to 80/s and e ≥ 1) have been performed to calibrate a strain-rate dependent elasto-plasticity model (for high strains). These tests also helped to highlight the dynamic recrystallization phenomena and their influence on the flow stress of the material. Thus, recrystallization model has also been implemented.In parallel, a numerical model for the prediction of machined surface integrity has been constructed. This model is based on a methodology called 'hybrid' (developed during the PhD thesis of Frederic Valiorgue for the AISI 304L steel). The method consists in replacing tool and chip modeling by equivalent loadings (obtained experimentally). A calibration step of these loadings has been carried out using orthogonal cutting and friction tests (with sensitivity studies of machining forces, friction and heat partition coefficients to cutting parameters variations).Finally, numerical simulations predictions of microstructural changes (austenitization and dynamic recrystallization) and residual stresses have been successfully compared with

Calibration and Industrial Application of Instrument for Surface Mapping based on AFM

DEFF Research Database (Denmark)

Hansen, Hans Nørgaard; Kofod, Niels; De Chiffre, Leonardo

2002-01-01

The paper describes the calibration and application of an integrated system for topographic characterisation of fine surfaces on large workpieces. The system, consisting of an atomic force microscope mounted on a coordinate measuring machine, was especially designed for surface mapping, i.e., mea...... consisting of a steel sphere with a polished surface having 3 nm roughness....

Numerical simulator of the CANDU fueling machine driving desk

International Nuclear Information System (INIS)

Doca, Cezar

2008-01-01

As a national and European premiere, in the 2003 - 2005 period, at the Institute for Nuclear Research Pitesti two CANDU fueling machine heads, no.4 and no.5, for the Nuclear Power Plant Cernavoda - Unit 2 were successfully tested. To perform the tests of these machines, a special CANDU fueling machine testing rig was built and was (and is) available for this goal. The design of the CANDU fueling machine test rig from the Institute for Nuclear Research Pitesti is a replica of the similar equipment operating in CANDU 6 type nuclear power plants. High technical level of the CANDU fueling machine tests required the using of an efficient data acquisition and processing Computer Control System. The challenging goal was to build a computer system (hardware and software) designed and engineered to control the test and calibration process of these fuel handling machines. The design takes care both of the functionality required to correctly control the CANDU fueling machine and of the additional functionality required to assist the testing process. Both the fueling machine testing rig and staff had successfully assessed by the AECL representatives during two missions. At same the time, at the Institute for Nuclear Research Pitesti was/is developed a numerical simulator for the CANDU fueling machine operators training. The paper presents the numerical simulator - a special PC program (software) which simulates the graphics and the functions and the operations at the main desk of the computer control system. The simulator permits 'to drive' a CANDU fueling machine in two manners: manual or automatic. The numerical simulator is dedicated to the training of operators who operate the CANDU fueling machine in a nuclear power plant with CANDU reactor. (author)

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

Science.gov (United States)

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Operating point resolved loss computation in electrical machines

Directory of Open Access Journals (Sweden)

Pfingsten Georg Von

2016-03-01

Full Text Available Magnetic circuits of electromagnetic energy converters, such as electrical machines, are nowadays highly utilized. This proposition is intrinsic for the magnetic as well as the electric circuit and depicts that significant enhancements of electrical machines are difficult to achieve in the absence of a detailed understanding of underlying effects. In order to improve the properties of electrical machines the accurate determination of the locally distributed iron losses based on idealized model assumptions solely is not sufficient. Other loss generating effects have to be considered and the possibility being able to distinguish between the causes of particular loss components is indispensable. Parasitic loss mechanisms additionally contributing to the total losses originating from field harmonics, non-linear material behaviour, rotational magnetizations, and detrimental effects caused by the manufacturing process or temperature, are not explicitly considered in the common iron-loss models, probably even not specifically contained in commonly used calibration factors. This paper presents a methodology being able to distinguish between different loss mechanisms and enables to individually consider particular loss mechanisms in the model of the electric machine. A sensitivity analysis of the model parameters can be performed to obtain information about which decisive loss origin for which working point has to be manipulated by the electromagnetic design or the control of the machine.

Stepwise Regression Analysis of MDOE Balance Calibration Data Acquired at DNW

Science.gov (United States)

DeLoach, RIchard; Philipsen, Iwan

2007-01-01

This paper reports a comparison of two experiment design methods applied in the calibration of a strain-gage balance. One features a 734-point test matrix in which loads are varied systematically according to a method commonly applied in aerospace research and known in the literature of experiment design as One Factor At a Time (OFAT) testing. Two variations of an alternative experiment design were also executed on the same balance, each with different features of an MDOE experiment design. The Modern Design of Experiments (MDOE) is an integrated process of experiment design, execution, and analysis applied at NASA's Langley Research Center to achieve significant reductions in cycle time, direct operating cost, and experimental uncertainty in aerospace research generally and in balance calibration experiments specifically. Personnel in the Instrumentation and Controls Department of the German Dutch Wind Tunnels (DNW) have applied MDOE methods to evaluate them in the calibration of a balance using an automated calibration machine. The data have been sent to Langley Research Center for analysis and comparison. This paper reports key findings from this analysis. The chief result is that a 100-point calibration exploiting MDOE principles delivered quality comparable to a 700+ point OFAT calibration with significantly reduced cycle time and attendant savings in direct and indirect costs. While the DNW test matrices implemented key MDOE principles and produced excellent results, additional MDOE concepts implemented in balance calibrations at Langley Research Center are also identified and described.

Differentiating between bipolar and unipolar depression in functional and structural MRI studies.

Science.gov (United States)

Han, Kyu-Man; De Berardis, Domenico; Fornaro, Michele; Kim, Yong-Ku

2018-03-28

Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification. Copyright © 2018 Elsevier Inc. All rights reserved.

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

Science.gov (United States)

Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

2017-08-01

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

Development of transfer standard devices for ensuring the accurate calibration of ultrasonic physical therapy machines in clinical use

International Nuclear Information System (INIS)

Hekkenberg, R T; Richards, A; Beissner, K; Zeqiri, B; Prout, G; Cantrall, Ch; Bezemer, R A; Koch, Ch; Hodnett, M

2004-01-01

Physical therapy ultrasound is widely applied to patients. However, many devices do not comply with the relevant standard stating that the actual power output shall be within ±20% of the device indication. Extreme cases have been reported: from delivering effectively no ultrasound or operating at maximum power at all powers indicated. This can potentially lead to patient injury as well as mistreatment. The present European (EC) project is an ongoing attempt to improve the quality of the treatment of patients being treated with ultrasonic physical-therapy. A Portable ultrasound Power Standard (PPS) is being developed and accurately calibrated. The PPS includes: Ultrasound transducers (including one exhibiting an unusual output) and a driver for the ultrasound transducers that has calibration and proficiency test functions. Also included with the PPS is a Cavitation Detector to determine the onset of cavitation occurring within the propagation medium. The PPS will be suitable for conducting in-the-field accreditation (proficiency testing and calibration). In order to be accredited it will be important to be able to show traceability of the calibration, the calibration process and qualification of testing staff. The clinical user will benefit from traceability because treatments will be performed more reliably

Testing the quality of images for permanent magnet desktop MRI systems using specially designed phantoms.

Science.gov (United States)

Qiu, Jianfeng; Wang, Guozhu; Min, Jiao; Wang, Xiaoyan; Wang, Pengcheng

2013-12-21

Our aim was to measure the performance of desktop magnetic resonance imaging (MRI) systems using specially designed phantoms, by testing imaging parameters and analysing the imaging quality. We designed multifunction phantoms with diameters of 18 and 60 mm for desktop MRI scanners in accordance with the American Association of Physicists in Medicine (AAPM) report no. 28. We scanned the phantoms with three permanent magnet 0.5 T desktop MRI systems, measured the MRI image parameters, and analysed imaging quality by comparing the data with the AAPM criteria and Chinese national standards. Image parameters included: resonance frequency, high contrast spatial resolution, low contrast object detectability, slice thickness, geometrical distortion, signal-to-noise ratio (SNR), and image uniformity. The image parameters of three desktop MRI machines could be measured using our specially designed phantoms, and most parameters were in line with MRI quality control criterion, including: resonance frequency, high contrast spatial resolution, low contrast object detectability, slice thickness, geometrical distortion, image uniformity and slice position accuracy. However, SNR was significantly lower than in some references. The imaging test and quality control are necessary for desktop MRI systems, and should be performed with the applicable phantom and corresponding standards.

Testing the quality of images for permanent magnet desktop MRI systems using specially designed phantoms

International Nuclear Information System (INIS)

Qiu, Jianfeng; Wang, Guozhu; Min, Jiao; Wang, Xiaoyan; Wang, Pengcheng

2013-01-01

Our aim was to measure the performance of desktop magnetic resonance imaging (MRI) systems using specially designed phantoms, by testing imaging parameters and analysing the imaging quality. We designed multifunction phantoms with diameters of 18 and 60 mm for desktop MRI scanners in accordance with the American Association of Physicists in Medicine (AAPM) report no. 28. We scanned the phantoms with three permanent magnet 0.5 T desktop MRI systems, measured the MRI image parameters, and analysed imaging quality by comparing the data with the AAPM criteria and Chinese national standards. Image parameters included: resonance frequency, high contrast spatial resolution, low contrast object detectability, slice thickness, geometrical distortion, signal-to-noise ratio (SNR), and image uniformity. The image parameters of three desktop MRI machines could be measured using our specially designed phantoms, and most parameters were in line with MRI quality control criterion, including: resonance frequency, high contrast spatial resolution, low contrast object detectability, slice thickness, geometrical distortion, image uniformity and slice position accuracy. However, SNR was significantly lower than in some references. The imaging test and quality control are necessary for desktop MRI systems, and should be performed with the applicable phantom and corresponding standards. (paper)

Can multi-slice or navigator-gated R2* MRI replace single-slice breath-hold acquisition for hepatic iron quantification?

International Nuclear Information System (INIS)

Loeffler, Ralf B.; McCarville, M.B.; Song, Ruitian; Hillenbrand, Claudia M.; Wagstaff, Anne W.; Smeltzer, Matthew P.; Krafft, Axel J.; Hankins, Jane S.

2017-01-01

Liver R2* values calculated from multi-gradient echo (mGRE) magnetic resonance images (MRI) are strongly correlated with hepatic iron concentration (HIC) as shown in several independently derived biopsy calibration studies. These calibrations were established for axial single-slice breath-hold imaging at the location of the portal vein. Scanning in multi-slice mode makes the exam more efficient, since whole-liver coverage can be achieved with two breath-holds and the optimal slice can be selected afterward. Navigator echoes remove the need for breath-holds and allow use in sedated patients. To evaluate if the existing biopsy calibrations can be applied to multi-slice and navigator-controlled mGRE imaging in children with hepatic iron overload, by testing if there is a bias-free correlation between single-slice R2* and multi-slice or multi-slice navigator controlled R2*. This study included MRI data from 71 patients with transfusional iron overload, who received an MRI exam to estimate HIC using gradient echo sequences. Patient scans contained 2 or 3 of the following imaging methods used for analysis: single-slice images (n = 71), multi-slice images (n = 69) and navigator-controlled images (n = 17). Small and large blood corrected region of interests were selected on axial images of the liver to obtain R2* values for all data sets. Bland-Altman and linear regression analysis were used to compare R2* values from single-slice images to those of multi-slice images and navigator-controlled images. Bland-Altman analysis showed that all imaging method comparisons were strongly associated with each other and had high correlation coefficients (0.98 ≤ r ≤ 1.00) with P-values ≤0.0001. Linear regression yielded slopes that were close to 1. We found that navigator-gated or breath-held multi-slice R2* MRI for HIC determination measures R2* values comparable to the biopsy-validated single-slice, single breath-hold scan. We conclude that these three R2* methods can be

Can multi-slice or navigator-gated R2* MRI replace single-slice breath-hold acquisition for hepatic iron quantification?

Energy Technology Data Exchange (ETDEWEB)

Loeffler, Ralf B.; McCarville, M.B.; Song, Ruitian; Hillenbrand, Claudia M. [St. Jude Children' s Research Hospital, Diagnostic Imaging, Memphis, TN (United States); Wagstaff, Anne W. [St. Jude Children' s Research Hospital, Diagnostic Imaging, Memphis, TN (United States); Rhodes College, Memphis, TN (United States); University of Alabama at Birmingham School of Medicine, Birmingham, AL (United States); Smeltzer, Matthew P. [St. Jude Children' s Research Hospital, Department of Biostatistics, Memphis, TN (United States); University of Memphis, Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, Memphis, TN (United States); Krafft, Axel J. [St. Jude Children' s Research Hospital, Diagnostic Imaging, Memphis, TN (United States); University Hospital Center Freiburg, Department of Radiology, Freiburg (Germany); Hankins, Jane S. [St. Jude Children' s Research Hospital, Department of Hematology, Memphis, TN (United States)

2017-01-15

Liver R2* values calculated from multi-gradient echo (mGRE) magnetic resonance images (MRI) are strongly correlated with hepatic iron concentration (HIC) as shown in several independently derived biopsy calibration studies. These calibrations were established for axial single-slice breath-hold imaging at the location of the portal vein. Scanning in multi-slice mode makes the exam more efficient, since whole-liver coverage can be achieved with two breath-holds and the optimal slice can be selected afterward. Navigator echoes remove the need for breath-holds and allow use in sedated patients. To evaluate if the existing biopsy calibrations can be applied to multi-slice and navigator-controlled mGRE imaging in children with hepatic iron overload, by testing if there is a bias-free correlation between single-slice R2* and multi-slice or multi-slice navigator controlled R2*. This study included MRI data from 71 patients with transfusional iron overload, who received an MRI exam to estimate HIC using gradient echo sequences. Patient scans contained 2 or 3 of the following imaging methods used for analysis: single-slice images (n = 71), multi-slice images (n = 69) and navigator-controlled images (n = 17). Small and large blood corrected region of interests were selected on axial images of the liver to obtain R2* values for all data sets. Bland-Altman and linear regression analysis were used to compare R2* values from single-slice images to those of multi-slice images and navigator-controlled images. Bland-Altman analysis showed that all imaging method comparisons were strongly associated with each other and had high correlation coefficients (0.98 ≤ r ≤ 1.00) with P-values ≤0.0001. Linear regression yielded slopes that were close to 1. We found that navigator-gated or breath-held multi-slice R2* MRI for HIC determination measures R2* values comparable to the biopsy-validated single-slice, single breath-hold scan. We conclude that these three R2* methods can be

Multivoxel Pattern Analysis for fMRI Data: A Review

Directory of Open Access Journals (Sweden)

Abdelhak Mahmoudi

2012-01-01

Full Text Available Functional magnetic resonance imaging (fMRI exploits blood-oxygen-level-dependent (BOLD contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs. In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC curves.

Multivoxel Pattern Analysis for fMRI Data: A Review

Science.gov (United States)

Takerkart, Sylvain; Regragui, Fakhita; Boussaoud, Driss; Brovelli, Andrea

2012-01-01

Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves. PMID:23401720

Invited Article: A novel calibration method for the JET real-time far infrared polarimeter and integration of polarimetry-based line-integrated density measurements for machine protection of a fusion plant.

Science.gov (United States)

Boboc, A; Bieg, B; Felton, R; Dalley, S; Kravtsov, Yu

2015-09-01

In this paper, we present the work in the implementation of a new calibration for the JET real-time polarimeter based on the complex amplitude ratio technique and a new self-validation mechanism of data. This allowed easy integration of the polarimetry measurements into the JET plasma density control (gas feedback control) and as well as machine protection systems (neutral beam injection heating safety interlocks). The new addition was used successfully during 2014 JET Campaign and is envisaged that will operate routinely from 2015 campaign onwards in any plasma condition (including ITER relevant scenarios). This mode of operation elevated the importance of the polarimetry as a diagnostic tool in the view of future fusion experiments.

Invited Article: A novel calibration method for the JET real-time far infrared polarimeter and integration of polarimetry-based line-integrated density measurements for machine protection of a fusion plant

Energy Technology Data Exchange (ETDEWEB)

Boboc, A., E-mail: Alexandru.Boboc@ccfe.ac.uk; Felton, R.; Dalley, S. [EUROfusion Consortium, JET, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); CCFE, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Bieg, B.; Kravtsov, Yu. [EUROfusion Consortium, JET, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Institute of Physics, Maritime University of Szczecin, Szczecin (Poland)

2015-09-15

In this paper, we present the work in the implementation of a new calibration for the JET real-time polarimeter based on the complex amplitude ratio technique and a new self-validation mechanism of data. This allowed easy integration of the polarimetry measurements into the JET plasma density control (gas feedback control) and as well as machine protection systems (neutral beam injection heating safety interlocks). The new addition was used successfully during 2014 JET Campaign and is envisaged that will operate routinely from 2015 campaign onwards in any plasma condition (including ITER relevant scenarios). This mode of operation elevated the importance of the polarimetry as a diagnostic tool in the view of future fusion experiments.

Optimization and validation of accelerated golden-angle radial sparse MRI reconstruction with self-calibrating GRAPPA operator gridding.

Science.gov (United States)

Benkert, Thomas; Tian, Ye; Huang, Chenchan; DiBella, Edward V R; Chandarana, Hersh; Feng, Li

2018-07-01

Golden-angle radial sparse parallel (GRASP) MRI reconstruction requires gridding and regridding to transform data between radial and Cartesian k-space. These operations are repeatedly performed in each iteration, which makes the reconstruction computationally demanding. This work aimed to accelerate GRASP reconstruction using self-calibrating GRAPPA operator gridding (GROG) and to validate its performance in clinical imaging. GROG is an alternative gridding approach based on parallel imaging, in which k-space data acquired on a non-Cartesian grid are shifted onto a Cartesian k-space grid using information from multicoil arrays. For iterative non-Cartesian image reconstruction, GROG is performed only once as a preprocessing step. Therefore, the subsequent iterative reconstruction can be performed directly in Cartesian space, which significantly reduces computational burden. Here, a framework combining GROG with GRASP (GROG-GRASP) is first optimized and then compared with standard GRASP reconstruction in 22 prostate patients. GROG-GRASP achieved approximately 4.2-fold reduction in reconstruction time compared with GRASP (∼333 min versus ∼78 min) while maintaining image quality (structural similarity index ≈ 0.97 and root mean square error ≈ 0.007). Visual image quality assessment by two experienced radiologists did not show significant differences between the two reconstruction schemes. With a graphics processing unit implementation, image reconstruction time can be further reduced to approximately 14 min. The GRASP reconstruction can be substantially accelerated using GROG. This framework is promising toward broader clinical application of GRASP and other iterative non-Cartesian reconstruction methods. Magn Reson Med 80:286-293, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Position calibration of a 3-DOF hand-controller with hybrid structure

Science.gov (United States)

Zhu, Chengcheng; Song, Aiguo

2017-09-01

A hand-controller is a human-robot interactive device, which measures the 3-DOF (Degree of Freedom) position of the human hand and sends it as a command to control robot movement. The device also receives 3-DOF force feedback from the robot and applies it to the human hand. Thus, the precision of 3-DOF position measurements is a key performance factor for hand-controllers. However, when using a hybrid type 3-DOF hand controller, various errors occur and are considered originating from machining and assembly variations within the device. This paper presents a calibration method to improve the position tracking accuracy of hybrid type hand-controllers by determining the actual size of the hand-controller parts. By re-measuring and re-calibrating this kind of hand-controller, the actual size of the key parts that cause errors is determined. Modifying the formula parameters with the actual sizes, which are obtained in the calibrating process, improves the end position tracking accuracy of the device.

Complex Wavelet transform for MRI

International Nuclear Information System (INIS)

Junor, P.; Janney, P.

2004-01-01

Full text: There is a perpetual compromise encountered in magnetic resonance (MRl) image reconstruction, between the traditional elements of image quality (noise, spatial resolution and contrast). Additional factors exacerbating this trade-off include various artifacts, computational (and hence time-dependent) overhead, and financial expense. This paper outlines a new approach to the problem of minimizing MRI image acquisition and reconstruction time without compromising resolution and noise reduction. The standard approaches for reconstructing magnetic resonance (MRI) images from raw data (which rely on relatively conventional signal processing) have matured but there are a number of challenges which limit their use. A major one is the 'intrinsic' signal-to-noise ratio (SNR) of the reconstructed image that depends on the strength of the main field. A typical clinical MRI almost invariably uses a super-cooled magnet in order to achieve a high field strength. The ongoing running cost of these super-cooled magnets prompts consideration of alternative magnet systems for use in MRIs for developing countries and in some remote regional installations. The decrease in image quality from using lower field strength magnets can be addressed by improvements in signal processing strategies. Conversely, improved signal processing will obviously benefit the current conventional field strength MRI machines. Moreover, the 'waiting time' experienced in many MR sequences (due to the relaxation time delays) can be exploited by more rigorous processing of the MR signals. Acquisition often needs to be repeated so that coherent averaging may partially redress the shortfall in SNR, at the expense of further delay. Wavelet transforms have been used in MRI as an alternative for encoding and denoising for over a decade. These have not supplanted the traditional Fourier transform methods that have long been the mainstay of MRI reconstruction, but have some inflexibility. The dual

Definition of free form object for low uncertainty measurements on cooridnate measuring machines

DEFF Research Database (Denmark)

Savio, Enrico; De Chiffre, Leonardo

This report is made as a part of the project Easytrac, an EU project under the programme: Competitive and Sustainable Growth: Contract No: G6RD-CT-2000-00188, coordinated by UNIMETRIK S.A. (Spain). The project is concerned with low uncertainty calibrations on coordinate measuring machines. The Ce...

Software Strategy for Robotic Transperineal Prostate Therapy in Closed-Bore MRI

Science.gov (United States)

Tokuda, Junichi; Fischer, Gregory S.; Csoma, Csaba; DiMaio, Simon P.; Gobbi, David G.; Fichtinger, Gabor; Tempany, Clare M.; Hata, Nobuhiko

2009-01-01

A software strategy to provide intuitive navigation for MRI-guided robotic transperineal prostate therapy is presented. In the system, the robot control unit, the MRI scanner, and open-source navigation software are connected to one another via Ethernet to exchange commands, coordinates, and images. Six states of the system called “workphases” are defined based on the clinical scenario to synchronize behaviors of all components. The wizard-style user interface allows easy following of the clinical workflow. On top of this framework, the software provides features for intuitive needle guidance: interactive target planning; 3D image visualization with current needle position; treatment monitoring through real-time MRI. These features are supported by calibration of robot and image coordinates by the fiducial-based registration. The performance test shows that the registration error of the system was 2.6 mm in the prostate area, and it displayed real-time 2D image 1.7 s after the completion of image acquisition. PMID:18982666

Calibration and verification of thermographic cameras for geometric measurements

Science.gov (United States)

Lagüela, S.; González-Jorge, H.; Armesto, J.; Arias, P.

2011-03-01

Infrared thermography is a technique with an increasing degree of development and applications. Quality assessment in the measurements performed with the thermal cameras should be achieved through metrology calibration and verification. Infrared cameras acquire temperature and geometric information, although calibration and verification procedures are only usual for thermal data. Black bodies are used for these purposes. Moreover, the geometric information is important for many fields as architecture, civil engineering and industry. This work presents a calibration procedure that allows the photogrammetric restitution and a portable artefact to verify the geometric accuracy, repeatability and drift of thermographic cameras. These results allow the incorporation of this information into the quality control processes of the companies. A grid based on burning lamps is used for the geometric calibration of thermographic cameras. The artefact designed for the geometric verification consists of five delrin spheres and seven cubes of different sizes. Metrology traceability for the artefact is obtained from a coordinate measuring machine. Two sets of targets with different reflectivity are fixed to the spheres and cubes to make data processing and photogrammetric restitution possible. Reflectivity was the chosen material propriety due to the thermographic and visual cameras ability to detect it. Two thermographic cameras from Flir and Nec manufacturers, and one visible camera from Jai are calibrated, verified and compared using calibration grids and the standard artefact. The calibration system based on burning lamps shows its capability to perform the internal orientation of the thermal cameras. Verification results show repeatability better than 1 mm for all cases, being better than 0.5 mm for the visible one. As it must be expected, also accuracy appears higher in the visible camera, and the geometric comparison between thermographic cameras shows slightly better

An efficient multi-stage algorithm for full calibration of the hemodynamic model from BOLD signal responses

KAUST Repository

Zambri, Brian; Djellouli, Rabia; Laleg-Kirati, Taous-Meriem

2017-01-01

We propose a computational strategy that falls into the category of prediction/correction iterative-type approaches, for calibrating the hemodynamic model introduced by Friston et al. (2000). The proposed method is employed to estimate consecutively the values of the biophysiological system parameters and the external stimulus characteristics of the model. Numerical results corresponding to both synthetic and real functional Magnetic Resonance Imaging (fMRI) measurements for a single stimulus as well as for multiple stimuli are reported to highlight the capability of this computational methodology to fully calibrate the considered hemodynamic model. This article is protected by copyright. All rights reserved.

An efficient multi-stage algorithm for full calibration of the hemodynamic model from BOLD signal responses

KAUST Repository

Zambri, Brian

2017-02-22

We propose a computational strategy that falls into the category of prediction/correction iterative-type approaches, for calibrating the hemodynamic model introduced by Friston et al. (2000). The proposed method is employed to estimate consecutively the values of the biophysiological system parameters and the external stimulus characteristics of the model. Numerical results corresponding to both synthetic and real functional Magnetic Resonance Imaging (fMRI) measurements for a single stimulus as well as for multiple stimuli are reported to highlight the capability of this computational methodology to fully calibrate the considered hemodynamic model. This article is protected by copyright. All rights reserved.

MRI EVALUATION OF TRIGEMINAL NEURALGIA

Directory of Open Access Journals (Sweden)

Sama Surya Sravani

2017-04-01

Full Text Available BACKGROUND Neuralgia is the set of symptoms associated with nerve dysfunction. The most common of these symptoms is pain, which can occur intermittently in one area of the body or can radiate along the length of a damaged nerve. The most common type of neuralgia is trigeminal neuralgia. This study focuses on the effectiveness of MRI in visualising the entire course of trigeminal nerve and to diagnose the exact location, aetiology responsible for trigeminal neuralgia and possible pretreatment evaluation. MATERIALS AND METHODS Clinical records and imaging studies of 30 patients between the ages of 18-60 years who presented to the Department of Radiodiagnosis, KIMS, for brain magnetic resonance imaging with (Philips 1.5T machine during June 2015 to December 2016 were analysed retrospectively. RESULTS  The entire course of trigeminal nerve is evaluated in these patients.  There are different causes of trigeminal neuralgia, but in our study, most frequent cause is mechanical irritation of nerve is due to neurovascular contact (24 cases. The other causes identified are cerebellopontine angle lesions, brainstem tumours, demyelinating disease involving brainstem.  The cisternal portion of the nerve is the most common site of involvement. CONCLUSION Trigeminal nerve is the largest cranial nerve. MRI is unique as it produces images of entire course of the nerve. Of the many causes of trigeminal neuralgia, neurovascular conflict is the most common cause. The exact location and degree of neurovascular compression is graded on MRI.

LINEAR KERNEL SUPPORT VECTOR MACHINES FOR MODELING PORE-WATER PRESSURE RESPONSES

Directory of Open Access Journals (Sweden)

KHAMARUZAMAN W. YUSOF

2017-08-01

Full Text Available Pore-water pressure responses are vital in many aspects of slope management, design and monitoring. Its measurement however, is difficult, expensive and time consuming. Studies on its predictions are lacking. Support vector machines with linear kernel was used here to predict the responses of pore-water pressure to rainfall. Pore-water pressure response data was collected from slope instrumentation program. Support vector machine meta-parameter calibration and model development was carried out using grid search and k-fold cross validation. The mean square error for the model on scaled test data is 0.0015 and the coefficient of determination is 0.9321. Although pore-water pressure response to rainfall is a complex nonlinear process, the use of linear kernel support vector machine can be employed where high accuracy can be sacrificed for computational ease and time.

PLEIADES ABSOLUTE CALIBRATION : INFLIGHT CALIBRATION SITES AND METHODOLOGY

Directory of Open Access Journals (Sweden)

S. Lachérade

2012-07-01

Full Text Available In-flight calibration of space sensors once in orbit is a decisive step to be able to fulfil the mission objectives. This article presents the methods of the in-flight absolute calibration processed during the commissioning phase. Four In-flight calibration methods are used: absolute calibration, cross-calibration with reference sensors such as PARASOL or MERIS, multi-temporal monitoring and inter-bands calibration. These algorithms are based on acquisitions over natural targets such as African deserts, Antarctic sites, La Crau (Automatic calibration station and Oceans (Calibration over molecular scattering or also new extra-terrestrial sites such as the Moon and selected stars. After an overview of the instrument and a description of the calibration sites, it is pointed out how each method is able to address one or several aspects of the calibration. We focus on how these methods complete each other in their operational use, and how they help building a coherent set of information that addresses all aspects of in-orbit calibration. Finally, we present the perspectives that the high level of agility of PLEIADES offers for the improvement of its calibration and a better characterization of the calibration sites.

Coupling machine learning with mechanistic models to study runoff production and river flow at the hillslope scale

Science.gov (United States)

Marçais, J.; Gupta, H. V.; De Dreuzy, J. R.; Troch, P. A. A.

2016-12-01

Geomorphological structure and geological heterogeneity of hillslopes are major controls on runoff responses. The diversity of hillslopes (morphological shapes and geological structures) on one hand, and the highly non linear runoff mechanism response on the other hand, make it difficult to transpose what has been learnt at one specific hillslope to another. Therefore, making reliable predictions on runoff appearance or river flow for a given hillslope is a challenge. Applying a classic model calibration (based on inverse problems technique) requires doing it for each specific hillslope and having some data available for calibration. When applied to thousands of cases it cannot always be promoted. Here we propose a novel modeling framework based on coupling process based models with data based approach. First we develop a mechanistic model, based on hillslope storage Boussinesq equations (Troch et al. 2003), able to model non linear runoff responses to rainfall at the hillslope scale. Second we set up a model database, representing thousands of non calibrated simulations. These simulations investigate different hillslope shapes (real ones obtained by analyzing 5m digital elevation model of Brittany and synthetic ones), different hillslope geological structures (i.e. different parametrizations) and different hydrologic forcing terms (i.e. different infiltration chronicles). Then, we use this model library to train a machine learning model on this physically based database. Machine learning model performance is then assessed by a classic validating phase (testing it on new hillslopes and comparing machine learning with mechanistic outputs). Finally we use this machine learning model to learn what are the hillslope properties controlling runoffs. This methodology will be further tested combining synthetic datasets with real ones.

High-field MRI and mercury release from dental amalgam fillings.

Science.gov (United States)

Mortazavi, S M J; Neghab, M; Anoosheh, S M H; Bahaeddini, N; Mortazavi, G; Neghab, P; Rajaeifard, A

2014-04-01

Mercury is among the most toxic nonradioactive elements which may cause toxicity even at low doses. Some studies showed release of mercury from dental amalgam fillings in individuals who used mobile phone. This study was conducted to assess the effect of high-field MRI on mercury release from dental amalgam filling. We studied two groups of students with identical tooth decays requiring a similar pattern of restorative dentistry. They were exposed to a magnetic flux density of 1.5 T produced by a MRI machine. 16 otherwise healthy students with identical dental decay participated in this study. They underwent similar restorative dentistry procedures and randomly divided into two groups of MRI-exposed and control arms. Urinary concentrations of mercury in the control subjects were measured before (hour 0) and 48 and 72 hrs after amalgam restoration, using cold vapor atomic absorption spectrometry. Urinary concentrations of mercury in exposed individuals were determined before (hour 0), and 24, 48, 72 and 96 hrs after amalgam restoration. Unlike control subjects, they underwent conventional brain MRI (15 min, 99 slices), 24 hrs after amalgam restoration. The mean±SD urinary mercury levels in MRI-exposed individuals increased linearly from a baseline value of 20.70±17.96 to 24.83±22.91 μg/L 72 hrs after MRI. In the control group, the concentration decreased linearly from 20.70±19.77 to 16.14±20.05 μg/L. The difference between urinary mercury in the exposed and control group, 72 hrs after MRI (96 h after restoration),was significant (p=0.046). These findings provide further support for the noxious effect of MRI (exposure to strong magnetic field)and release of mercury from dental amalgam fillings.

TU-F-BRB-02: Motion Artifacts and Suppression in MRI

International Nuclear Information System (INIS)

Zhong, X.

2015-01-01

The current clinical standard of organ respiratory imaging, 4D-CT, is fundamentally limited by poor soft-tissue contrast and imaging dose. These limitations are potential barriers to beneficial “4D” radiotherapy methods which optimize the target and OAR dose-volume considering breathing motion but rely on a robust motion characterization. Conversely, MRI imparts no known radiation risk and has excellent soft-tissue contrast. MRI-based motion management is therefore highly desirable and holds great promise to improve radiotherapy of moving cancers, particularly in the abdomen. Over the past decade, MRI techniques have improved significantly, making MR-based motion management clinically feasible. For example, cine MRI has high temporal resolution up to 10 f/s and has been used to track and/or characterize tumor motion, study correlation between external and internal motions. New MR technologies, such as 4D-MRI and MRI hybrid treatment machines (i.e. MR-linac or MR-Co60), have been recently developed. These technologies can lead to more accurate target volume determination and more precise radiation dose delivery via direct tumor gating or tracking. Despite all these promises, great challenges exist and the achievable clinical benefit of MRI-based tumor motion management has yet to be fully explored, much less realized. In this proposal, we will review novel MR-based motion management methods and technologies, the state-of-the-art concerning MRI development and clinical application and the barriers to more widespread adoption. Learning Objectives: Discuss the need of MR-based motion management for improving patient care in radiotherapy. Understand MR techniques for motion imaging and tumor motion characterization. Understand the current state of the art and future steps for clinical integration. Henry Ford Health System holds research agreements with Philips Healthcare. Research sponsored in part by a Henry Ford Health System Internal Mentored Grant

TU-F-BRB-00: MRI-Based Motion Management for RT

International Nuclear Information System (INIS)

2015-01-01

The current clinical standard of organ respiratory imaging, 4D-CT, is fundamentally limited by poor soft-tissue contrast and imaging dose. These limitations are potential barriers to beneficial “4D” radiotherapy methods which optimize the target and OAR dose-volume considering breathing motion but rely on a robust motion characterization. Conversely, MRI imparts no known radiation risk and has excellent soft-tissue contrast. MRI-based motion management is therefore highly desirable and holds great promise to improve radiotherapy of moving cancers, particularly in the abdomen. Over the past decade, MRI techniques have improved significantly, making MR-based motion management clinically feasible. For example, cine MRI has high temporal resolution up to 10 f/s and has been used to track and/or characterize tumor motion, study correlation between external and internal motions. New MR technologies, such as 4D-MRI and MRI hybrid treatment machines (i.e. MR-linac or MR-Co60), have been recently developed. These technologies can lead to more accurate target volume determination and more precise radiation dose delivery via direct tumor gating or tracking. Despite all these promises, great challenges exist and the achievable clinical benefit of MRI-based tumor motion management has yet to be fully explored, much less realized. In this proposal, we will review novel MR-based motion management methods and technologies, the state-of-the-art concerning MRI development and clinical application and the barriers to more widespread adoption. Learning Objectives: Discuss the need of MR-based motion management for improving patient care in radiotherapy. Understand MR techniques for motion imaging and tumor motion characterization. Understand the current state of the art and future steps for clinical integration. Henry Ford Health System holds research agreements with Philips Healthcare. Research sponsored in part by a Henry Ford Health System Internal Mentored Grant

High-Field MRI and Mercury Release from Dental Amalgam Fillings

Directory of Open Access Journals (Sweden)

SMJ Mortazavi

2014-04-01

Full Text Available Mercury is among the most toxic nonradioactive elements which may cause toxicity even at low doses. Some studies showed release of mercury from dental amalgam fillings in individuals who used mobile phone. This study was conducted to assess the effect of high-field MRI on mercury release from dental amalgam filling. We studied two groups of students with identical tooth decays requiring a similar pattern of restorative dentistry. They were exposed to a magnetic flux density of 1.5 T produced by a MRI machine. 16 otherwise healthy students with identical dental decay participated in this study. They underwent similar restorative dentistry procedures and randomly divided into two groups of MRI-exposed and control arms. Urinary concentrations of mercury in the control subjects were measured before (hour 0 and 48 and 72 hrs after amalgam restoration, using cold vapor atomic absorption spectrometry. Urinary concentrations of mercury in exposed individuals were determined before (hour 0, and 24, 48, 72 and 96 hrs after amalgam restoration. Unlike control subjects, they underwent conventional brain MRI (15 min, 99 slices, 24 hrs after amalgam restoration. The mean±SD urinary mercury levels in MRI-exposed individuals increased linearly from a baseline value of 20.70±17.96 to 24.83±22.91 μg/L 72 hrs after MRI. In the control group, the concentration decreased linearly from 20.70±19.77 to 16.14±20.05 μg/L. The difference between urinary mercury in the exposed and control group, 72 hrs after MRI (96 h after restoration,was significant (p=0.046. These findings provide further support for the noxious effect of MRI (exposure to strong magnetic fieldand release of mercury from dental amalgam fillings.

TU-F-BRB-00: MRI-Based Motion Management for RT

Energy Technology Data Exchange (ETDEWEB)

NONE

2015-06-15

The current clinical standard of organ respiratory imaging, 4D-CT, is fundamentally limited by poor soft-tissue contrast and imaging dose. These limitations are potential barriers to beneficial “4D” radiotherapy methods which optimize the target and OAR dose-volume considering breathing motion but rely on a robust motion characterization. Conversely, MRI imparts no known radiation risk and has excellent soft-tissue contrast. MRI-based motion management is therefore highly desirable and holds great promise to improve radiotherapy of moving cancers, particularly in the abdomen. Over the past decade, MRI techniques have improved significantly, making MR-based motion management clinically feasible. For example, cine MRI has high temporal resolution up to 10 f/s and has been used to track and/or characterize tumor motion, study correlation between external and internal motions. New MR technologies, such as 4D-MRI and MRI hybrid treatment machines (i.e. MR-linac or MR-Co60), have been recently developed. These technologies can lead to more accurate target volume determination and more precise radiation dose delivery via direct tumor gating or tracking. Despite all these promises, great challenges exist and the achievable clinical benefit of MRI-based tumor motion management has yet to be fully explored, much less realized. In this proposal, we will review novel MR-based motion management methods and technologies, the state-of-the-art concerning MRI development and clinical application and the barriers to more widespread adoption. Learning Objectives: Discuss the need of MR-based motion management for improving patient care in radiotherapy. Understand MR techniques for motion imaging and tumor motion characterization. Understand the current state of the art and future steps for clinical integration. Henry Ford Health System holds research agreements with Philips Healthcare. Research sponsored in part by a Henry Ford Health System Internal Mentored Grant.

TU-F-BRB-02: Motion Artifacts and Suppression in MRI

Energy Technology Data Exchange (ETDEWEB)

Zhong, X. [Siemens (Germany)

2015-06-15

The current clinical standard of organ respiratory imaging, 4D-CT, is fundamentally limited by poor soft-tissue contrast and imaging dose. These limitations are potential barriers to beneficial “4D” radiotherapy methods which optimize the target and OAR dose-volume considering breathing motion but rely on a robust motion characterization. Conversely, MRI imparts no known radiation risk and has excellent soft-tissue contrast. MRI-based motion management is therefore highly desirable and holds great promise to improve radiotherapy of moving cancers, particularly in the abdomen. Over the past decade, MRI techniques have improved significantly, making MR-based motion management clinically feasible. For example, cine MRI has high temporal resolution up to 10 f/s and has been used to track and/or characterize tumor motion, study correlation between external and internal motions. New MR technologies, such as 4D-MRI and MRI hybrid treatment machines (i.e. MR-linac or MR-Co60), have been recently developed. These technologies can lead to more accurate target volume determination and more precise radiation dose delivery via direct tumor gating or tracking. Despite all these promises, great challenges exist and the achievable clinical benefit of MRI-based tumor motion management has yet to be fully explored, much less realized. In this proposal, we will review novel MR-based motion management methods and technologies, the state-of-the-art concerning MRI development and clinical application and the barriers to more widespread adoption. Learning Objectives: Discuss the need of MR-based motion management for improving patient care in radiotherapy. Understand MR techniques for motion imaging and tumor motion characterization. Understand the current state of the art and future steps for clinical integration. Henry Ford Health System holds research agreements with Philips Healthcare. Research sponsored in part by a Henry Ford Health System Internal Mentored Grant.

Game controller modification for fMRI hyperscanning experiments in a cooperative virtual reality environment.

Science.gov (United States)

Trees, Jason; Snider, Joseph; Falahpour, Maryam; Guo, Nick; Lu, Kun; Johnson, Douglas C; Poizner, Howard; Liu, Thomas T

2014-01-01

Hyperscanning, an emerging technique in which data from multiple interacting subjects' brains are simultaneously recorded, has become an increasingly popular way to address complex topics, such as "theory of mind." However, most previous fMRI hyperscanning experiments have been limited to abstract social interactions (e.g. phone conversations). Our new method utilizes a virtual reality (VR) environment used for military training, Virtual Battlespace 2 (VBS2), to create realistic avatar-avatar interactions and cooperative tasks. To control the virtual avatar, subjects use a MRI compatible Playstation 3 game controller, modified by removing all extraneous metal components and replacing any necessary ones with 3D printed plastic models. Control of both scanners' operation is initiated by a VBS2 plugin to sync scanner time to the known time within the VR environment. Our modifications include:•Modification of game controller to be MRI compatible.•Design of VBS2 virtual environment for cooperative interactions.•Syncing two MRI machines for simultaneous recording.

Dataset for: An efficient multi-stage algorithm for full calibration of the hemodynamic model from BOLD signal responses

KAUST Repository

Djellouli, Rabia

2017-01-01

We propose a computational strategy that falls into the category of prediction/correction iterative-type approaches, for calibrating the hemodynamic model introduced by Friston et al. (2000). The proposed method is employed to estimate consecutively the values of the biophysiological system parameters and the external stimulus characteristics of the model. Numerical results corresponding to both synthetic and real functional Magnetic Resonance Imaging (fMRI) measurements for a single stimulus as well as for multiple stimuli are reported to highlight the capability of this computational methodology to fully calibrate the considered hemodynamic model.

Calibration of High Frequency MEMS Microphones

Science.gov (United States)

Shams, Qamar A.; Humphreys, William M.; Bartram, Scott M.; Zuckewar, Allan J.

2007-01-01

Understanding and controlling aircraft noise is one of the major research topics of the NASA Fundamental Aeronautics Program. One of the measurement technologies used to acquire noise data is the microphone directional array (DA). Traditional direction array hardware, consisting of commercially available condenser microphones and preamplifiers can be too expensive and their installation in hard-walled wind tunnel test sections too complicated. An emerging micro-machining technology coupled with the latest cutting edge technologies for smaller and faster systems have opened the way for development of MEMS microphones. The MEMS microphone devices are available in the market but suffer from certain important shortcomings. Based on early experiments with array prototypes, it has been found that both the bandwidth and the sound pressure level dynamic range of the microphones should be increased significantly to improve the performance and flexibility of the overall array. Thus, in collaboration with an outside MEMS design vendor, NASA Langley modified commercially available MEMS microphone as shown in Figure 1 to meet the new requirements. Coupled with the design of the enhanced MEMS microphones was the development of a new calibration method for simultaneously obtaining the sensitivity and phase response of the devices over their entire broadband frequency range. Over the years, several methods have been used for microphone calibration. Some of the common methods of microphone calibration are Coupler (Reciprocity, Substitution, and Simultaneous), Pistonphone, Electrostatic actuator, and Free-field calibration (Reciprocity, Substitution, and Simultaneous). Traditionally, electrostatic actuators (EA) have been used to characterize air-condenser microphones for wideband frequency ranges; however, MEMS microphones are not adaptable to the EA method due to their construction and very small diaphragm size. Hence a substitution-based, free-field method was developed to

An Axial Sliding Test for machine elements surfaces

DEFF Research Database (Denmark)

Godi, Alessandro; Grønbæk, J.; Mohaghegh, Kamran

2012-01-01

are necessary: a press to provide the normal pressure and a tensile machine to perform the axial movements. The test is calibrated so that the correspondence between the normal pressure and the container advancement is found. Finally, preliminary tests are carried out involving a multifunctional and a fine......Throughout the years, it has become more and more important to find new methods for reducing friction and wear occurrence in machine elements. A possible solution is found in texturing the surfaces under tribological contact, hence the development and spread of plateau-honed surface for cylinder...... liners. To prove the efficacy of a particular textured surface, it is paramount to perform experimental tests under controlled laboratory conditions. In this paper a new test rig simulating pure sliding conditions is presented, dubbed Axial Sliding Test. It presents four major components: a rod, a sleeve...

Bayesian model calibration of ramp compression experiments on Z

Science.gov (United States)

Brown, Justin; Hund, Lauren

2017-06-01

Bayesian model calibration (BMC) is a statistical framework to estimate inputs for a computational model in the presence of multiple uncertainties, making it well suited to dynamic experiments which must be coupled with numerical simulations to interpret the results. Often, dynamic experiments are diagnosed using velocimetry and this output can be modeled using a hydrocode. Several calibration issues unique to this type of scenario including the functional nature of the output, uncertainty of nuisance parameters within the simulation, and model discrepancy identifiability are addressed, and a novel BMC process is proposed. As a proof of concept, we examine experiments conducted on Sandia National Laboratories' Z-machine which ramp compressed tantalum to peak stresses of 250 GPa. The proposed BMC framework is used to calibrate the cold curve of Ta (with uncertainty), and we conclude that the procedure results in simple, fast, and valid inferences. Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Multiparametric computer-aided differential diagnosis of Alzheimer's disease and frontotemporal dementia using structural and advanced MRI

International Nuclear Information System (INIS)

Bron, Esther E.; Klein, Stefan; Smits, Marion; Steketee, Rebecca M.E.; Meijboom, Rozanna; Papma, Janne M.; Swieten, John C. van; Groot, Marius de; Niessen, Wiro J.

2017-01-01

To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls. This retrospective study used MRI data from 24 early-onset AD and 33 early-onset FTD patients and 34 controls (CN). Classification was based on voxel-wise feature maps derived from structural MRI, ASL, and DTI. Support vector machines (SVMs) were trained to classify AD versus CN (AD-CN), FTD-CN, AD-FTD, and AD-FTD-CN (multi-class). Classification performance was assessed by the area under the receiver-operating-characteristic curve (AUC) and accuracy. Using SVM significance maps, we analysed contributions of brain regions. Combining ASL and DTI with structural MRI resulted in higher classification performance for differential diagnosis of AD and FTD (AUC = 84%; p = 0.05) than using structural MRI by itself (AUC = 72%). The performance of ASL and DTI themselves did not improve over structural MRI. The classifications were driven by different brain regions for ASL and DTI than for structural MRI, suggesting complementary information. ASL and DTI are promising additions to structural MRI for classification of early-onset AD, early-onset FTD, and controls, and may improve the computer-aided differential diagnosis on a single-subject level. (orig.)

Particle identification at LHCb: new calibration techniques and machine learning classification algorithms

CERN Multimedia

CERN. Geneva

2018-01-01

Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic uncertainties. This talk highlights recent developments in PID that use innovative machine learning techniques, as well as novel data-driven approaches which ensure that PID performance is well reproduced in simulation.

Smart Cutting Tools and Smart Machining: Development Approaches, and Their Implementation and Application Perspectives

Science.gov (United States)

Cheng, Kai; Niu, Zhi-Chao; Wang, Robin C.; Rakowski, Richard; Bateman, Richard

2017-09-01

Smart machining has tremendous potential and is becoming one of new generation high value precision manufacturing technologies in line with the advance of Industry 4.0 concepts. This paper presents some innovative design concepts and, in particular, the development of four types of smart cutting tools, including a force-based smart cutting tool, a temperature-based internally-cooled cutting tool, a fast tool servo (FTS) and smart collets for ultraprecision and micro manufacturing purposes. Implementation and application perspectives of these smart cutting tools are explored and discussed particularly for smart machining against a number of industrial application requirements. They are contamination-free machining, machining of tool-wear-prone Si-based infra-red devices and medical applications, high speed micro milling and micro drilling, etc. Furthermore, implementation techniques are presented focusing on: (a) plug-and-produce design principle and the associated smart control algorithms, (b) piezoelectric film and surface acoustic wave transducers to measure cutting forces in process, (c) critical cutting temperature control in real-time machining, (d) in-process calibration through machining trials, (e) FE-based design and analysis of smart cutting tools, and (f) application exemplars on adaptive smart machining.

Calibration of high-dose radiation facilities (Handbook)

International Nuclear Information System (INIS)

Gupta, B.L.; Bhat, R.M.

1986-01-01

In India at present several high intensity radiation sources are used. There are 135 teletheraphy machines and 65 high intensity cobalt-60 sources in the form of gamma chambers (2.5 Ci) and PANBIT (50 Ci). Several food irradiation facilities and a medical sterilization plant ISOMED are also in operation. The application of these high intensity sources involve a wide variation of dose from 10 Gy to 100 kGy. Accurate and reproducible radiation dosimetry is essential in the use of these sources. This handbook is especially compiled for calibration of high-dose radiation facilities. The first few chapters discuss such topics as interaction of radiation with matter, radiation chemistry, radiation processing, commonly used high intensity radiation sources and their special features, radiation units and dosimetry principles. In the chapters which follow, chemical dosimeters are discussed in detail. This discussion covers Fricke dosimeter, FBX dosimeter, ceric sulphate dosimeter, free radical dosimetry, coloured indicators for irrdiation verification. A final chapter is devoted to practical hints to be followed in calibration work. (author)

Linear Discriminant Analysis achieves high classification accuracy for the BOLD fMRI response to naturalistic movie stimuli.

Directory of Open Access Journals (Sweden)

Hendrik eMandelkow

2016-03-01

Full Text Available Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI. However, conventional fMRI analysis based on statistical parametric mapping (SPM and the general linear model (GLM is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA, have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbour (NN, Gaussian Naïve Bayes (GNB, and (regularised Linear Discriminant Analysis (LDA in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie.Results show that LDA regularised by principal component analysis (PCA achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2s apart during a 300s movie (chance level 0.7% = 2s/300s. The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these

Calibration

International Nuclear Information System (INIS)

Greacen, E.L.; Correll, R.L.; Cunningham, R.B.; Johns, G.G.; Nicolls, K.D.

1981-01-01

Procedures common to different methods of calibration of neutron moisture meters are outlined and laboratory and field calibration methods compared. Gross errors which arise from faulty calibration techniques are described. The count rate can be affected by the dry bulk density of the soil, the volumetric content of constitutional hydrogen and other chemical components of the soil and soil solution. Calibration is further complicated by the fact that the neutron meter responds more strongly to the soil properties close to the detector and source. The differences in slope of calibration curves for different soils can be as much as 40%

A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

Science.gov (United States)

Sun, Baozhou; Lam, Dao; Yang, Deshan; Grantham, Kevin; Zhang, Tiezhi; Mutic, Sasa; Zhao, Tianyu

2018-05-01

Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution

Interfacing and data acquisitioning of creep testing machines

International Nuclear Information System (INIS)

Rana, M.A.; Ahmad, Z.; Farooq, M.A.; Ali, L.; Mushtaq, N.

1998-05-01

Automation of DSM-6100-CREEP TESTING MACHINES is made by using an IBM PC/XT/AT compatibles along with DAS-16 High Speed Analog I/O board. Creep test parameters namely force, temperature and LVDTs (Linear Variable Differential Transducer) left and right are calibrated. Empirical formula for each parameter is developed to convert data, which is received in the form of counts, into engineering units. LVDT controller module is designed and fabricated to handle two LVDTs for data acquisition of a creep test required for it. (author)

Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning.

Science.gov (United States)

Mani, Subramani; Chen, Yukun; Arlinghaus, Lori R; Li, Xia; Chakravarthy, A Bapsi; Bhave, Sandeep R; Welch, E Brian; Levy, Mia A; Yankeelov, Thomas E

2011-01-01

The ability to predict early in the course of treatment the response of breast tumors to neoadjuvant chemotherapy can stratify patients based on response for patient-specific treatment strategies. Currently response to neoadjuvant chemotherapy is evaluated based on physical exam or breast imaging (mammogram, ultrasound or conventional breast MRI). There is a poor correlation among these measurements and with the actual tumor size when measured by the pathologist during definitive surgery. We tested the feasibility of using quantitative MRI as a tool for early prediction of tumor response. Between 2007 and 2010 twenty consecutive patients diagnosed with Stage II/III breast cancer and receiving neoadjuvant chemotherapy were enrolled on a prospective imaging study. Our study showed that quantitative MRI parameters along with routine clinical measures can predict responders from non-responders to neoadjuvant chemotherapy. The best predictive model had an accuracy of 0.9, a positive predictive value of 0.91 and an AUC of 0.96.

Study of Environmental Data Complexity using Extreme Learning Machine

Science.gov (United States)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Transverse Polarization for Energy Calibration at the Z peak

CERN Document Server

Koratzinos, M

2015-01-01

In this paper we deal with aspects of transverse polarization for the purpose of energy calibration of proposed circular colliders like the FCC-ee and the CEPC. The main issues of such a measurement will be discussed. The possibility of using this method to accurately determine the energy at the WW threshold as well as the Z peak will be addressed. The use of wigglers for reducing long polarization times will be discussed and a possible strategy will be presented for minimising the energy uncertainty error in these large machines.

Design of a tracked ultrasound calibration phantom made of LEGO bricks

Science.gov (United States)

Walsh, Ryan; Soehl, Marie; Rankin, Adam; Lasso, Andras; Fichtinger, Gabor

2014-03-01

PURPOSE: Spatial calibration of tracked ultrasound systems is commonly performed using precisely fabricated phantoms. Machining or 3D printing has relatively high cost and not easily available. Moreover, the possibilities for modifying the phantoms are very limited. Our goal was to find a method to construct a calibration phantom from affordable, widely available components, which can be built in short time, can be easily modified, and provides comparable accuracy to the existing solutions. METHODS: We designed an N-wire calibration phantom made of LEGO® bricks. To affirm the phantom's reproducibility and build time, ten builds were done by first-time users. The phantoms were used for a tracked ultrasound calibration by an experienced user. The success of each user's build was determined by the lowest root mean square (RMS) wire reprojection error of three calibrations. The accuracy and variance of calibrations were evaluated for the calibrations produced for various tracked ultrasound probes. The proposed model was compared to two of the currently available phantom models for both electromagnetic and optical tracking. RESULTS: The phantom was successfully built by all ten first-time users in an average time of 18.8 minutes. It cost approximately $10 CAD for the required LEGO® bricks and averaged a 0.69mm of error in the calibration reproducibility for ultrasound calibrations. It is one third the cost of similar 3D printed phantoms and takes much less time to build. The proposed phantom's image reprojections were 0.13mm more erroneous than those of the highest performing current phantom model The average standard deviation of multiple 3D image reprojections differed by 0.05mm between the phantoms CONCLUSION: It was found that the phantom could be built in less time, was one third the cost, compared to similar 3D printed models. The proposed phantom was found to be capable of producing equivalent calibrations to 3D printed phantoms.

Patient positioning with X-ray detector self-calibration for image guided therapy

International Nuclear Information System (INIS)

Selby, B.P.; Sakas, G.; Stilla, U.; Groch, W.-D.

2011-01-01

Full text: Automatic alignment estimation from projection images has a range of applications, but misaligned cameras induce inaccuracies. Calibration methods for optical cameras requiring calibration bodies or detectable features have been a matter of research for years. Not so for image guided therapy, although exact patient pose recovery is crucial. To image patient anatomy, X-ray instead of optical equipment is used. Feature detection is often infeasible. Furthermore, a method not requiring a calibration body, usable during treatment, would be desirable to improve accuracy of the patient alignment. We present a novel approach not relying on image features but combining intensity based calibration with 3D pose recovery. A stereoscopic X-ray camera model is proposed, and effects of erroneous parameters on the patient alignment are evaluated. The relevant camera parameters are automatically computed by comparison of X-ray to CT images and are incorporated in the patient alignment computation. The methods were tested with ground truth data of an anatomic phantom with artificially produced misalignments and available real-patient images from a particle therapy machine. We show that our approach can compensate patient alignment errors through mis-calibration of a camera from more than 5 mm to below 0.2 mm. Usage of images with artificial noise shows that the method is robust against image degradation of 2-5%. X-ray camera sel calibration improves accuracy when cameras are misaligned. We could show that rigid body alignment was computed more accurately and that self-calibration is possible, even if detection of corresponding image features is not. (author)

Analysis of machining and machine tools

CERN Document Server

Liang, Steven Y

2016-01-01

This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

MD#2183: Calibration of the IR6 B2 diamond BLMs

CERN Document Server

Valette, Matthieu; Lindstrom, Bjorn Hans Filip

2018-01-01

In case of an asynchronous beam dump with a fully filled LHC machine, causing ~40 bunches to impact on the movable dump protection absorber (TCDQ), it is expected that all standard ionisation chamber Beam Loss Monitors (IC BLM) around the LHC dumping region in IR6 will be saturated. Diamond Beam Loss Monitors (dBLM) were therefore installed next to the TCDQ downstream of the extraction kickers. These detectors allow resolving losses at a nanosecond timescale and with a dynamic range of several orders of magnitude; thus, allowing to derive the number of nominal bunches impacting the TCDQ. After a first series of calibrations using asynchronous beam dump tests, an experiment was conducted during MD#1182 to demonstrate the possibility of resolving a nominal bunch hitting the TCDQ. During this first MD only the Beam 1 dBLM was calibrated appropriately, a second calibration MD was therefore performed in 2017 for the B2 system. Results from this MD and conclusions regarding dBLM saturation with a top energy nominal...

Automated segmentation of reference tissue for prostate cancer localization in dynamic contrast enhanced MRI

Science.gov (United States)

Vos, Pieter C.; Hambrock, Thomas; Barentsz, Jelle O.; Huisman, Henkjan J.

2010-03-01

For pharmacokinetic (PK) analysis of Dynamic Contrast Enhanced (DCE) MRI the arterial input function needs to be estimated. Previously, we demonstrated that PK parameters have a significant better discriminative performance when per patient reference tissue was used, but required manual annotation of reference tissue. In this study we propose a fully automated reference tissue segmentation method that tackles this limitation. The method was tested with our Computer Aided Diagnosis (CADx) system to study the effect on the discriminating performance for differentiating prostate cancer from benign areas in the peripheral zone (PZ). The proposed method automatically segments normal PZ tissue from DCE derived data. First, the bladder is segmented in the start-to-enhance map using the Otsu histogram threshold selection method. Second, the prostate is detected by applying a multi-scale Hessian filter to the relative enhancement map. Third, normal PZ tissue was segmented by threshold and morphological operators. The resulting segmentation was used as reference tissue to estimate the PK parameters. In 39 consecutive patients carcinoma, benign and normal tissue were annotated on MR images by a radiologist and a researcher using whole mount step-section histopathology as reference. PK parameters were computed for each ROI. Features were extracted from the set of ROIs using percentiles to train a support vector machine that was used as classifier. Prospective performance was estimated by means of leave-one-patient-out cross validation. A bootstrap resampling approach with 10,000 iterations was used for estimating the bootstrap mean AUCs and 95% confidence intervals. In total 42 malignant, 29 benign and 37 normal regions were annotated. For all patients, normal PZ was successfully segmented. The diagnostic accuracy obtained for differentiating malignant from benign lesions using a conventional general patient plasma profile showed an accuracy of 0.64 (0.53-0.74). Using the

Dynamic contrast-enhanced, extremity-dedicated MRI identifies synovitis changes in the follow-up of rheumatoid arthritis patients treated with rituximab

DEFF Research Database (Denmark)

Cimmino, Marco A; Parodi, Massimiliano; Zampogna, Giuseppe

2014-01-01

according to the 1987 ACR criteria were treated with a single course of RTX (2 infusions of 1000 mg, 15 days apart). MRI of the dominant hand was performed with a 0.2T extremity-dedicated machine using pre and post contrast T1 weighted SE, turbo 3D, and STIR sequences at baseline, and after 4 and 24 weeks....... MRI was analysed using the OMERACT-RAMRIS score and the dynamic contrast-enhanced (DCE-MRI) technique for wrist synovitis, which calculates the enhancement ratio as both rate of early enhancement (REE) and relative enhancement (RE). The corresponding ME and IRE parameters were calculated also through...

Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

Science.gov (United States)

Romeo, Valeria; Maurea, Simone; Cuocolo, Renato; Petretta, Mario; Mainenti, Pier Paolo; Verde, Francesco; Coppola, Milena; Dell'Aversana, Serena; Brunetti, Arturo

2018-01-17

Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest. To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach. Retrospective, observational study. Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL. Unenhanced T 1 -weighted in-phase (IP) and out-of-phase (OP) as well as T 2 -weighted (T 2 -w) MR images acquired at 3T. Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T 2 -w images. Different selection methods were trained and tested using the J48 machine-learning classifiers. The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test. A total of 138 TA-derived features were extracted; among these, four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP (Mean_Intensity and Maximum_3D_Diameter), and T 2 -w (Standard_Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%. The expert radiologist obtained a diagnostic accuracy of 73%. McNemar's test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist. Machine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions. 4 Technical Efficacy: Stage 2 J

Automated lesion detection on MRI scans using combined unsupervised and supervised methods

International Nuclear Information System (INIS)

Guo, Dazhou; Fridriksson, Julius; Fillmore, Paul; Rorden, Christopher; Yu, Hongkai; Zheng, Kang; Wang, Song

2015-01-01

Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. First, unsupervised methods perform a unified segmentation normalization to warp images from the native space into a standard space and to generate probability maps for different tissue types, e.g., gray matter, white matter and fluid. This allows us to construct an initial lesion probability map by comparing the normalized MRI to healthy control subjects. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection. We tested this method using T1-weighted MRIs from 60 in-house stroke patients. Using leave-one-out cross validation, the proposed method can achieve an average Dice coefficient of 73.1 % when compared to lesion maps hand-delineated by trained neurologists. Furthermore, we tested the proposed method on the T1-weighted MRIs in the MICCAI BRATS 2012 dataset. The proposed method can achieve an average Dice coefficient of 66.5 % in comparison to the expert annotated tumor maps provided in MICCAI BRATS 2012 dataset. In addition, on these two test datasets, the proposed method shows competitive performance to three state-of-the-art methods, including Stamatakis et al., Seghier et al., and Sanjuan et al. In this paper, we introduced a novel automated procedure for lesion detection from T1-weighted MRIs by combining both an unsupervised and a

Cross-Calibration and Comparison of Variability in Two Bone Densitometers in a Research Setting: The Framingham Experience

Science.gov (United States)

Gagnon, David R.; McLean, Robert R.; Hannan, Marian T.; Cupples, L. Adrienne; Hogan, Mary; Kiel, Douglas P.

2010-01-01

New technology introduced over time results in changes in densitometers during longitudinal studies of bone mineral density (BMD). This requires that a cross-calibration process be completed to translate measurements from the old densitometer to the new one. Previously described cross-calibration methods for research settings have collected single measures on each densitometer and used linear regression to estimate cross-calibration corrections. Thus, these methods may produce corrections that have limited precision and underestimate the variability in converted BMD values. Furthermore, most prior studies have included small samples recruited from specialized populations. Increasing the sample size, obtaining multiple measures on each machine, and utilizing linear mixed models to account for between- and within-subject variability may improve cross-calibration estimates. The purpose of this study was to conduct an in vivo cross-calibration of a Lunar DPX-L with a Lunar Prodigy densitometer using a sample of 249 healthy volunteers who were scanned twice on each densitometer, without repositioning, at both the femur and spine. Scans were analyzed using both automated and manual placement of regions of interest. Wilcoxon rank-sum tests and Bland-Altman plots were used to examine possible differences between repeat scans within and across densitometers. We used linear mixed models to determine the cross-calibration equations for the femoral neck, trochanter, total hip and lumbar spine (L2-L4) regions. Results using automated and manual placement of the regions of interest did not differ significantly The DPX–L exhibited larger median absolute differences in repeat scans for femoral neck [0.016 vs. 0.012, p=0.1] and trochanter [0.011 vs. 0.009, p=0.06] BMD values compared to the Prodigy. The Bland-Altman plots revealed no statistically significant linear relation between the difference in paired measures between machines and mean BMD. In our large sample of healthy

A single-mode data acquisition architecture for PET/MRI

Energy Technology Data Exchange (ETDEWEB)

Sportelli, Giancarlo; Belcari, Nicola; Bisogni, Maria Giuseppina; Camarlinghi, Niccolo; Zaccaro, Emanuele; Del Guerra, Alberto [Department of Physics, University of Pisa and INFN, Pisa (Italy)

2015-05-18

The development of MRI compatible detectors based on compact solid state photomultipliers has recently led to simultaneous fully integrated PET/MRI systems for human imaging. The PET acquisition design for MRI integration is known to have several additional constraints, including smaller space, electromagnetic compatibility issues and thermal management. The current work presents the PET acquisition architecture that has been developed for the TRIMAGE project, whose aim is to provide a cost effective, commercial grade trimodality PET/MRI/EEG scanner. The TRIMAGE PET component consists of 216 modules of 2.5 cm x 2.5 cm, arranged in 18 rectangular detectors of 5 cm x 15 cm, the latter in the axial direction, to form a full ring of 31 cm diameter. Each module consists of a staggered dual layer LYSO matrix read out by two arrays of 4 x 8 SiPMs and an ASIC. The detector board hosts a low-power low-end FPGA that performs pixel identification, energy calibration and handles the communication between the ASICs and the motherboard, which is located in proximity of the scanner. Data is streamed using high-density shielded cables and high-speed LVDS transmission to 9 low-end SoC FPGAs and from there to a central mainboard where coincidences and events statistics are processed. Coincidence data is finally transmitted to a host PC for image reconstruction. The proposed architecture and technological solutions will be presented and discussed.

A single-mode data acquisition architecture for PET/MRI

International Nuclear Information System (INIS)

Sportelli, Giancarlo; Belcari, Nicola; Bisogni, Maria Giuseppina; Camarlinghi, Niccolo; Zaccaro, Emanuele; Del Guerra, Alberto

2015-01-01

The development of MRI compatible detectors based on compact solid state photomultipliers has recently led to simultaneous fully integrated PET/MRI systems for human imaging. The PET acquisition design for MRI integration is known to have several additional constraints, including smaller space, electromagnetic compatibility issues and thermal management. The current work presents the PET acquisition architecture that has been developed for the TRIMAGE project, whose aim is to provide a cost effective, commercial grade trimodality PET/MRI/EEG scanner. The TRIMAGE PET component consists of 216 modules of 2.5 cm x 2.5 cm, arranged in 18 rectangular detectors of 5 cm x 15 cm, the latter in the axial direction, to form a full ring of 31 cm diameter. Each module consists of a staggered dual layer LYSO matrix read out by two arrays of 4 x 8 SiPMs and an ASIC. The detector board hosts a low-power low-end FPGA that performs pixel identification, energy calibration and handles the communication between the ASICs and the motherboard, which is located in proximity of the scanner. Data is streamed using high-density shielded cables and high-speed LVDS transmission to 9 low-end SoC FPGAs and from there to a central mainboard where coincidences and events statistics are processed. Coincidence data is finally transmitted to a host PC for image reconstruction. The proposed architecture and technological solutions will be presented and discussed.

A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations.

Science.gov (United States)

Levman, Jacob E D; Warner, Ellen; Causer, Petrina; Martel, Anne L

2014-02-01

This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results. An alternative vector machine formulation was found to improve on the results produced by the established SVM in randomized bootstrap validation trials, yielding a receiver-operating characteristic curve area of 0.82 which represents a statistically significant improvement over the SVM technique in this application.

Temperature and concentration calibration of aqueous polyvinylpyrrolidone (PVP solutions for isotropic diffusion MRI phantoms.

Directory of Open Access Journals (Sweden)

Friedrich Wagner

Full Text Available To use the "apparent diffusion coefficient" (Dapp as a quantitative imaging parameter, well-suited test fluids are essential. In this study, the previously proposed aqueous solutions of polyvinylpyrrolidone (PVP were examined and temperature calibrations were obtained. For example, at a temperature of 20°C, Dapp ranged from 1.594 (95% CI: 1.593, 1.595 μm2/ms to 0.3326 (95% CI: 0. 3304, 0.3348 μm2/ms for PVP-concentrations ranging from 10% (w/w to 50% (w/w using K30 polymer lengths. The temperature dependence of Dapp was found to be so strong that a negligence seems not advisable. The temperature dependence is descriptively modelled by an exponential function exp(c2 (T - 20°C and the determined c2 values are reported, which can be used for temperature calibration. For example, we find the value 0.02952 K-1 for 30% (w/w PVP-concentration and K30 polymer length. In general, aqueous PVP solutions were found to be suitable to produce easily applicable and reliable Dapp-phantoms.

Radiometric calibration of digital cameras using neural networks

Science.gov (United States)

Grunwald, Michael; Laube, Pascal; Schall, Martin; Umlauf, Georg; Franz, Matthias O.

2017-08-01

Digital cameras are used in a large variety of scientific and industrial applications. For most applications, the acquired data should represent the real light intensity per pixel as accurately as possible. However, digital cameras are subject to physical, electronic and optical effects that lead to errors and noise in the raw image. Temperature- dependent dark current, read noise, optical vignetting or different sensitivities of individual pixels are examples of such effects. The purpose of radiometric calibration is to improve the quality of the resulting images by reducing the influence of the various types of errors on the measured data and thus improving the quality of the overall application. In this context, we present a specialized neural network architecture for radiometric calibration of digital cameras. Neural networks are used to learn a temperature- and exposure-dependent mapping from observed gray-scale values to true light intensities for each pixel. In contrast to classical at-fielding, neural networks have the potential to model nonlinear mappings which allows for accurately capturing the temperature dependence of the dark current and for modeling cameras with nonlinear sensitivities. Both scenarios are highly relevant in industrial applications. The experimental comparison of our network approach to classical at-fielding shows a consistently higher reconstruction quality, also for linear cameras. In addition, the calibration is faster than previous machine learning approaches based on Gaussian processes.

Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.

Science.gov (United States)

Rachmadi, Muhammad Febrian; Valdés-Hernández, Maria Del C; Agan, Maria Leonora Fatimah; Di Perri, Carol; Komura, Taku

2018-06-01

We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology in routine clinical brain magnetic resonance images (MRI). This is a rather difficult segmentation problem because of the small area (i.e., volume) of the WMH and their similarity to non-pathological brain tissue. We investigate the effectiveness of the 2D CNN scheme by comparing its performance against those obtained from another deep learning approach: Deep Boltzmann Machine (DBM), two conventional machine learning approaches: Support Vector Machine (SVM) and Random Forest (RF), and a public toolbox: Lesion Segmentation Tool (LST), all reported to be useful for segmenting WMH in MRI. We also introduce a way to incorporate spatial information in convolution level of CNN for WMH segmentation named global spatial information (GSI). Analysis of covariance corroborated known associations between WMH progression, as assessed by all methods evaluated, and demographic and clinical data. Deep learning algorithms outperform conventional machine learning algorithms by excluding MRI artefacts and pathologies that appear similar to WMH. Our proposed approach of incorporating GSI also successfully helped CNN to achieve better automatic WMH segmentation regardless of network's settings tested. The mean Dice Similarity Coefficient (DSC) values for LST-LGA, SVM, RF, DBM, CNN and CNN-GSI were 0.2963, 0.1194, 0.1633, 0.3264, 0.5359 and 5389 respectively. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Automatic analysis of trabecular bone structure from knee MRI

DEFF Research Database (Denmark)

Marques, Joselene; Granlund, Rabia; Lillholm, Martin

2012-01-01

We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic......, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker. The performance of the developed marker reached a generalization area-under-the-ROC (AUC) of 0...

Heart MRI

Science.gov (United States)

Magnetic resonance imaging - cardiac; Magnetic resonance imaging - heart; Nuclear magnetic resonance - cardiac; NMR - cardiac; MRI of the heart; Cardiomyopathy - MRI; Heart failure - MRI; Congenital heart disease - MRI

Regression model of support vector machines for least squares prediction of crystallinity of cracking catalysts by infrared spectroscopy

International Nuclear Information System (INIS)

Comesanna Garcia, Yumirka; Dago Morales, Angel; Talavera Bustamante, Isneri

2010-01-01

The recently introduction of the least squares support vector machines method for regression purposes in the field of Chemometrics has provided several advantages to linear and nonlinear multivariate calibration methods. The objective of the paper was to propose the use of the least squares support vector machine as an alternative multivariate calibration method for the prediction of the percentage of crystallinity of fluidized catalytic cracking catalysts, by means of Fourier transform mid-infrared spectroscopy. A linear kernel was used in the calculations of the regression model. The optimization of its gamma parameter was carried out using the leave-one-out cross-validation procedure. The root mean square error of prediction was used to measure the performance of the model. The accuracy of the results obtained with the application of the method is in accordance with the uncertainty of the X-ray powder diffraction reference method. To compare the generalization capability of the developed method, a comparison study was carried out, taking into account the results achieved with the new model and those reached through the application of linear calibration methods. The developed method can be easily implemented in refinery laboratories

The natural history of partial rotator cuff tear evaluated by MRI. Can we predict the prognosis of partial rotator cuff tear by MRI?

International Nuclear Information System (INIS)

Matsuura, Koumei

2010-01-01

The cause and progress of the tear in the title are not fully understood and its treatment varies dependently on the injured site, depth, accompanied disease and symptom, and imaging profile. The author classified the tears in 4 types in MRI findings, followed their temporal progression and clinical symptom, and found that this classification in MRI finding was helpful to predict the prognosis, which is described in this paper. Subjects are 47 shoulders of 45 patients (M19/F26, av. age 71.0 y) who underwent conservative treatment of the disease during the time May, 2003-Oct. 2008, periodical MRI (2.7 times in av.) for 18.5 mo in av. and follow-up diagnosis until 24.9 mo in av. MRI is conducted with the machine 1.0 T Siemens harmonicdome to acquire the fast spin echo T1, T2 weighted images, and short inversion time inversion recovery (STIR) coronal, axial and sagittal ones. Tears in MRI are classified in Type 1 (abnormal signal type) (25 shoulders), Type 2 (abnormal signal and swelling type) (7 shoulders), Type 3 (cut off end type) (10 shoulders) and Type 4 (tapered end type) (5 shoulders). The partial rotator cuff tear is suggested to be originated from the denaturation of the cuff with a subsequent certain injury or loading to become Type 1 to 2 and to progress to Type 3 to 4 by continuous acrominal impingement. It is suggested that at the first diagnosis the Type 1 does not have so much serious symptom, but which tends to last long while the symptom is serious in Type 2 and 3: the prognosis in Type 4 is good. (K.T)

Characterization of sound emitted by wind machines used for frost control

Energy Technology Data Exchange (ETDEWEB)

Gambino, V.; Gambino, T. [Aercoustics Engineering Ltd., Toronto, ON (Canada); Fraser, H.W. [Ontario Ministry of Agriculture, Food and Rural Affairs, Vineland, ON (Canada)

2007-07-01

Wind machines are used in Niagara-on-the-Lake to protect cold-sensitive crops against cold injury during winter's extreme cold temperatures,spring's late frosts and autumn's early frosts. The number of wind machines in Ontario has about doubled annually from only a few in the late 1990's, to more than 425 in 2006. They are not used for generating power. Noise complaints have multiplied as the number of wind machines has increased. The objective of this study was to characterize the sound produced by wind machines; learn why residents are annoyed by wind machine noise; and suggest ways to possibly reduce sound emissions. One part of the study explored acoustic emission characteristics, the sonic differences of units made by different manufacturers, sound propagation properties under typical use atmospheric conditions and low frequency noise impact potential. Tests were conducted with a calibrated Larson Davis 2900B portable spectrum analyzer. Sound was measured with a microphone whose frequency response covered the range 4 Hz to 20 kHz. The study examined and found several unique acoustic properties that are characteristic of wind machines. It was determined that noise from wind machines is due to both aerodynamic and mechanical effects, but aerodynamic sounds were found to be the most significant. It was concluded that full range or broadband sounds manifest themselves as noise components that extend throughout the audible frequency range from the bladepass frequency to upwards of 1000 Hz. The sound spectrum of a wind machine is full natural tones and impulses that give it a readily identifiable acoustic character. Atmospheric conditions including temperature, lapse rate, relative humidity, mild winds, gradients and atmospheric turbulence all play a significant role in the long range outdoor propagation of sound from wind machines. 6 refs., 6 figs.

Automated SmartPrep tracker positioning in liver MRI scans

International Nuclear Information System (INIS)

Goto, Takao; Kabasawa, Hiroyuki

2013-01-01

This paper presents a new method for automated SmartPrep tracker positioning in liver MRI scans. SmartPrep is used to monitor the contrast bolus signal in order to detect the arrival time of the bolus. Accurately placing the tracker in the aorta while viewing three planar scout images is a difficult task for the operator and is an important problem from the workflow standpoint. The development of an automated SmartPrep tracker would therefore help to improve workflow in liver MRI scans. In our proposed method, the aorta is detected using AdaBoost (which is a machine learning technique) by searching around the cerebral spinal fluid (CSF) in the spinal cord. Analysis of scout scan images showed that our detection method functioned properly for a variety of axial MR images without intensity correction. A total of 234 images reconstructed from the datasets of 64 volunteers were analyzed, and the results showed that the detection error for the aorta was approximately 3 mm. (author)

MRI STUDY OF TYPES AND INCIDENCE OF INTERNAL DERANGEMENTS OF TRAUMATIC KNEE JOINT

Directory of Open Access Journals (Sweden)

Bomidi Sudha Rani

2016-12-01

Full Text Available BACKGROUND MRI has been accepted as the best imaging modality for noninvasive evaluation of knee injuries and it has proved reliable, safe and offers advantages over diagnostic arthroscopy, which is currently regarded as the reference standard for the diagnosis of internal derangements of the knee. 1 METHODS AND MATERIALS A prospective study of fifty patients who underwent MRI for the diagnosis of internal derangement of knee was conducted between the period of January 2015 to January 2016 in Government General Hospital, Kakinada. All the patients with history of knee joint pain following trauma and clinically suspected to have meniscal and ligament tears are included in the study. Patients were evaluated using GE 1.5 T MRI machine with pulsar gradient system using a sensor extremity coil. RESULTS Commonest lesion detected in our study was ACL tear followed by medial meniscal tear and medial collateral ligament injury. The most common sign of cruciate ligament injury was hyperintensity in the ligament. Grade 3 was the most common grade of meniscal tear. CONCLUSION MRI is an excellent, noninvasive, radiation free imaging modality and is unique in its ability to evaluate the internal structure as well as soft tissue delineation. Many anatomical variants can mimic a tear on MRI. MRI is an excellent noninvasive modality for imaging the knee and helps in arriving at a correct anatomical diagnosis there by guiding further management of the patient.

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.

Monitoring local heating around an interventional MRI antenna with RF radiometry

Energy Technology Data Exchange (ETDEWEB)

Ertürk, M. Arcan [Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21287 and Division of MR Research, Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland 21287 (United States); El-Sharkawy, AbdEl-Monem M.; Bottomley, Paul A., E-mail: bottoml@mri.jhu.edu [Division of MR Research, Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland 21287 (United States)

2015-03-15

Purpose: Radiofrequency (RF) radiometry uses thermal noise detected by an antenna to measure the temperature of objects independent of medical imaging technologies such as magnetic resonance imaging (MRI). Here, an active interventional MRI antenna can be deployed as a RF radiometer to measure local heating, as a possible new method of monitoring device safety and thermal therapy. Methods: A 128 MHz radiometer receiver was fabricated to measure the RF noise voltage from an interventional 3 T MRI loopless antenna and calibrated for temperature in a uniformly heated bioanalogous gel phantom. Local heating (ΔT) was induced using the antenna for RF transmission and measured by RF radiometry, fiber-optic thermal sensors, and MRI thermometry. The spatial thermal sensitivity of the antenna radiometer was numerically computed using a method-of-moment electric field analyses. The gel’s thermal conductivity was measured by MRI thermometry, and the localized time-dependent ΔT distribution computed from the bioheat transfer equation and compared with radiometry measurements. A “H-factor” relating the 1 g-averaged ΔT to the radiometric temperature was introduced to estimate peak temperature rise in the antenna’s sensitive region. Results: The loopless antenna radiometer linearly tracked temperature inside a thermally equilibrated phantom up to 73 °C to within ±0.3 °C at a 2 Hz sample rate. Computed and MRI thermometric measures of peak ΔT agreed within 13%. The peak 1 g-average temperature was H = 1.36 ± 0.02 times higher than the radiometric temperature for any media with a thermal conductivity of 0.15–0.50 (W/m)/K, indicating that the radiometer can measure peak 1 g-averaged ΔT in physiologically relevant tissue within ±0.4 °C. Conclusions: Active internal MRI detectors can serve as RF radiometers at the MRI frequency to provide accurate independent measures of local and peak temperature without the artifacts that can accompany MRI thermometry or

Monitoring local heating around an interventional MRI antenna with RF radiometry

Science.gov (United States)

Ertürk, M. Arcan; El-Sharkawy, AbdEl-Monem M.; Bottomley, Paul A.

2015-01-01

Purpose: Radiofrequency (RF) radiometry uses thermal noise detected by an antenna to measure the temperature of objects independent of medical imaging technologies such as magnetic resonance imaging (MRI). Here, an active interventional MRI antenna can be deployed as a RF radiometer to measure local heating, as a possible new method of monitoring device safety and thermal therapy. Methods: A 128 MHz radiometer receiver was fabricated to measure the RF noise voltage from an interventional 3 T MRI loopless antenna and calibrated for temperature in a uniformly heated bioanalogous gel phantom. Local heating (ΔT) was induced using the antenna for RF transmission and measured by RF radiometry, fiber-optic thermal sensors, and MRI thermometry. The spatial thermal sensitivity of the antenna radiometer was numerically computed using a method-of-moment electric field analyses. The gel’s thermal conductivity was measured by MRI thermometry, and the localized time-dependent ΔT distribution computed from the bioheat transfer equation and compared with radiometry measurements. A “H-factor” relating the 1 g-averaged ΔT to the radiometric temperature was introduced to estimate peak temperature rise in the antenna’s sensitive region. Results: The loopless antenna radiometer linearly tracked temperature inside a thermally equilibrated phantom up to 73 °C to within ±0.3 °C at a 2 Hz sample rate. Computed and MRI thermometric measures of peak ΔT agreed within 13%. The peak 1 g-average temperature was H = 1.36 ± 0.02 times higher than the radiometric temperature for any media with a thermal conductivity of 0.15–0.50 (W/m)/K, indicating that the radiometer can measure peak 1 g-averaged ΔT in physiologically relevant tissue within ±0.4 °C. Conclusions: Active internal MRI detectors can serve as RF radiometers at the MRI frequency to provide accurate independent measures of local and peak temperature without the artifacts that can accompany MRI thermometry or

Monitoring local heating around an interventional MRI antenna with RF radiometry

International Nuclear Information System (INIS)

Ertürk, M. Arcan; El-Sharkawy, AbdEl-Monem M.; Bottomley, Paul A.

2015-01-01

Purpose: Radiofrequency (RF) radiometry uses thermal noise detected by an antenna to measure the temperature of objects independent of medical imaging technologies such as magnetic resonance imaging (MRI). Here, an active interventional MRI antenna can be deployed as a RF radiometer to measure local heating, as a possible new method of monitoring device safety and thermal therapy. Methods: A 128 MHz radiometer receiver was fabricated to measure the RF noise voltage from an interventional 3 T MRI loopless antenna and calibrated for temperature in a uniformly heated bioanalogous gel phantom. Local heating (ΔT) was induced using the antenna for RF transmission and measured by RF radiometry, fiber-optic thermal sensors, and MRI thermometry. The spatial thermal sensitivity of the antenna radiometer was numerically computed using a method-of-moment electric field analyses. The gel’s thermal conductivity was measured by MRI thermometry, and the localized time-dependent ΔT distribution computed from the bioheat transfer equation and compared with radiometry measurements. A “H-factor” relating the 1 g-averaged ΔT to the radiometric temperature was introduced to estimate peak temperature rise in the antenna’s sensitive region. Results: The loopless antenna radiometer linearly tracked temperature inside a thermally equilibrated phantom up to 73 °C to within ±0.3 °C at a 2 Hz sample rate. Computed and MRI thermometric measures of peak ΔT agreed within 13%. The peak 1 g-average temperature was H = 1.36 ± 0.02 times higher than the radiometric temperature for any media with a thermal conductivity of 0.15–0.50 (W/m)/K, indicating that the radiometer can measure peak 1 g-averaged ΔT in physiologically relevant tissue within ±0.4 °C. Conclusions: Active internal MRI detectors can serve as RF radiometers at the MRI frequency to provide accurate independent measures of local and peak temperature without the artifacts that can accompany MRI thermometry or

Machine rates for selected forest harvesting machines

Science.gov (United States)

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

Directory of Open Access Journals (Sweden)

Leland S Hu

Full Text Available Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM. Contrast-enhanced MRI (CE-MRI targets enhancing core (ENH but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT, despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients. The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients.Multi-parametric MRI and texture analysis can help characterize and visualize GBM's spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning.

Science.gov (United States)

Li, Lingli; Fan, Wenliang; Li, Jun; Li, Quanlin; Wang, Jin; Fan, Yang; Ye, Tianhe; Guo, Jialun; Li, Sen; Zhang, Youpeng; Cheng, Yongbiao; Tang, Yong; Zeng, Hanqing; Yang, Lian; Zhu, Zhaohui

2018-03-29

To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a machine learning classification. 45 VED patients and 50 healthy controls were included. Voxel-based morphometry (VBM), tract-based spatial statistics (TBSS) and correlation analyses of VED patients and clinical variables were performed. The machine learning classification method was adopted to confirm its effectiveness in distinguishing VED patients from healthy controls. Compared to healthy control subjects, VED patients showed significantly decreased cortical volumes in the left postcentral gyrus and precentral gyrus, while only the right middle temporal gyrus showed a significant increase in cortical volume. Increased axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) values were observed in widespread brain regions. Certain regions of these alterations related to VED patients showed significant correlations with clinical symptoms and disorder durations. Machine learning analyses discriminated patients from controls with overall accuracy 96.7%, sensitivity 93.3% and specificity 99.0%. Cortical volume and white matter (WM) microstructural changes were observed in VED patients, and showed significant correlations with clinical symptoms and dysfunction durations. Various DTI-derived indices of some brain regions could be regarded as reliable discriminating features between VED patients and healthy control subjects, as shown by machine learning analyses. • Multimodal magnetic resonance imaging helps clinicians to assess patients with VED. • VED patients show cerebral structural alterations related to their clinical symptoms. • Machine learning analyses discriminated VED patients from controls with an excellent performance. • Machine learning classification provided a preliminary demonstration of DTI

Design and production of a novel sand materials strength testing machine for foundry applications

DEFF Research Database (Denmark)

Nwaogu, Ugochukwu Chibuzoh; Hansen, K. S.; Tiedje, Niels Skat

2012-01-01

testing machine was designed and built for both green sand and chemically-bonded sand materials. This machine measures and presents the loading response as a force-displacement profile from which the mechanical properties of the moulding materials can be deduced. The system was interfaced to a computer......In the foundry, existing strength testing machines are used to measure only the maximum fracture strength of mould and core materials. With traditionally used methods, the loading history to ascertain deformation of the material is not available. In this paper, a novel moulding material strength...... with a commercial PC based-control and data acquisition software. The testing conditions and operations are specified in the user interface and the data acquisition is made according to specifications. The force and displacements were calibrated to ensure consistency and reliability of the measurement data...

Rapid geodesic mapping of brain functional connectivity: implementation of a dedicated co-processor in a field-programmable gate array (FPGA) and application to resting state functional MRI.

Science.gov (United States)

Minati, Ludovico; Cercignani, Mara; Chan, Dennis

2013-10-01

Graph theory-based analyses of brain network topology can be used to model the spatiotemporal correlations in neural activity detected through fMRI, and such approaches have wide-ranging potential, from detection of alterations in preclinical Alzheimer's disease through to command identification in brain-machine interfaces. However, due to prohibitive computational costs, graph-based analyses to date have principally focused on measuring connection density rather than mapping the topological architecture in full by exhaustive shortest-path determination. This paper outlines a solution to this problem through parallel implementation of Dijkstra's algorithm in programmable logic. The processor design is optimized for large, sparse graphs and provided in full as synthesizable VHDL code. An acceleration factor between 15 and 18 is obtained on a representative resting-state fMRI dataset, and maps of Euclidean path length reveal the anticipated heterogeneous cortical involvement in long-range integrative processing. These results enable high-resolution geodesic connectivity mapping for resting-state fMRI in patient populations and real-time geodesic mapping to support identification of imagined actions for fMRI-based brain-machine interfaces. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy.

Science.gov (United States)

Wang, Jiahui; Fan, Zheng; Vandenborne, Krista; Walter, Glenn; Shiloh-Malawsky, Yael; An, Hongyu; Kornegay, Joe N; Styner, Martin A

2013-09-01

Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semiautomated system to quantify MRI biomarkers of GRMD. Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using a semiautomated full muscle segmentation method. We then performed preprocessing, including intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted and T2-weighted fat-suppressed images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume, intensity statistics over MRI biomarker maps, and statistical image texture features. The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation showed significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation. The experimental results demonstrated that this quantification tool could reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients.

MRI-based quantification of Duchenne muscular dystrophy in a canine model

Science.gov (United States)

Wang, Jiahui; Fan, Zheng; Kornegay, Joe N.; Styner, Martin A.

2011-03-01

Duchenne muscular dystrophy (DMD) is a progressive and fatal X-linked disease caused by mutations in the DMD gene. Magnetic resonance imaging (MRI) has shown potential to provide non-invasive and objective biomarkers for monitoring disease progression and therapeutic effect in DMD. In this paper, we propose a semi-automated scheme to quantify MRI features of golden retriever muscular dystrophy (GRMD), a canine model of DMD. Our method was applied to a natural history data set and a hydrodynamic limb perfusion data set. The scheme is composed of three modules: pre-processing, muscle segmentation, and feature analysis. The pre-processing module includes: calculation of T2 maps, spatial registration of T2 weighted (T2WI) images, T2 weighted fat suppressed (T2FS) images, and T2 maps, and intensity calibration of T2WI and T2FS images. We then manually segment six pelvic limb muscles. For each of the segmented muscles, we finally automatically measure volume and intensity statistics of the T2FS images and T2 maps. For the natural history study, our results showed that four of six muscles in affected dogs had smaller volumes and all had higher mean intensities in T2 maps as compared to normal dogs. For the perfusion study, the muscle volumes and mean intensities in T2FS were increased in the post-perfusion MRI scans as compared to pre-perfusion MRI scans, as predicted. We conclude that our scheme successfully performs quantitative analysis of muscle MRI features of GRMD.

TU-F-BRB-01: Resolving and Characterizing Breathing Motion for Radiotherapy with MRI

Energy Technology Data Exchange (ETDEWEB)

Tryggestad, E. [Mayo Clinic (United States)

2015-06-15

The current clinical standard of organ respiratory imaging, 4D-CT, is fundamentally limited by poor soft-tissue contrast and imaging dose. These limitations are potential barriers to beneficial “4D” radiotherapy methods which optimize the target and OAR dose-volume considering breathing motion but rely on a robust motion characterization. Conversely, MRI imparts no known radiation risk and has excellent soft-tissue contrast. MRI-based motion management is therefore highly desirable and holds great promise to improve radiotherapy of moving cancers, particularly in the abdomen. Over the past decade, MRI techniques have improved significantly, making MR-based motion management clinically feasible. For example, cine MRI has high temporal resolution up to 10 f/s and has been used to track and/or characterize tumor motion, study correlation between external and internal motions. New MR technologies, such as 4D-MRI and MRI hybrid treatment machines (i.e. MR-linac or MR-Co60), have been recently developed. These technologies can lead to more accurate target volume determination and more precise radiation dose delivery via direct tumor gating or tracking. Despite all these promises, great challenges exist and the achievable clinical benefit of MRI-based tumor motion management has yet to be fully explored, much less realized. In this proposal, we will review novel MR-based motion management methods and technologies, the state-of-the-art concerning MRI development and clinical application and the barriers to more widespread adoption. Learning Objectives: Discuss the need of MR-based motion management for improving patient care in radiotherapy. Understand MR techniques for motion imaging and tumor motion characterization. Understand the current state of the art and future steps for clinical integration. Henry Ford Health System holds research agreements with Philips Healthcare. Research sponsored in part by a Henry Ford Health System Internal Mentored Grant.

TU-F-BRB-01: Resolving and Characterizing Breathing Motion for Radiotherapy with MRI

International Nuclear Information System (INIS)

Tryggestad, E.

2015-01-01

The current clinical standard of organ respiratory imaging, 4D-CT, is fundamentally limited by poor soft-tissue contrast and imaging dose. These limitations are potential barriers to beneficial “4D” radiotherapy methods which optimize the target and OAR dose-volume considering breathing motion but rely on a robust motion characterization. Conversely, MRI imparts no known radiation risk and has excellent soft-tissue contrast. MRI-based motion management is therefore highly desirable and holds great promise to improve radiotherapy of moving cancers, particularly in the abdomen. Over the past decade, MRI techniques have improved significantly, making MR-based motion management clinically feasible. For example, cine MRI has high temporal resolution up to 10 f/s and has been used to track and/or characterize tumor motion, study correlation between external and internal motions. New MR technologies, such as 4D-MRI and MRI hybrid treatment machines (i.e. MR-linac or MR-Co60), have been recently developed. These technologies can lead to more accurate target volume determination and more precise radiation dose delivery via direct tumor gating or tracking. Despite all these promises, great challenges exist and the achievable clinical benefit of MRI-based tumor motion management has yet to be fully explored, much less realized. In this proposal, we will review novel MR-based motion management methods and technologies, the state-of-the-art concerning MRI development and clinical application and the barriers to more widespread adoption. Learning Objectives: Discuss the need of MR-based motion management for improving patient care in radiotherapy. Understand MR techniques for motion imaging and tumor motion characterization. Understand the current state of the art and future steps for clinical integration. Henry Ford Health System holds research agreements with Philips Healthcare. Research sponsored in part by a Henry Ford Health System Internal Mentored Grant

Multiparametric computer-aided differential diagnosis of Alzheimer's disease and frontotemporal dementia using structural and advanced MRI

Energy Technology Data Exchange (ETDEWEB)

Bron, Esther E.; Klein, Stefan [Erasmus MC, Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology and Nuclear Medicine, Office Na2502, P.O. Box 2040, Rotterdam (Netherlands); Smits, Marion; Steketee, Rebecca M.E.; Meijboom, Rozanna [Erasmus MC, Department of Radiology and Nuclear Medicine, Rotterdam (Netherlands); Papma, Janne M.; Swieten, John C. van [Erasmus MC, Department of Neurology, Rotterdam (Netherlands); Groot, Marius de [Erasmus MC, Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology and Nuclear Medicine, Office Na2502, P.O. Box 2040, Rotterdam (Netherlands); Erasmus MC, Department of Epidemiology, Rotterdam (Netherlands); Niessen, Wiro J. [Erasmus MC, Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology and Nuclear Medicine, Office Na2502, P.O. Box 2040, Rotterdam (Netherlands); Delft University of Technology, Imaging Physics, Applied Sciences, Delft (Netherlands)

2017-08-15

To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls. This retrospective study used MRI data from 24 early-onset AD and 33 early-onset FTD patients and 34 controls (CN). Classification was based on voxel-wise feature maps derived from structural MRI, ASL, and DTI. Support vector machines (SVMs) were trained to classify AD versus CN (AD-CN), FTD-CN, AD-FTD, and AD-FTD-CN (multi-class). Classification performance was assessed by the area under the receiver-operating-characteristic curve (AUC) and accuracy. Using SVM significance maps, we analysed contributions of brain regions. Combining ASL and DTI with structural MRI resulted in higher classification performance for differential diagnosis of AD and FTD (AUC = 84%; p = 0.05) than using structural MRI by itself (AUC = 72%). The performance of ASL and DTI themselves did not improve over structural MRI. The classifications were driven by different brain regions for ASL and DTI than for structural MRI, suggesting complementary information. ASL and DTI are promising additions to structural MRI for classification of early-onset AD, early-onset FTD, and controls, and may improve the computer-aided differential diagnosis on a single-subject level. (orig.)

Another look at volume self-calibration: calibration and self-calibration within a pinhole model of Scheimpflug cameras

International Nuclear Information System (INIS)

Cornic, Philippe; Le Besnerais, Guy; Champagnat, Frédéric; Illoul, Cédric; Cheminet, Adam; Le Sant, Yves; Leclaire, Benjamin

2016-01-01

We address calibration and self-calibration of tomographic PIV experiments within a pinhole model of cameras. A complete and explicit pinhole model of a camera equipped with a 2-tilt angles Scheimpflug adapter is presented. It is then used in a calibration procedure based on a freely moving calibration plate. While the resulting calibrations are accurate enough for Tomo-PIV, we confirm, through a simple experiment, that they are not stable in time, and illustrate how the pinhole framework can be used to provide a quantitative evaluation of geometrical drifts in the setup. We propose an original self-calibration method based on global optimization of the extrinsic parameters of the pinhole model. These methods are successfully applied to the tomographic PIV of an air jet experiment. An unexpected by-product of our work is to show that volume self-calibration induces a change in the world frame coordinates. Provided the calibration drift is small, as generally observed in PIV, the bias on the estimated velocity field is negligible but the absolute location cannot be accurately recovered using standard calibration data. (paper)

Brain response pattern identification of fMRI data using a particle swarm optimization-based approach.

Science.gov (United States)

Ma, Xinpei; Chou, Chun-An; Sayama, Hiroki; Chaovalitwongse, Wanpracha Art

2016-09-01

Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions. To solve the problem, we propose a feature interaction detection framework, integrating hierarchical heterogeneous particle swarm optimization and support vector machines, for voxel selection in MVPA. In the proposed approach, we first select the most informative voxels and then identify a response pattern based on the connectivity of the selected voxels. The effectiveness of the proposed approach was examined for the Haxby's dataset of object-level representations. The computational results demonstrated higher classification accuracy by the extracted response patterns, compared to state-of-the-art feature selection algorithms, such as forward selection and backward selection.

[A new machinability test machine and the machinability of composite resins for core built-up].

Science.gov (United States)

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

Activities on calibration of radiation protection instruments in Indonesia

International Nuclear Information System (INIS)

Trijoko, S.

1995-01-01

As the use of the ionizing radiation emitted by radionuclides or produced by modern machines in Indonesia has increased significantly in the past two decades, the demand for radiation protection measures has also grown up very rapidly. In the mind of Indonesian people, ionizing radiation is always associated with atomic bombs. Indonesian government has set up National Atomic Energy Agency (BATAN) through the Act No. 31/1964. The BATAN has responsibility in the research and development, implementation and inspection of the safe use of ionizing radiation for peaceful purposes, and always put a great concern on radiation protection matter. The Center for Standardization and Radiation Safety Research (CSRSR) has been founded to implement research and services in the fields of radiation safety, standardization, dosimetry, radiation health, as well as the application of nuclear techniques to medicine. In order to provide the national reference in terms of radiation dosimetry and calibration, the Secondary Standard Dosimetry Laboratory was completely set up in Jakarta by 1984. As available facilities, radiation instruments and radiation sources are described. Calibration and personal monitoring services are reported. (K.I.)

Calibration method for a vision guiding-based laser-tracking measurement system

International Nuclear Information System (INIS)

Shao, Mingwei; Wei, Zhenzhong; Hu, Mengjie; Zhang, Guangjun

2015-01-01

Laser-tracking measurement systems (laser trackers) based on a vision-guiding device are widely used in industrial fields, and their calibration is important. As conventional methods typically have many disadvantages, such as difficult machining of the target and overdependence on the retroreflector, a novel calibration method is presented in this paper. The retroreflector, which is necessary in the normal calibration method, is unnecessary in our approach. As the laser beam is linear, points on the beam can be obtained with the help of a normal planar target. In this way, we can determine the function of a laser beam under the camera coordinate system, while its corresponding function under the laser-tracker coordinate system can be obtained from the encoder of the laser tracker. Clearly, when several groups of functions are confirmed, the rotation matrix can be solved from the direction vectors of the laser beams in different coordinate systems. As the intersection of the laser beams is the origin of the laser-tracker coordinate system, the translation matrix can also be determined. Our proposed method not only achieves the calibration of a single laser-tracking measurement system but also provides a reference for the calibration of a multistation system. Simulations to evaluate the effects of some critical factors were conducted. These simulations show the robustness and accuracy of our method. In real experiments, the root mean square error of the calibration result reached 1.46 mm within a range of 10 m, even though the vision-guiding device focuses on a point approximately 5 m away from the origin of its coordinate system, with a field of view of approximately 200 mm  ×  200 mm. (paper)

Cost-Benefit Analysis of Computer Resources for Machine Learning

Science.gov (United States)

Champion, Richard A.

2007-01-01

Machine learning describes pattern-recognition algorithms - in this case, probabilistic neural networks (PNNs). These can be computationally intensive, in part because of the nonlinear optimizer, a numerical process that calibrates the PNN by minimizing a sum of squared errors. This report suggests efficiencies that are expressed as cost and benefit. The cost is computer time needed to calibrate the PNN, and the benefit is goodness-of-fit, how well the PNN learns the pattern in the data. There may be a point of diminishing returns where a further expenditure of computer resources does not produce additional benefits. Sampling is suggested as a cost-reduction strategy. One consideration is how many points to select for calibration and another is the geometric distribution of the points. The data points may be nonuniformly distributed across space, so that sampling at some locations provides additional benefit while sampling at other locations does not. A stratified sampling strategy can be designed to select more points in regions where they reduce the calibration error and fewer points in regions where they do not. Goodness-of-fit tests ensure that the sampling does not introduce bias. This approach is illustrated by statistical experiments for computing correlations between measures of roadless area and population density for the San Francisco Bay Area. The alternative to training efficiencies is to rely on high-performance computer systems. These may require specialized programming and algorithms that are optimized for parallel performance.

Development of radio frequency (RF) and microwave (MW) calibration facility using GTEM cell

International Nuclear Information System (INIS)

Rozaimah Abd Rahim; Mohd Yusof Mohd Ali; Mohd Anuar Abd Majid

2005-01-01

Recent studies indicate that non ionizing radiation (NIR) can cause health effect on human beings. Usage of equipment/machine/system in industrial sector, medical fields, surveillance, telecommunication, broadcast, weather forecast and some consumer product that can produced NIR attract public concern about the potential hazard cause by this radiation. It increases public demands on the use of suitable equipment/survey meters for measuring NIR radiation from such sources. This equipment/survey meters need to be calibrated to insure that the equipment/survey meters give correct, accurate and precise reading. One of the systems that can be used to do the calibration work is GTEM cell. GTEM cell can also be used to perform tests involving electromagnetic fields such as electromagnetic compatibility test (EMC), electromagnetic immunity or susceptibility test (EMS) and electromagnetic interference test (EMI). This paper highlights some of the work that had been carried out to map out the field strengths within the GTEM cell and test results of some of the calibration work performed on RF measuring instrument. (Author)

Quantitative MRI for hepatic fat fraction and T2* measurement in pediatric patients with non-alcoholic fatty liver disease.

Science.gov (United States)

Deng, Jie; Fishbein, Mark H; Rigsby, Cynthia K; Zhang, Gang; Schoeneman, Samantha E; Donaldson, James S

2014-11-01

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in children. The gold standard for diagnosis is liver biopsy. MRI is a non-invasive imaging method to provide quantitative measurement of hepatic fat content. The methodology is particularly appealing for the pediatric population because of its rapidity and radiation-free imaging techniques. To develop a multi-point Dixon MRI method with multi-interference models (multi-fat-peak modeling and bi-exponential T2* correction) for accurate hepatic fat fraction (FF) and T2* measurements in pediatric patients with NAFLD. A phantom study was first performed to validate the accuracy of the MRI fat fraction measurement by comparing it with the chemical fat composition of the ex-vivo pork liver-fat homogenate. The most accurate model determined from the phantom study was used for fat fraction and T2* measurements in 52 children and young adults referred from the pediatric hepatology clinic with suspected or identified NAFLD. Separate T2* values of water (T2*W) and fat (T2*F) components derived from the bi-exponential fitting were evaluated and plotted as a function of fat fraction. In ten patients undergoing liver biopsy, we compared histological analysis of liver fat fraction with MRI fat fraction. In the phantom study the 6-point Dixon with 5-fat-peak, bi-exponential T2* modeling demonstrated the best precision and accuracy in fat fraction measurements compared with other methods. This model was further calibrated with chemical fat fraction and applied in patients, where similar patterns were observed as in the phantom study that conventional 2-point and 3-point Dixon methods underestimated fat fraction compared to the calibrated 6-point 5-fat-peak bi-exponential model (P fat fraction, T2*W (27.9 ± 3.5 ms) decreased, whereas T2*F (20.3 ± 5.5 ms) increased; and T2*W and T2*F became increasingly more similar when fat fraction was higher than 15-20%. Histological fat

Quantitative MRI for hepatic fat fraction and T2* measurement in pediatric patients with non-alcoholic fatty liver disease

Energy Technology Data Exchange (ETDEWEB)

Deng, Jie; Rigsby, Cynthia K.; Donaldson, James S. [Ann and Robert H. Lurie Children' s Hospital of Chicago, Department of Medical Imaging, Chicago, IL (United States); Northwestern University, Department of Radiology, Feinberg School of Medicine, Chicago, IL (United States); Fishbein, Mark H. [Ann and Robert H. Lurie Children' s Hospital of Chicago, Division of Gastroenterology, Hepatology, and Nutrition, Chicago, IL (United States); Zhang, Gang [Ann and Robert H. Lurie Children' s Hospital of Chicago, Biostatistics Research Core, Chicago, IL (United States); Schoeneman, Samantha E. [Ann and Robert H. Lurie Children' s Hospital of Chicago, Department of Medical Imaging, Chicago, IL (United States)

2014-11-15

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in children. The gold standard for diagnosis is liver biopsy. MRI is a non-invasive imaging method to provide quantitative measurement of hepatic fat content. The methodology is particularly appealing for the pediatric population because of its rapidity and radiation-free imaging techniques. To develop a multi-point Dixon MRI method with multi-interference models (multi-fat-peak modeling and bi-exponential T2* correction) for accurate hepatic fat fraction (FF) and T2* measurements in pediatric patients with NAFLD. A phantom study was first performed to validate the accuracy of the MRI fat fraction measurement by comparing it with the chemical fat composition of the ex-vivo pork liver-fat homogenate. The most accurate model determined from the phantom study was used for fat fraction and T2* measurements in 52 children and young adults referred from the pediatric hepatology clinic with suspected or identified NAFLD. Separate T2* values of water (T2*{sub W}) and fat (T2*{sub F}) components derived from the bi-exponential fitting were evaluated and plotted as a function of fat fraction. In ten patients undergoing liver biopsy, we compared histological analysis of liver fat fraction with MRI fat fraction. In the phantom study the 6-point Dixon with 5-fat-peak, bi-exponential T2* modeling demonstrated the best precision and accuracy in fat fraction measurements compared with other methods. This model was further calibrated with chemical fat fraction and applied in patients, where similar patterns were observed as in the phantom study that conventional 2-point and 3-point Dixon methods underestimated fat fraction compared to the calibrated 6-point 5-fat-peak bi-exponential model (P < 0.0001). With increasing fat fraction, T2*{sub W} (27.9 ± 3.5 ms) decreased, whereas T2*{sub F} (20.3 ± 5.5 ms) increased; and T2*{sub W} and T2*{sub F} became increasingly more similar when fat

Calibration uncertainty

DEFF Research Database (Denmark)

Heydorn, Kaj; Anglov, Thomas

2002-01-01

Methods recommended by the International Standardization Organisation and Eurachem are not satisfactory for the correct estimation of calibration uncertainty. A novel approach is introduced and tested on actual calibration data for the determination of Pb by ICP-AES. The improved calibration...

Miniaturisation of Pressure-Sensitive Paint Measurement Systems Using Low-Cost, Miniaturised Machine Vision Cameras.

Science.gov (United States)

Quinn, Mark Kenneth; Spinosa, Emanuele; Roberts, David A

2017-07-25

Measurements of pressure-sensitive paint (PSP) have been performed using new or non-scientific imaging technology based on machine vision tools. Machine vision camera systems are typically used for automated inspection or process monitoring. Such devices offer the benefits of lower cost and reduced size compared with typically scientific-grade cameras; however, their optical qualities and suitability have yet to be determined. This research intends to show relevant imaging characteristics and also show the applicability of such imaging technology for PSP. Details of camera performance are benchmarked and compared to standard scientific imaging equipment and subsequent PSP tests are conducted using a static calibration chamber. The findings demonstrate that machine vision technology can be used for PSP measurements, opening up the possibility of performing measurements on-board small-scale model such as those used for wind tunnel testing or measurements in confined spaces with limited optical access.

Miniaturisation of Pressure-Sensitive Paint Measurement Systems Using Low-Cost, Miniaturised Machine Vision Cameras

Directory of Open Access Journals (Sweden)

Mark Kenneth Quinn

2017-07-01

Full Text Available Measurements of pressure-sensitive paint (PSP have been performed using new or non-scientific imaging technology based on machine vision tools. Machine vision camera systems are typically used for automated inspection or process monitoring. Such devices offer the benefits of lower cost and reduced size compared with typically scientific-grade cameras; however, their optical qualities and suitability have yet to be determined. This research intends to show relevant imaging characteristics and also show the applicability of such imaging technology for PSP. Details of camera performance are benchmarked and compared to standard scientific imaging equipment and subsequent PSP tests are conducted using a static calibration chamber. The findings demonstrate that machine vision technology can be used for PSP measurements, opening up the possibility of performing measurements on-board small-scale model such as those used for wind tunnel testing or measurements in confined spaces with limited optical access.

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

International Nuclear Information System (INIS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Brink, Henrik; Crellin-Quick, Arien; Butler, Nathaniel R.

2012-01-01

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

Energy Technology Data Exchange (ETDEWEB)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Brink, Henrik; Crellin-Quick, Arien [Astronomy Department, University of California, Berkeley, CA 94720-3411 (United States); Butler, Nathaniel R., E-mail: jwrichar@stat.berkeley.edu [School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287 (United States)

2012-12-15

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

Human-machine interface for a VR-based medical imaging environment

Science.gov (United States)

Krapichler, Christian; Haubner, Michael; Loesch, Andreas; Lang, Manfred K.; Englmeier, Karl-Hans

1997-05-01

Modern 3D scanning techniques like magnetic resonance imaging (MRI) or computed tomography (CT) produce high- quality images of the human anatomy. Virtual environments open new ways to display and to analyze those tomograms. Compared with today's inspection of 2D image sequences, physicians are empowered to recognize spatial coherencies and examine pathological regions more facile, diagnosis and therapy planning can be accelerated. For that purpose a powerful human-machine interface is required, which offers a variety of tools and features to enable both exploration and manipulation of the 3D data. Man-machine communication has to be intuitive and efficacious to avoid long accustoming times and to enhance familiarity with and acceptance of the interface. Hence, interaction capabilities in virtual worlds should be comparable to those in the real work to allow utilization of our natural experiences. In this paper the integration of hand gestures and visual focus, two important aspects in modern human-computer interaction, into a medical imaging environment is shown. With the presented human- machine interface, including virtual reality displaying and interaction techniques, radiologists can be supported in their work. Further, virtual environments can even alleviate communication between specialists from different fields or in educational and training applications.

Air kerma standard for calibration of well-type chambers in Brazil using {sup 192}Ir HDR sources and its traceability

Energy Technology Data Exchange (ETDEWEB)

Di Prinzio, Renato; Almeida, Carlos Eduardo de [Laboratorio de Ciencias Radiologicas-Universidade do Estado do Rio de Janeiro (LCR/UERJ), R. Sao Francisco Xavier, 524, Pavilhao Haroldo Lisboa da Cunha, Terreo, Sala 136-Maracana, CEP 20550-900-Rio de Janeiro/RJ-Rio de Janeiro, RJ (Brazil) and Instituto de Radioprotecao e Dosimetria-Comissao Nacional de Energia Nuclear (IRD/CNEN), Av. Salvador Allende, s/n, Jacarepagua-CE22780-160-Rio de Janeiro, RJ (Brazil); Laboratorio de Ciencias Radiologicas-Universidade do Estado do Rio de Janeiro (LCR/UERJ), R. Sao Francisco Xavier, 524, Pavilhao Haroldo Lisboa da Cunha, Terreo, Sala 136-Maracana, CEP 20550-900-Rio de Janeiro/RJ-Rio de Janeiro, RJ (Brazil)

2009-03-15

In Brazil there are over 100 high dose rate (HDR) brachytherapy facilities using well-type chambers for the determination of the air kerma rate of {sup 192}Ir sources. This paper presents the methodology developed and extensively tested by the Laboratorio de Ciencias Radiologicas (LCR) and presently in use to calibrate those types of chambers. The system was initially used to calibrate six well-type chambers of brachytherapy services, and the maximum deviation of only 1.0% was observed between the calibration coefficients obtained and the ones in the calibration certificate provided by the UWADCL. In addition to its traceability to the Brazilian National Standards, the whole system was taken to University of Wisconsin Accredited Dosimetry Calibration Laboratory (UWADCL) for a direct comparison and the same formalism to calculate the air kerma was used. The comparison results between the two laboratories show an agreement of 0.9% for the calibration coefficients. Three Brazilian well-type chambers were calibrated at the UWADCL, and by LCR, in Brazil, using the developed system and a clinical HDR machine. The results of the calibration of three well chambers have shown an agreement better than 1.0%. Uncertainty analyses involving the measurements made both at the UWADCL and LCR laboratories are discussed.

The Machine within the Machine

CERN Multimedia

Katarina Anthony

2014-01-01

Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

Quantitative performance evaluation of 124I PET/MRI lesion dosimetry in differentiated thyroid cancer

Science.gov (United States)

Wierts, R.; Jentzen, W.; Quick, H. H.; Wisselink, H. J.; Pooters, I. N. A.; Wildberger, J. E.; Herrmann, K.; Kemerink, G. J.; Backes, W. H.; Mottaghy, F. M.

2018-01-01

The aim was to investigate the quantitative performance of 124I PET/MRI for pre-therapy lesion dosimetry in differentiated thyroid cancer (DTC). Phantom measurements were performed on a PET/MRI system (Biograph mMR, Siemens Healthcare) using 124I and 18F. The PET calibration factor and the influence of radiofrequency coil attenuation were determined using a cylindrical phantom homogeneously filled with radioactivity. The calibration factor was 1.00  ±  0.02 for 18F and 0.88  ±  0.02 for 124I. Near the radiofrequency surface coil an underestimation of less than 5% in radioactivity concentration was observed. Soft-tissue sphere recovery coefficients were determined using the NEMA IEC body phantom. Recovery coefficients were systematically higher for 18F than for 124I. In addition, the six spheres of the phantom were segmented using a PET-based iterative segmentation algorithm. For all 124I measurements, the deviations in segmented lesion volume and mean radioactivity concentration relative to the actual values were smaller than 15% and 25%, respectively. The effect of MR-based attenuation correction (three- and four-segment µ-maps) on bone lesion quantification was assessed using radioactive spheres filled with a K2HPO4 solution mimicking bone lesions. The four-segment µ-map resulted in an underestimation of the imaged radioactivity concentration of up to 15%, whereas the three-segment µ-map resulted in an overestimation of up to 10%. For twenty lesions identified in six patients, a comparison of 124I PET/MRI to PET/CT was performed with respect to segmented lesion volume and radioactivity concentration. The interclass correlation coefficients showed excellent agreement in segmented lesion volume and radioactivity concentration (0.999 and 0.95, respectively). In conclusion, it is feasible that accurate quantitative 124I PET/MRI could be used to perform radioiodine pre-therapy lesion dosimetry in DTC.

A SVM-based quantitative fMRI method for resting-state functional network detection.

Science.gov (United States)

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Adaptation of a haptic robot in a 3T fMRI.

Science.gov (United States)

Snider, Joseph; Plank, Markus; May, Larry; Liu, Thomas T; Poizner, Howard

2011-10-04

Functional magnetic resonance imaging (fMRI) provides excellent functional brain imaging via the BOLD signal with advantages including non-ionizing radiation, millimeter spatial accuracy of anatomical and functional data, and nearly real-time analyses. Haptic robots provide precise measurement and control of position and force of a cursor in a reasonably confined space. Here we combine these two technologies to allow precision experiments involving motor control with haptic/tactile environment interaction such as reaching or grasping. The basic idea is to attach an 8 foot end effecter supported in the center to the robot allowing the subject to use the robot, but shielding it and keeping it out of the most extreme part of the magnetic field from the fMRI machine (Figure 1). The Phantom Premium 3.0, 6DoF, high-force robot (SensAble Technologies, Inc.) is an excellent choice for providing force-feedback in virtual reality experiments, but it is inherently non-MR safe, introduces significant noise to the sensitive fMRI equipment, and its electric motors may be affected by the fMRI's strongly varying magnetic field. We have constructed a table and shielding system that allows the robot to be safely introduced into the fMRI environment and limits both the degradation of the fMRI signal by the electrically noisy motors and the degradation of the electric motor performance by the strongly varying magnetic field of the fMRI. With the shield, the signal to noise ratio (SNR: mean signal/noise standard deviation) of the fMRI goes from a baseline of ~380 to ~330, and ~250 without the shielding. The remaining noise appears to be uncorrelated and does not add artifacts to the fMRI of a test sphere (Figure 2). The long, stiff handle allows placement of the robot out of range of the most strongly varying parts of the magnetic field so there is no significant effect of the fMRI on the robot. The effect of the handle on the robot's kinematics is minimal since it is lightweight (~2

INTRODUCING NOVEL GENERATION OF HIGH ACCURACY CAMERA OPTICAL-TESTING AND CALIBRATION TEST-STANDS FEASIBLE FOR SERIES PRODUCTION OF CAMERAS

Directory of Open Access Journals (Sweden)

M. Nekouei Shahraki

2015-12-01

Full Text Available The recent advances in the field of computer-vision have opened the doors of many opportunities for taking advantage of these techniques and technologies in many fields and applications. Having a high demand for these systems in today and future vehicles implies a high production volume of video cameras. The above criterions imply that it is critical to design test systems which deliver fast and accurate calibration and optical-testing capabilities. In this paper we introduce new generation of test-stands delivering high calibration quality in single-shot calibration of fisheye surround-view cameras. This incorporates important geometric features from bundle-block calibration, delivers very high (sub-pixel calibration accuracy, makes possible a very fast calibration procedure (few seconds, and realizes autonomous calibration via machines. We have used the geometrical shape of a Spherical Helix (Type: 3D Spherical Spiral with special geometrical characteristics, having a uniform radius which corresponds to the uniform motion. This geometrical feature was mechanically realized using three dimensional truncated icosahedrons which practically allow the implementation of a spherical helix on multiple surfaces. Furthermore the test-stand enables us to perform many other important optical tests such as stray-light testing, enabling us to evaluate the certain qualities of the camera optical module.

Exposure-rate calibration using large-area calibration pads

International Nuclear Information System (INIS)

Novak, E.F.

1988-09-01

The US Department of Energy (DOE) Office of Remedial Action and Waste Technology established the Technical Measurements Center (TMC) at the DOE Grand Junction Projects Office (GJPO) in Grand Junction, Colorado, to standardize, calibrate, and compare measurements made in support of DOE remedial action programs. A set of large-area, radioelement-enriched concrete pads was constructed by the DOE in 1978 at the Walker Field Airport in Grand Junction for use as calibration standards for airborne gamma-ray spectrometer systems. The use of these pads was investigated by the TMC as potential calibration standards for portable scintillometers employed in measuring gamma-ray exposure rates at Uranium Mill Tailings Remedial Action (UMTRA) project sites. Data acquired on the pads using a pressurized ionization chamber (PIC) and three scintillometers are presented as an illustration of an instrumental calibration. Conclusions and recommended calibration procedures are discussed, based on the results of these data

Real-time PCR Machine System Modeling and a Systematic Approach for the Robust Design of a Real-time PCR-on-a-Chip System

OpenAIRE

Lee, Da-Sheng

2010-01-01

Chip-based DNA quantification systems are widespread, and used in many point-of-care applications. However, instruments for such applications may not be maintained or calibrated regularly. Since machine reliability is a key issue for normal operation, this study presents a system model of the real-time Polymerase Chain Reaction (PCR) machine to analyze the instrument design through numerical experiments. Based on model analysis, a systematic approach was developed to lower the variation of DN...

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.

External validation of the MRI-DRAGON score: early prediction of stroke outcome after intravenous thrombolysis.

Science.gov (United States)

Turc, Guillaume; Aguettaz, Pierre; Ponchelle-Dequatre, Nelly; Hénon, Hilde; Naggara, Olivier; Leclerc, Xavier; Cordonnier, Charlotte; Leys, Didier; Mas, Jean-Louis; Oppenheim, Catherine

2014-01-01

The aim of our study was to validate in an independent cohort the MRI-DRAGON score, an adaptation of the (CT-) DRAGON score to predict 3-month outcome in acute ischemic stroke patients undergoing MRI before intravenous thrombolysis (IV-tPA). We reviewed consecutive (2009-2013) anterior circulation stroke patients treated within 4.5 hours by IV-tPA in the Lille stroke unit (France), where MRI is the first-line pretherapeutic work-up. We assessed the discrimination and calibration of the MRI-DRAGON score to predict poor 3-month outcome, defined as modified Rankin Score >2, using c-statistic and the Hosmer-Lemeshow test, respectively. We included 230 patients (mean ±SD age 70.4±16.0 years, median [IQR] baseline NIHSS 8 [5]-[14]; poor outcome in 78(34%) patients). The c-statistic was 0.81 (95%CI 0.75-0.87), and the Hosmer-Lemeshow test was not significant (p = 0.54). The MRI-DRAGON score showed good prognostic performance in the external validation cohort. It could therefore be used to inform the patient's relatives about long-term prognosis and help to identify poor responders to IV-tPA alone, who may be candidates for additional therapeutic strategies, if they are otherwise eligible for such procedures based on the institutional criteria.

External validation of the MRI-DRAGON score: early prediction of stroke outcome after intravenous thrombolysis.

Directory of Open Access Journals (Sweden)

Guillaume Turc

Full Text Available The aim of our study was to validate in an independent cohort the MRI-DRAGON score, an adaptation of the (CT- DRAGON score to predict 3-month outcome in acute ischemic stroke patients undergoing MRI before intravenous thrombolysis (IV-tPA.We reviewed consecutive (2009-2013 anterior circulation stroke patients treated within 4.5 hours by IV-tPA in the Lille stroke unit (France, where MRI is the first-line pretherapeutic work-up. We assessed the discrimination and calibration of the MRI-DRAGON score to predict poor 3-month outcome, defined as modified Rankin Score >2, using c-statistic and the Hosmer-Lemeshow test, respectively.We included 230 patients (mean ±SD age 70.4±16.0 years, median [IQR] baseline NIHSS 8 [5]-[14]; poor outcome in 78(34% patients. The c-statistic was 0.81 (95%CI 0.75-0.87, and the Hosmer-Lemeshow test was not significant (p = 0.54.The MRI-DRAGON score showed good prognostic performance in the external validation cohort. It could therefore be used to inform the patient's relatives about long-term prognosis and help to identify poor responders to IV-tPA alone, who may be candidates for additional therapeutic strategies, if they are otherwise eligible for such procedures based on the institutional criteria.

Effect of Machining Velocity in Nanoscale Machining Operations

International Nuclear Information System (INIS)

Islam, Sumaiya; Khondoker, Noman; Ibrahim, Raafat

2015-01-01

The aim of this study is to investigate the generated forces and deformations of single crystal Cu with (100), (110) and (111) crystallographic orientations at nanoscale machining operation. A nanoindenter equipped with nanoscratching attachment was used for machining operations and in-situ observation of a nano scale groove. As a machining parameter, the machining velocity was varied to measure the normal and cutting forces. At a fixed machining velocity, different levels of normal and cutting forces were generated due to different crystallographic orientations of the specimens. Moreover, after machining operation percentage of elastic recovery was measured and it was found that both the elastic and plastic deformations were responsible for producing a nano scale groove within the range of machining velocities from 250-1000 nm/s. (paper)

A Technique for Generating Volumetric Cine MRI (VC-MRI)

Science.gov (United States)

Harris, Wendy; Ren, Lei; Cai, Jing; Zhang, You; Chang, Zheng; Yin, Fang-Fang

2016-01-01

Purpose To develop a technique to generate on-board volumetric-cine MRI (VC-MRI) using patient prior images, motion modeling and on-board 2D-cine MRI. Methods One phase of a 4D-MRI acquired during patient simulation is used as patient prior images. 3 major respiratory deformation patterns of the patient are extracted from 4D-MRI based on principal-component-analysis. The on-board VC-MRI at any instant is considered as a deformation of the prior MRI. The deformation field is represented as a linear combination of the 3 major deformation patterns. The coefficients of the deformation patterns are solved by the data fidelity constraint using the acquired on-board single 2D-cine MRI. The method was evaluated using both XCAT simulation of lung cancer patients and MRI data from four real liver cancer patients. The accuracy of the estimated VC-MRI was quantitatively evaluated using Volume-Percent-Difference(VPD), Center-of-Mass-Shift(COMS), and target tracking errors. Effects of acquisition orientation, region-of-interest(ROI) selection, patient breathing pattern change and noise on the estimation accuracy were also evaluated. Results Image subtraction of ground-truth with estimated on-board VC-MRI shows fewer differences than image subtraction of ground-truth with prior image. Agreement between profiles in the estimated and ground-truth VC-MRI was achieved with less than 6% error for both XCAT and patient data. Among all XCAT scenarios, the VPD between ground-truth and estimated lesion volumes was on average 8.43±1.52% and the COMS was on average 0.93±0.58mm across all time-steps for estimation based on the ROI region in the sagittal cine images. Matching to ROI in the sagittal view achieved better accuracy when there was substantial breathing pattern change. The technique was robust against noise levels up to SNR=20. For patient data, average tracking errors were less than 2 mm in all directions for all patients. Conclusions Preliminary studies demonstrated the

MRI Primer

International Nuclear Information System (INIS)

Oldendorf, W.; Oldendorf, W. Jr.

1991-01-01

Designed for studies, radiologists, and clinicians at all levels of training, this book provides a basic introduction to the principles, physics, and instrumentation of magnetic resonance imaging. The fundamental concepts that are essential for the optimal clinical use of MRI are thoroughly explained in easily accessible terms. To facilitate the reader's comprehension, the material is presented nonmathematically, using no equations and a minimum of symbols and abbreviations. MRI Primer presents a clear account of the phenomenon of nuclear magnetic resonance and the use of gradient magnetic fields to create clinically useful images of cross-sectional slices. Close attention is given to the magnetization vector as a means of expressing nuclear behavior, the role of T 1 and T 2 weighing in imaging, the use of contrast agents, and the pulse sequences most often used in clinical practice, as well as to the relative capabilities and limitations of MRI and CT. The basic hardware components of an MRI scanner are described in detail. Sample MRI scans illustrate how MRI characterizes tissue. An appendix provides a brief introduction to quantum processes in MRI

MD#1182: Calibration of diamond particle detectors in IP6

CERN Document Server

Valette, Matthieu; Lindstrom, Bjorn Hans Filip; Wiesner, Christoph

2017-01-01

In case of an asynchronous beam dump with a fully filled LHC machine it is expected that all standard ionisation chamber Beam Loss Monitors (IC BLM) around the LHC dumping region in IP6 will be saturated. Diamond Beam Loss Monitors (dBLM) were therefore installed next to the movable dump protection absorber (TCDQ) downstream of the extraction kickers. These detectors allow resolving losses at a nanosecond timescale and with an dynamic range of several orders of magnitude; thus, allowing to know the number of nominal bunches impacting the TCDQ. After a first series of calibrations using asynchronous beam dump tests, an experiment was conducted during MD#1182 to demonstrate the possibility of resolving a nominal bunch hitting the TCDQ. The impact parameter of the bunches on the TCDQ was first scanned using probe bunches with lower intensity then tests were done with nominal bunches (1.1e11 p/bunch) at injection energy. High energy calibration of the losses was also attempted unsuccessfully. Due to different beh...

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

Intercomparison and calibration of dose calibrators used in nuclear medicine facilities

CERN Document Server

Costa, A M D

2003-01-01

The aim of this work was to establish a working standard for intercomparison and calibration of dose calibrators used in most of nuclear medicine facilities for the determination of the activity of radionuclides administered to patients in specific examinations or therapeutic procedures. A commercial dose calibrator, a set of standard radioactive sources, and syringes, vials and ampoules with radionuclide solutions used in nuclear medicine were utilized in this work. The commercial dose calibrator was calibrated for radionuclide solutions used in nuclear medicine. Simple instrument tests, such as linearity response and variation response with the source volume at a constant source activity concentration were performed. This instrument may be used as a reference system for intercomparison and calibration of other activity meters, as a method of quality control of dose calibrators utilized in nuclear medicine facilities.

Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities

Energy Technology Data Exchange (ETDEWEB)

Mehranian, Abolfazl; Arabi, Hossein [Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva CH-1211 (Switzerland); Zaidi, Habib, E-mail: habib.zaidi@hcuge.ch [Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva CH-1211 (Switzerland); Geneva Neuroscience Centre, University of Geneva, Geneva CH-1205 (Switzerland); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen 9700 RB (Netherlands)

2016-03-15

Attenuation correction is an essential component of the long chain of data correction techniques required to achieve the full potential of quantitative positron emission tomography (PET) imaging. The development of combined PET/magnetic resonance imaging (MRI) systems mandated the widespread interest in developing novel strategies for deriving accurate attenuation maps with the aim to improve the quantitative accuracy of these emerging hybrid imaging systems. The attenuation map in PET/MRI should ideally be derived from anatomical MR images; however, MRI intensities reflect proton density and relaxation time properties of biological tissues rather than their electron density and photon attenuation properties. Therefore, in contrast to PET/computed tomography, there is a lack of standardized global mapping between the intensities of MRI signal and linear attenuation coefficients at 511 keV. Moreover, in standard MRI sequences, bones and lung tissues do not produce measurable signals owing to their low proton density and short transverse relaxation times. MR images are also inevitably subject to artifacts that degrade their quality, thus compromising their applicability for the task of attenuation correction in PET/MRI. MRI-guided attenuation correction strategies can be classified in three broad categories: (i) segmentation-based approaches, (ii) atlas-registration and machine learning methods, and (iii) emission/transmission-based approaches. This paper summarizes past and current state-of-the-art developments and latest advances in PET/MRI attenuation correction. The advantages and drawbacks of each approach for addressing the challenges of MR-based attenuation correction are comprehensively described. The opportunities brought by both MRI and PET imaging modalities for deriving accurate attenuation maps and improving PET quantification will be elaborated. Future prospects and potential clinical applications of these techniques and their integration in commercial

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

Science.gov (United States)

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Environmentally Friendly Machining

CERN Document Server

Dixit, U S; Davim, J Paulo

2012-01-01

Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

MRI findings of temporal lobe epilepsy

International Nuclear Information System (INIS)

Nakahara, Ichiro; Yin, Dali; Fukami, Masahiro; Kondo, Seiji; Takeuchi, Juji; Kanemoto, Kousuke; Sengoku, Akira; Kawai, Itsuo

1992-01-01

MRI findings were analyzed retrospectively in 46 patients with temporal lobe epilepsy in which the side of epileptogenic focus had been confirmed by EEG studies. T 1 - and T 2 -weighted images were obtained by the use of a 1.0 or 1.5 T superconducting-type MRI machine with a coronal scan perpendicular to the axis of the temporal horn of the lateral ventricle. Additional axial and sagittal scans were performed in some cases. The area of the hippocampal body was measured quantitatively using a computerized image-analysis system in 26 cases in which the hippocampus had been visualized with enough contrast on T 1 -weighted coronal images. Abnormal findings were observed in 31/46 (67%) cases. Hippocampal (HC) and temporal lobe (TL) atrophy were observed in 18/46 (39%) and 23/46 (50%) cases respectively, and the side of the atrophy corresponded with the side of the epileptogenic focus, as confirmed by EEG studies, with specificities of 89% and 74% respectively. A quantitative measurement of the area of the hippocampal body showed unilateral hippocampal atrophy more than 10% in 18/25 (69%) cases (10-25%: 10 cases, 25-50%: 7 cases, 50% 2 abnormality was observed in only 4 cases. Structural lesions were observed in 4 cases including an arachnoid cyst, an astrocytoma in amygdala, the Dandy-Walker syndrome, and tuberous sclerosis, using the more efficient imaging qualities than the CT scan. From these observations, it is apparant that superconducting MRI is extremely useful in the diagnosis of the epileptogenic topography of temporal lobe epilepsy. Particularly, hippocampal atrophy was found to correspond with the side of the epileptogenic focus on EEG with a high specificity; its quantitative evaluation could be one of the most important standards in detecting the operative indications for temporal lobe epilepsy. (author)

Breast cancer Ki67 expression preoperative discrimination by DCE-MRI radiomics features

Science.gov (United States)

Ma, Wenjuan; Ji, Yu; Qin, Zhuanping; Guo, Xinpeng; Jian, Xiqi; Liu, Peifang

2018-02-01

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

The radiation metrology network related to the field of mammography: implementation and uncertainty analysis of the calibration system

Science.gov (United States)

Peixoto, J. G. P.; de Almeida, C. E.

2001-09-01

It is recognized by the international guidelines that it is necessary to offer calibration services for mammography beams in order to improve the quality of clinical diagnosis. Major efforts have been made by several laboratories in order to establish an appropriate and traceable calibration infrastructure and to provide the basis for a quality control programme in mammography. The contribution of the radiation metrology network to the users of mammography is reviewed in this work. Also steps required for the implementation of a mammography calibration system using a constant potential x-ray and a clinical mammography x-ray machine are presented. The various qualities of mammography radiation discussed in this work are in accordance with the IEC 61674 and the AAPM recommendations. They are at present available at several primary standard dosimetry laboratories (PSDLs), namely the PTB, NIST and BEV and a few secondary standard dosimetry laboratories (SSDLs) such as at the University of Wisconsin and at the IAEA's SSDL. We discuss the uncertainties involved in all steps of the calibration chain in accord with the ISO recommendations.

Enhanced Quality Control in Pharmaceutical Applications by Combining Raman Spectroscopy and Machine Learning Techniques

Science.gov (United States)

Martinez, J. C.; Guzmán-Sepúlveda, J. R.; Bolañoz Evia, G. R.; Córdova, T.; Guzmán-Cabrera, R.

2018-06-01

In this work, we applied machine learning techniques to Raman spectra for the characterization and classification of manufactured pharmaceutical products. Our measurements were taken with commercial equipment, for accurate assessment of variations with respect to one calibrated control sample. Unlike the typical use of Raman spectroscopy in pharmaceutical applications, in our approach the principal components of the Raman spectrum are used concurrently as attributes in machine learning algorithms. This permits an efficient comparison and classification of the spectra measured from the samples under study. This also allows for accurate quality control as all relevant spectral components are considered simultaneously. We demonstrate our approach with respect to the specific case of acetaminophen, which is one of the most widely used analgesics in the market. In the experiments, commercial samples from thirteen different laboratories were analyzed and compared against a control sample. The raw data were analyzed based on an arithmetic difference between the nominal active substance and the measured values in each commercial sample. The principal component analysis was applied to the data for quantitative verification (i.e., without considering the actual concentration of the active substance) of the difference in the calibrated sample. Our results show that by following this approach adulterations in pharmaceutical compositions can be clearly identified and accurately quantified.

Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson's Natural Language Processing Algorithm.

Science.gov (United States)

Trivedi, Hari; Mesterhazy, Joseph; Laguna, Benjamin; Vu, Thienkhai; Sohn, Jae Ho

2018-04-01

Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise or preferences of the protocoling radiologist. Providing a best-practice recommendation for an MRI protocol has the potential to improve efficiency and decrease the likelihood of a suboptimal or erroneous study. The goal of this study was to develop and validate a machine learning-based natural language classifier that can automatically assign the use of intravenous contrast for musculoskeletal MRI protocols based upon the free-text clinical indication of the study, thereby improving efficiency of the protocoling radiologist and potentially decreasing errors. We utilized a deep learning-based natural language classification system from IBM Watson, a question-answering supercomputer that gained fame after challenging the best human players on Jeopardy! in 2011. We compared this solution to a series of traditional machine learning-based natural language processing techniques that utilize a term-document frequency matrix. Each classifier was trained with 1240 MRI protocols plus their respective clinical indications and validated with a test set of 280. Ground truth of contrast assignment was obtained from the clinical record. For evaluation of inter-reader agreement, a blinded second reader radiologist analyzed all cases and determined contrast assignment based on only the free-text clinical indication. In the test set, Watson demonstrated overall accuracy of 83.2% when compared to the original protocol. This was similar to the overall accuracy of 80.2% achieved by an ensemble of eight traditional machine learning algorithms based on a term-document matrix. When compared to the second reader's contrast assignment, Watson achieved 88.6% agreement. When evaluating only the subset of cases where the original protocol and second reader were concordant (n = 251), agreement climbed further to 90.0%. The classifier was

Spectrometric methods used in the calibration of radiodiagnostic measuring instruments

Energy Technology Data Exchange (ETDEWEB)

De Vries, W [Rijksuniversiteit Utrecht (Netherlands)

1995-12-01

Recently a set of parameters for checking the quality of radiation for use in diagnostic radiology was established at the calibration facility of Nederlands Meetinstituut (NMI). The establishment of the radiation quality required re-evaluation of the correction factors for the primary air-kerma standards. Free-air ionisation chambers require several correction factors to measure air-kerma according to its definition. These correction factors were calculated for the NMi free-air chamber by Monte Carlo simulations for monoenergetic photons in the energy range from 10 keV to 320 keV. The actual correction factors follow from weighting these mono-energetic correction factors with the air-kerma spectrum of the photon beam. This paper describes the determination of the photon spectra of the X-ray qualities used for the calibration of dosimetric instruments used in radiodiagnostics. The detector used for these measurements is a planar HPGe-detector, placed in the direct beam of the X-ray machine. To convert the measured pulse height spectrum to the actual photon spectrum corrections must be made for fluorescent photon escape, single and multiple compton scattering inside the detector, and detector efficiency. From the calculated photon spectra a number of parameters of the X-ray beam can be calculated. The calculated first and second half value layer in aluminum and copper are compared with the measured values of these parameters to validate the method of spectrum reconstruction. Moreover the spectrum measurements offer the possibility to calibrate the X-ray generator in terms of maximum high voltage. The maximum photon energy in the spectrum is used as a standard for calibration of kVp-meters.

Validation of a densimeter calibration procedure for a secondary calibration laboratory

International Nuclear Information System (INIS)

Alpizar Herrera, Juan Carlos

2014-01-01

A survey was conducted to quantify the need for calibration of a density measurement instrument at the research units at the Sede Rodrigo Facio of the Universidad de Costa Rica. A calibration procedure was documented for the instrument that presented the highest demand in the survey by the calibration service. A study of INTE-ISO/IEC 17025: 2005 and specifically in section 5.4 of this standard was done, to document the procedure for calibrating densimeters. Densimeter calibration procedures and standards were sought from different national and international sources. The method of hydrostatic weighing or Cuckow method was the basis of the defined procedure. Documenting the calibration procedure and creating other documents was performed for data acquisition log, intermediate calculation log and calibration certificate copy. A veracity test was performed using as reference laboratory a laboratory of calibration secondary national as part of the validation process of the documented procedure. The results of the E_n statistic of 0.41; 0.34 and 0.46 for the calibration points 90%, 50% and 10% were obtained for the densimeter scale respectively. A reproducibility analysis of the method was performed with satisfactory results. Different suppliers were contacted to estimate the economic costs of the equipment and materials, needed to develop the documented method of densimeter calibration. The acquisition of an analytical balance was recommended, instead of a precision scale, in order to improve the results obtained with the documented method [es

SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

Energy Technology Data Exchange (ETDEWEB)

Ogden, K; O’Dwyer, R [SUNY Upstate Medical University, Syracuse, NY (United States); Bradford, T [Syracuse University, Syracuse, NY (United States); Cussen, L [Rochester Institute of Technology, Rochester, NY (United States)

2016-06-15

Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

International Nuclear Information System (INIS)

Ogden, K; O’Dwyer, R; Bradford, T; Cussen, L

2016-01-01

Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

MRI findings of central nervous system granulocytic sarcoma (chloroma)

International Nuclear Information System (INIS)

Lee, Chang Man; Kim, Myung Soon; Kim, Ik Soo; Cho, Kwan Soo

1997-01-01

To characterize MRI findings of central nervous system (CNS) granulocytic sarcoma (chloroma) and to analyse the points which differentiate it from other CNS tumors. We evaluated MRI in six patients with CNS granulocytic sarcoma proven by surgery or bone marrow biopsy (intracranical, one case and spine five cases). A 0.5T superconductive MR machine was used for diagnosis and, axial, coronal and sagittal T1- and T2-weighted spin echo images and Gd-DTPA enhanced T1-weighted images were obtained. We retrospectively analized the location, signal intensity, margin, contrast enhancement and homogeneity, and bony change around the tumor. MRI findings of CNS granulocytic sarcomas were as follows : one tumor was seen to be an extra-axial mass in the posterior fossa of the brain, four were epidural, and one was an epidural and presacral masses in the spine;tumor magins were lobulated and three were smooth. On T1-weighted images, all tumors were of isoignal intensity;on T2-weighted images, four were of isosignal intersity and two were of high signal intensity. Contrast enhancement was inhomogeneous in five of six cases. Bony change around the tumor was seen in two cases. On T1-weighted images, CNS granulocytic sarcomas (chloromas) were of isosignal intensity, relative to brain parenchyma or spinal cord;on T2-weighted images, they were of iso or high signal intensity, with relative contrast enhancement. These points could be useful in differentiating them from other CNS tumors

TH-B-BRC-03: Measurement and Calibration Discrepancy: Advanced Warning Guidance

Energy Technology Data Exchange (ETDEWEB)

Followill, D. [UT MD Anderson Cancer Center (United States)

2016-06-15

Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews of some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803.

TH-B-BRC-03: Measurement and Calibration Discrepancy: Advanced Warning Guidance

International Nuclear Information System (INIS)

Followill, D.

2016-01-01

Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews of some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803

Discriminating between brain rest and attention states using fMRI connectivity graphs and subtree SVM

Science.gov (United States)

Mokhtari, Fatemeh; Bakhtiari, Shahab K.; Hossein-Zadeh, Gholam Ali; Soltanian-Zadeh, Hamid

2012-02-01

Decoding techniques have opened new windows to explore the brain function and information encoding in brain activity. In the current study, we design a recursive support vector machine which is enriched by a subtree graph kernel. We apply the classifier to discriminate between attentional cueing task and resting state from a block design fMRI dataset. The classifier is trained using weighted fMRI graphs constructed from activated regions during the two mentioned states. The proposed method leads to classification accuracy of 1. It is also able to elicit discriminative regions and connectivities between the two states using a backward edge elimination algorithm. This algorithm shows the importance of regions including cerebellum, insula, left middle superior frontal gyrus, post cingulate cortex, and connectivities between them to enhance the correct classification rate.

gr-MRI: A software package for magnetic resonance imaging using software defined radios

Science.gov (United States)

Hasselwander, Christopher J.; Cao, Zhipeng; Grissom, William A.

2016-09-01

The goal of this work is to develop software that enables the rapid implementation of custom MRI spectrometers using commercially-available software defined radios (SDRs). The developed gr-MRI software package comprises a set of Python scripts, flowgraphs, and signal generation and recording blocks for GNU Radio, an open-source SDR software package that is widely used in communications research. gr-MRI implements basic event sequencing functionality, and tools for system calibrations, multi-radio synchronization, and MR signal processing and image reconstruction. It includes four pulse sequences: a single-pulse sequence to record free induction signals, a gradient-recalled echo imaging sequence, a spin echo imaging sequence, and an inversion recovery spin echo imaging sequence. The sequences were used to perform phantom imaging scans with a 0.5 Tesla tabletop MRI scanner and two commercially-available SDRs. One SDR was used for RF excitation and reception, and the other for gradient pulse generation. The total SDR hardware cost was approximately 2000. The frequency of radio desynchronization events and the frequency with which the software recovered from those events was also measured, and the SDR's ability to generate frequency-swept RF waveforms was validated and compared to the scanner's commercial spectrometer. The spin echo images geometrically matched those acquired using the commercial spectrometer, with no unexpected distortions. Desynchronization events were more likely to occur at the very beginning of an imaging scan, but were nearly eliminated if the user invoked the sequence for a short period before beginning data recording. The SDR produced a 500 kHz bandwidth frequency-swept pulse with high fidelity, while the commercial spectrometer produced a waveform with large frequency spike errors. In conclusion, the developed gr-MRI software can be used to develop high-fidelity, low-cost custom MRI spectrometers using commercially-available SDRs.

Hybrid machining processes perspectives on machining and finishing

CERN Document Server

Gupta, Kapil; Laubscher, R F

2016-01-01

This book describes various hybrid machining and finishing processes. It gives a critical review of the past work based on them as well as the current trends and research directions. For each hybrid machining process presented, the authors list the method of material removal, machining system, process variables and applications. This book provides a deep understanding of the need, application and mechanism of hybrid machining processes.

Precise on-machine extraction of the surface normal vector using an eddy current sensor array

International Nuclear Information System (INIS)

Wang, Yongqing; Lian, Meng; Liu, Haibo; Ying, Yangwei; Sheng, Xianjun

2016-01-01

To satisfy the requirements of on-machine measurement of the surface normal during complex surface manufacturing, a highly robust normal vector extraction method using an Eddy current (EC) displacement sensor array is developed, the output of which is almost unaffected by surface brightness, machining coolant and environmental noise. A precise normal vector extraction model based on a triangular-distributed EC sensor array is first established. Calibration of the effects of object surface inclination and coupling interference on measurement results, and the relative position of EC sensors, is involved. A novel apparatus employing three EC sensors and a force transducer was designed, which can be easily integrated into the computer numerical control (CNC) machine tool spindle and/or robot terminal execution. Finally, to test the validity and practicability of the proposed method, typical experiments were conducted with specified testing pieces using the developed approach and system, such as an inclined plane and cylindrical and spherical surfaces. (paper)

Precise on-machine extraction of the surface normal vector using an eddy current sensor array

Science.gov (United States)

Wang, Yongqing; Lian, Meng; Liu, Haibo; Ying, Yangwei; Sheng, Xianjun

2016-11-01

To satisfy the requirements of on-machine measurement of the surface normal during complex surface manufacturing, a highly robust normal vector extraction method using an Eddy current (EC) displacement sensor array is developed, the output of which is almost unaffected by surface brightness, machining coolant and environmental noise. A precise normal vector extraction model based on a triangular-distributed EC sensor array is first established. Calibration of the effects of object surface inclination and coupling interference on measurement results, and the relative position of EC sensors, is involved. A novel apparatus employing three EC sensors and a force transducer was designed, which can be easily integrated into the computer numerical control (CNC) machine tool spindle and/or robot terminal execution. Finally, to test the validity and practicability of the proposed method, typical experiments were conducted with specified testing pieces using the developed approach and system, such as an inclined plane and cylindrical and spherical surfaces.

MRI assessment program. Consensus statement on clinical efficacy of MRI

International Nuclear Information System (INIS)

1998-05-01

This consensus statement is largely based on the experience gained at the MRI units at the four hospitals which have operated scanners in the MRI program. It reflects the considered opinion of the radiologists responsible for the MRI services at those hospitals. Account has also been taken of relevant overseas data. This collection of opinion relates particularly to comparison with other imaging modalities. The specific comments will require further consideration as technical developments with MRI become available, additional experience is gained with gadolinium contrast material and additional data are obtained on the influence of MRI on patient management. MRI, at present, is used either to improve diagnostic accuracy when other tests are negative or equivocal, when there is strong clinical suspicion of disease, or to improve surgical or other management planning when the diagnosis known. In some situations (eg syringomyelia, congenital spinal disease, posterior fossa/cerebello-pontine angle tumours) it may entirely replace other tests (eg myelography, air contrast, CT) which are substantially less accurate and/or more invasive. In other situations (eg hemispheric brain tumours, lumbar disc protrusions) when other tests, such as CT, can be as accurate, MRI is not usually or initially indicated because it is currently more expensive and of limited availability. However, balanced against this is the fact that it does not expose the patient to potentially harmful ionising radiation. It is also stressed that MRI images depend on complex, widely variable and, as yet, incompletely understood parameters. There is concern that this may result in false positive diagnoses, especially where MRI is used alone as a screening test, or used as the initial test. For several reasons (availability, cost, medical and diagnostic efficacy), the specific comments on indications for MRI presented are based upon the assumption that MRI is a tertiary and complementary imaging examination

MRI

DEFF Research Database (Denmark)

Schroeter, Aileen; Rudin, Markus; Gianolio, Eliana

2017-01-01

This chapter discusses principles of nuclear magnetic resonance (NMR) and MRI followed by a survey on the major classes of MRI contrast agents (CA), their modes of action, and some of the most significative applications. The two more established classes of MRI-CA are represented by paramagnetic...... been attained that markedly increase the number and typology of systems with CEST properties. Currently much attention is also devoted to hyperpolarized molecules that display a sensitivity enhancement sufficient for their direct exploitation for the formation of the MR image. A real breakthrough...

Wind Tunnel Strain-Gage Balance Calibration Data Analysis Using a Weighted Least Squares Approach

Science.gov (United States)

Ulbrich, N.; Volden, T.

2017-01-01

A new approach is presented that uses a weighted least squares fit to analyze wind tunnel strain-gage balance calibration data. The weighted least squares fit is specifically designed to increase the influence of single-component loadings during the regression analysis. The weighted least squares fit also reduces the impact of calibration load schedule asymmetries on the predicted primary sensitivities of the balance gages. A weighting factor between zero and one is assigned to each calibration data point that depends on a simple count of its intentionally loaded load components or gages. The greater the number of a data point's intentionally loaded load components or gages is, the smaller its weighting factor becomes. The proposed approach is applicable to both the Iterative and Non-Iterative Methods that are used for the analysis of strain-gage balance calibration data in the aerospace testing community. The Iterative Method uses a reasonable estimate of the tare corrected load set as input for the determination of the weighting factors. The Non-Iterative Method, on the other hand, uses gage output differences relative to the natural zeros as input for the determination of the weighting factors. Machine calibration data of a six-component force balance is used to illustrate benefits of the proposed weighted least squares fit. In addition, a detailed derivation of the PRESS residuals associated with a weighted least squares fit is given in the appendices of the paper as this information could not be found in the literature. These PRESS residuals may be needed to evaluate the predictive capabilities of the final regression models that result from a weighted least squares fit of the balance calibration data.

Radiomics for ultrafast dynamic contrast-enhanced breast MRI in the diagnosis of breast cancer: a pilot study

Science.gov (United States)

Drukker, Karen; Anderson, Rachel; Edwards, Alexandra; Papaioannou, John; Pineda, Fred; Abe, Hiroyuke; Karzcmar, Gregory; Giger, Maryellen L.

2018-02-01

Radiomics for dynamic contrast-enhanced (DCE) breast MRI have shown promise in the diagnosis of breast cancer as applied to conventional DCE-MRI protocols. Here, we investigate the potential of using such radiomic features in the diagnosis of breast cancer applied on ultrafast breast MRI in which images are acquired every few seconds. The dataset consisted of 64 lesions (33 malignant and 31 benign) imaged with both `conventional' and ultrafast DCE-MRI. After automated lesion segmentation in each image sequence, we calculated 38 radiomic features categorized as describing size, shape, margin, enhancement-texture, kinetics, and enhancement variance kinetics. For each feature, we calculated the 95% confidence interval of the area under the ROC curve (AUC) to determine whether the performance of each feature in the task of distinguishing between malignant and benign lesions was better than random guessing. Subsequently, we assessed performance of radiomic signatures in 10-fold cross-validation repeated 10 times using a support vector machine with as input all the features as well as features by category. We found that many of the features remained useful (AUC>0.5) for the ultrafast protocol, with the exception of some features, e.g., those designed for latephase kinetics such as the washout rate. For ultrafast MRI, the radiomics enhancement-texture signature achieved the best performance, which was comparable to that of the kinetics signature for `conventional' DCE-MRI, both achieving AUC values of 0.71. Radiomic developed for `conventional' DCE-MRI shows promise for translation to the ultrafast protocol, where enhancement texture appears to play a dominant role.

Fuel-handling machine tests at the Institute for Nuclear Research - Pitesti. Computer and software research and engineering

International Nuclear Information System (INIS)

Doca, Cezar; Predescu, Darie; Maiorescu, Oliviu; Dobrescu, Sorin

2003-01-01

This poster introduces the fuel-handling machine SCC-MID. This work is part of a very ambitious project that was accomplished with remarkable investment efforts. Material and human resources was spent to build a test stand for fuel handling machines (CANDU system), closely linked to NPP Cernavoda. A challenging goal was also to develop a computer system (hw/sw) designed and engineered to control the test and calibration process of this fuel-handling machine. The design takes care both of the functionality required to correctly control the F/H machine and of the additional functionality required to assist the testing process. How to test the system itself to validate the implemented solutions, how to safely and consistently maintain the data involved, how to manage such a system, how to gradually integrate the system in the whole stand saving time and work already done and solutions already validated were questions we had to find out right solutions. We choose modular solutions both for hardware and software, based on late technologies which in addition permit to achieve the versatility we needed, namely: VME based hw systems running OS9/68k (Unix like real-time multi-user multitasking OS), ISaGRAF (process control application oriented development and run-time software), Hawk (cross-compiler and IDE software for C/C++ software development intended to run on other Motorola based hw), Suretrack (project management software). The system topology implements open system network concepts that permit communication between different sw/hw platforms (OS9/Motorola and ix86/ms-windows based systems) We spent major resources to model the technological processes and test tools like: - real time simulation of the machine behavior while responding to the human commands or to the state changes of other machine parts as a result of other commands or as mechanical interlinks or technological interlocks and presentation of results revealing time related movements; - database

Comparative studies of brain activation with MEG and functional MRI

International Nuclear Information System (INIS)

George, J.S.; Aine, C.J.; Sanders, J.A.; Lewine, J.D.; Caprihan, A.

1993-01-01

The past two years have witnessed the emergence of MRI as a functional imaging methodology. Initial demonstrations involved the injection of a paramagnetic contrast agent and required ultrafast echo planar imaging capability to adequately resolve the passage of the injected bolus. By measuring the local reduction in image intensity due to magnetic susceptibility, it was possible to calculate blood volume, which changes as a function of neural activation. Later developments have exploited endogenous contrast mechanisms to monitor changes in blood volume or in venous blood oxygen content. Recently, we and others have demonstrated that it is possible to make such measurements in a clinical imager, suggesting that the large installed base of such machines might be utilized for functional imaging. Although it is likely that functional MRI (fMRI) will subsume some of the clinical and basic neuroscience applications now touted for MEG, it is also clear that these techniques offer different largely complementary, capabilities. At the very least, it is useful to compare and cross-validate the activation maps produced by these techniques. Such studies will be valuable as a check on results of neuromagnetic distributed current reconstructions and will allow better characterization of the relationship between neurophysiological activation and associated hemodynamic changes. A more exciting prospect is the development of analyses that combine information from the two modalities to produce a better description of underlying neural activity than is possible with either technique in isolation. In this paper we describe some results from initial comparative studies and outline several techniques that can be used to treat MEG and fMRI data within a unified computational framework

An MRI-Conditional External Cardiac Defibrillator for Resuscitation Within the MRI Scanner Bore

Science.gov (United States)

Schmidt, Ehud J.; Watkins, Ronald D.; Zviman, Menekhem M.; Guttman, Michael A.; Wang, Wei; Halperin, Henry A.

2016-01-01

Background Subjects undergoing cardiac arrest within an MRI scanner are currently removed from the bore and then from the MRI suite, prior to delivery of CPR and defibrillation, potentially increasing risk of mortality. This precludes many higher-risk (acute-ischemic, acute-stroke) patients from undergoing MRI imaging and MRI-guided intervention. An MRI-conditional cardiac defibrillator should enable scanning with defibrillation pads attached and the generator ON, enabling application of defibrillation within the MRI seconds after a cardiac event. An MRI-conditional external defibrillator may improve patient acceptance for MRI procedures. Methods and Results A commercial external defibrillator was rendered 1.5 Tesla MRI-conditional by addition of novel Radio-Frequency (RF) filters between the generator and commercial disposable surface-pads. The RF filters reduced emission into the MRI scanner, and prevented cable/surface-pad heating during imaging, while preserving all the defibrillator’s monitoring and delivery functions. Human volunteers were imaged using high Specific-Absorption-Rate sequences to validate MRI image quality (IQ) and lack of heating. Swine were electrically fibrillated (N=4) and thereafter defibrillated both outside and inside the MRI bore. MRI IQ was reduced by 0.8 or 1.6 dB, with the generator in monitoring mode and operating on battery or AC power, respectively. Commercial surface-pads did not create artifacts deeper than 6mm below the skin surface. RF heating was within FDA guidelines. Defibrillation was completely successful inside and outside the MRI bore. Conclusions A prototype MRI-conditional defibrillation system successfully defibrillated in the MRI without degrading image quality, or increasing the time needed for defibrillation. It can increase patient acceptance for MRI procedures. PMID:27729363

Auto calibration of a cone-beam-CT

International Nuclear Information System (INIS)

Gross, Daniel; Heil, Ulrich; Schulze, Ralf; Schoemer, Elmar; Schwanecke, Ulrich

2012-01-01

Purpose: This paper introduces a novel autocalibration method for cone-beam-CTs (CBCT) or flat-panel CTs, assuming a perfect rotation. The method is based on ellipse-fitting. Autocalibration refers to accurate recovery of the geometric alignment of a CBCT device from projection images alone, without any manual measurements. Methods: The authors use test objects containing small arbitrarily positioned radio-opaque markers. No information regarding the relative positions of the markers is used. In practice, the authors use three to eight metal ball bearings (diameter of 1 mm), e.g., positioned roughly in a vertical line such that their projection image curves on the detector preferably form large ellipses over the circular orbit. From this ellipse-to-curve mapping and also from its inversion the authors derive an explicit formula. Nonlinear optimization based on this mapping enables them to determine the six relevant parameters of the system up to the device rotation angle, which is sufficient to define the geometry of a CBCT-machine assuming a perfect rotational movement. These parameters also include out-of-plane rotations. The authors evaluate their method by simulation based on data used in two similar approaches [L. Smekal, M. Kachelriess, S. E, and K. Wa, “Geometric misalignment and calibration in cone-beam tomography,” Med. Phys. 31(12), 3242–3266 (2004); K. Yang, A. L. C. Kwan, D. F. Miller, and J. M. Boone, “A geometric calibration method for cone beam CT systems,” Med. Phys. 33(6), 1695–1706 (2006)]. This allows a direct comparison of accuracy. Furthermore, the authors present real-world 3D reconstructions of a dry human spine segment and an electronic device. The reconstructions were computed from projections taken with a commercial dental CBCT device having two different focus-to-detector distances that were both calibrated with their method. The authors compare their reconstruction with a reconstruction computed by the manufacturer of the

ORNL calibrations facility

International Nuclear Information System (INIS)

Berger, C.D.; Gupton, E.D.; Lane, B.H.; Miller, J.H.; Nichols, S.W.

1982-08-01

The ORNL Calibrations Facility is operated by the Instrumentation Group of the Industrial Safety and Applied Health Physics Division. Its primary purpose is to maintain radiation calibration standards for calibration of ORNL health physics instruments and personnel dosimeters. This report includes a discussion of the radioactive sources and ancillary equipment in use and a step-by-step procedure for calibration of those survey instruments and personnel dosimeters in routine use at ORNL

Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model

Directory of Open Access Journals (Sweden)

Yi Liu

2016-01-01

Full Text Available Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of Pareto solutions, a more precise crowd distance assigner is presented in this paper. Furthermore, the traditional generalized spread metric (SP is sensitive with the size of Pareto set. A novel diversity performance metric, which is independent of Pareto set size, is put forward in this research. The efficacy of the new algorithm MODE-CMCS is compared with the nondominated sorting genetic algorithm II (NSGA-II on a daily streamflow forecasting model based on support vector machine (SVM. The results verify that the performance of MODE-CMCS is superior to the NSGA-II for automatic calibration of hydrologic model.

Non-linear calibration models for near infrared spectroscopy

DEFF Research Database (Denmark)

Ni, Wangdong; Nørgaard, Lars; Mørup, Morten

2014-01-01

by ridge regression (RR). The performance of the different methods is demonstrated by their practical applications using three real-life near infrared (NIR) data sets. Different aspects of the various approaches including computational time, model interpretability, potential over-fitting using the non-linear...... models on linear problems, robustness to small or medium sample sets, and robustness to pre-processing, are discussed. The results suggest that GPR and BANN are powerful and promising methods for handling linear as well as nonlinear systems, even when the data sets are moderately small. The LS......-SVM), relevance vector machines (RVM), Gaussian process regression (GPR), artificial neural network (ANN), and Bayesian ANN (BANN). In this comparison, partial least squares (PLS) regression is used as a linear benchmark, while the relationship of the methods is considered in terms of traditional calibration...

Real-time fMRI pattern decoding and neurofeedback using FRIEND: an FSL-integrated BCI toolbox.

Science.gov (United States)

Sato, João R; Basilio, Rodrigo; Paiva, Fernando F; Garrido, Griselda J; Bramati, Ivanei E; Bado, Patricia; Tovar-Moll, Fernanda; Zahn, Roland; Moll, Jorge

2013-01-01

The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available.

Real-time fMRI pattern decoding and neurofeedback using FRIEND: an FSL-integrated BCI toolbox.

Directory of Open Access Journals (Sweden)

João R Sato

Full Text Available The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM, fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available.

Real-Time fMRI Pattern Decoding and Neurofeedback Using FRIEND: An FSL-Integrated BCI Toolbox

Science.gov (United States)

Sato, João R.; Basilio, Rodrigo; Paiva, Fernando F.; Garrido, Griselda J.; Bramati, Ivanei E.; Bado, Patricia; Tovar-Moll, Fernanda; Zahn, Roland; Moll, Jorge

2013-01-01

The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available. PMID:24312569

SCIAMACHY Level 1 data: calibration concept and in-flight calibration

Science.gov (United States)

Lichtenberg, G.; Kleipool, Q.; Krijger, J. M.; van Soest, G.; van Hees, R.; Tilstra, L. G.; Acarreta, J. R.; Aben, I.; Ahlers, B.; Bovensmann, H.; Chance, K.; Gloudemans, A. M. S.; Hoogeveen, R. W. M.; Jongma, R. T. N.; Noël, S.; Piters, A.; Schrijver, H.; Schrijvers, C.; Sioris, C. E.; Skupin, J.; Slijkhuis, S.; Stammes, P.; Wuttke, M.

2006-11-01

The calibration of SCIAMACHY was thoroughly checked since the instrument was launched on-board ENVISAT in February 2002. While SCIAMACHY's functional performance is excellent since launch, a number of technical difficulties have appeared, that required adjustments to the calibration. The problems can be separated into three types: (1) Those caused by the instrument and/or platform environment. Among these are the high water content in the satellite structure and/or MLI layer. This results in the deposition of ice on the detectors in channels 7 and 8 which seriously affects the retrievals in the IR, mostly because of the continuous change of the slit function caused by scattering of the light through the ice layer. Additionally a light leak in channel 7 severely hampers any retrieval from this channel. (2) Problems due to errors in the on-ground calibration and/or data processing affecting for example the radiometric calibration. A new approach based on a mixture of on-ground and in-flight data is shortly described here. (3) Problems caused by principal limitations of the calibration concept, e.g. the possible appearance of spectral structures after the polarisation correction due to unavoidable errors in the determination of atmospheric polarisation. In this paper we give a complete overview of the calibration and problems that still have to be solved. We will also give an indication of the effect of calibration problems on retrievals where possible. Since the operational processing chain is currently being updated and no newly processed data are available at this point in time, for some calibration issues only a rough estimate of the effect on Level 2 products can be given. However, it is the intention of this paper to serve as a future reference for detailed studies into specific calibration issues.

The MRI appearance of the optic nerve sheath following fenestration for benign intracranial hypertension

Energy Technology Data Exchange (ETDEWEB)

Sallomi, D.; Taylor, H.; Hibbert, J.; Sanders, M.D.; Spalton, D.J.; Tonge, K. [Guys and St. Thomas` Hospitals, London (United Kingdom)

1998-09-01

Optic nerve fenestration is carried out in cases of severe benign intracranial hypertension. This study aimed to monitor the optic nerve sheath appearances and orbital changes that occur following this procedure. The eight patients were all female with an average age of 37.3 years and a range of 20-58 years. The duration of symptoms was 2-6 years. Symptoms included headaches, diplopia and visual obscurations. Examination revealed severe papilledema. All investigations, including MRI, biochemical and immunological tests, were negative. Patients had fenestration of a 2 mm x 3 mm segment of the medial aspect of the optic nerve sheath. Imaging was obtained with a 1 T MRI machine using a head coil. Coronal, axial and sagittal 3 mm contiguous sections using STIR sequences with TR 4900 ms, IT 150 ms and TE 60 ms were obtained. Five patients showed clinical improvement. The post-operative MRI findings in four of these included a decreased volume of cerebrospinal fluid (CSF) around the optic nerve sheaths and a localized collection of fluid within the orbit. There were no MRI changes in the three patients with no clinical improvement. Decreased CSF volume around the optic nerve and a fluid collection within the orbit may indicate a favorable outcome in optic nerve fenestration. (orig.) With 3 figs., 12 refs.

Reliability of MRI assessment of acute musculotendinous groin injuries in athletes

Energy Technology Data Exchange (ETDEWEB)

Serner, Andreas; Hoelmich, Per [Aspetar, Orthopaedic and Sports Medicine Hospital, Sports City Street, P.O. Box 29222, Doha (Qatar); Copenhagen University Hospital, Sports Orthopaedic Research Center-Copenhagen (SORC-C), Department of Orthopedic Surgery, Amager-Hvidovre (Denmark); Roemer, Frank W. [University of Erlangen-Nuremberg, Department of Radiology, Erlangen (Germany); Boston University School of Medicine, Quantitative Imaging Center (QIC), Department of Radiology, Boston, MA (United States); Thorborg, Kristian [Copenhagen University Hospital, Sports Orthopaedic Research Center-Copenhagen (SORC-C), Department of Orthopedic Surgery, Amager-Hvidovre (Denmark); Niu, Jingbo [Boston University School of Medicine, Clinical Epidemiology and Training Unit, Department of Medicine, Boston, MA (United States); Weir, Adam [Aspetar, Orthopaedic and Sports Medicine Hospital, Sports City Street, P.O. Box 29222, Doha (Qatar); Tol, Johannes L. [Aspetar, Orthopaedic and Sports Medicine Hospital, Sports City Street, P.O. Box 29222, Doha (Qatar); OLVG West, The Sports Physician Group, Department of Sports Medicine, Amsterdam (Netherlands); Academic Medical Center, Amsterdam Center of Evidence Based Sports Medicine, Amsterdam (Netherlands); Guermazi, Ali [Boston University School of Medicine, Quantitative Imaging Center (QIC), Department of Radiology, Boston, MA (United States)

2017-04-15

To describe a multi-dimensional MRI assessment approach with a focus on acute musculotendinous groin lesions, and to evaluate scoring reproducibility. Male athletes who participated in competitive sports and presented within 7 days of an acute onset of sports-related groin pain were included. All athletes underwent MRI (1.5 T) according to a standardized groin-centred protocol. From several calibration sessions, a system was developed assessing grade, location and extent of muscle strains, peri-lesional haematoma, as well as other non-acute findings commonly associated with long-standing groin pain. Kappa (K) statistics and intraclass correlation coefficients (ICCs) were used to describe intra- and inter-rater reproducibility. Seventy-five athletes (mean age 26.6 ± 4.4 years) were included in the analyses, and 85 different acute lesions were observed. Adductor longus lesions were most common (42.7 %) followed by rectus femoris lesions (16.3 %). Kappa values ranged between 0.70 and 1.00 for almost all categorical features for acute lesions, with almost perfect intra- and inter-rater agreement (K = 0.89-1.00) for presence, number, location and grading of lesions. ICCs ranged between 0.77 and 1.00 for continuous measures of acute lesion extent. A standardized MRI assessment approach of acute groin injuries was described and showed good intra- and inter-rater reproducibility. (orig.)

Reliability of MRI assessment of acute musculotendinous groin injuries in athletes

International Nuclear Information System (INIS)

Serner, Andreas; Hoelmich, Per; Roemer, Frank W.; Thorborg, Kristian; Niu, Jingbo; Weir, Adam; Tol, Johannes L.; Guermazi, Ali

2017-01-01

To describe a multi-dimensional MRI assessment approach with a focus on acute musculotendinous groin lesions, and to evaluate scoring reproducibility. Male athletes who participated in competitive sports and presented within 7 days of an acute onset of sports-related groin pain were included. All athletes underwent MRI (1.5 T) according to a standardized groin-centred protocol. From several calibration sessions, a system was developed assessing grade, location and extent of muscle strains, peri-lesional haematoma, as well as other non-acute findings commonly associated with long-standing groin pain. Kappa (K) statistics and intraclass correlation coefficients (ICCs) were used to describe intra- and inter-rater reproducibility. Seventy-five athletes (mean age 26.6 ± 4.4 years) were included in the analyses, and 85 different acute lesions were observed. Adductor longus lesions were most common (42.7 %) followed by rectus femoris lesions (16.3 %). Kappa values ranged between 0.70 and 1.00 for almost all categorical features for acute lesions, with almost perfect intra- and inter-rater agreement (K = 0.89-1.00) for presence, number, location and grading of lesions. ICCs ranged between 0.77 and 1.00 for continuous measures of acute lesion extent. A standardized MRI assessment approach of acute groin injuries was described and showed good intra- and inter-rater reproducibility. (orig.)

Monitoring machining conditions by infrared images

Science.gov (United States)

Borelli, Joao E.; Gonzaga Trabasso, Luis; Gonzaga, Adilson; Coelho, Reginaldo T.

2001-03-01

During machining process the knowledge of the temperature is the most important factor in tool analysis. It allows to control main factors that influence tool use, life time and waste. The temperature in the contact area between the piece and the tool is resulting from the material removal in cutting operation and it is too difficult to be obtained because the tool and the work piece are in motion. One way to measure the temperature in this situation is detecting the infrared radiation. This work presents a new methodology for diagnosis and monitoring of machining processes with the use of infrared images. The infrared image provides a map in gray tones of the elements in the process: tool, work piece and chips. Each gray tone in the image corresponds to a certain temperature for each one of those materials and the relationship between the gray tones and the temperature is gotten by the previous of infrared camera calibration. The system developed in this work uses an infrared camera, a frame grabber board and a software composed of three modules. The first module makes the image acquisition and processing. The second module makes the feature image extraction and performs the feature vector. Finally, the third module uses fuzzy logic to evaluate the feature vector and supplies the tool state diagnostic as output.

Non-invasive in vivo evaluation of in situ forming PLGA implants by benchtop magnetic resonance imaging (BT-MRI) and EPR spectroscopy.

Science.gov (United States)

Kempe, Sabine; Metz, Hendrik; Pereira, Priscila G C; Mäder, Karsten

2010-01-01

In the present study, we used benchtop magnetic resonance imaging (BT-MRI) for non-invasive and continuous in vivo studies of in situ forming poly(lactide-co-glycolide) (PLGA) implants without the use of contrast agents. Polyethylene glycol (PEG) 400 was used as an alternative solvent to the clinically used NMP. In addition to BT-MRI, we applied electron paramagnetic resonance (EPR) spectroscopy to characterize implant formation and drug delivery processes in vitro and in vivo. We were able to follow key processes of implant formation by EPR and MRI. Because EPR spectra are sensitive to polarity and mobility, we were able to follow the kinetics of the solvent/non-solvent exchange and the PLGA precipitation. Due to the high water affinity of PEG 400, we observed a transient accumulation of water in the implant neighbourhood. Furthermore, we detected the encapsulation by BT-MRI of the implant as a response of the biological system to the polymer, followed by degradation over a period of two months. We could show that MRI in general has the potential to get new insights in the in vivo fate of in situ forming implants. The study also clearly shows that BT-MRI is a new viable and much less expensive alternative for superconducting MRI machines to monitor drug delivery processes in vivo in small mammals. Copyright 2009 Elsevier B.V. All rights reserved.

Calibration of the Nonlinear Accelerator Model at the Diamond Storage Ring

CERN Document Server

Bartolini, Riccardo; Rowland, James; Martin, Ian; Schmidt, Frank

2010-01-01

The correct implementation of the nonlinear ring model is crucial to achieve the top performance of a synchrotron light source. Several dynamics quantities can be used to compare the real machine with the model and eventually to correct the accelerator. Most of these methods are based on the analysis of turn-by-turn data of excited betatron oscillations. We present the experimental results of the campaign of measurements carried out at the Diamond. A combination of Frequency Map Analysis (FMA) and detuning with momentum measurements has allowed a precise calibration of the nonlinear model capable of reproducing the nonlinear beam dynamics in the storage ring

Machine terms dictionary

Energy Technology Data Exchange (ETDEWEB)

NONE

1979-04-15

This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.

In pursuit of precision: the calibration of minds and machines in late nineteenth-century psychology.

Science.gov (United States)

Benschop, R; Draaisma, D

2000-01-01

A prominent feature of late nineteenth-century psychology was its intense preoccupation with precision. Precision was at once an ideal and an argument: the quest for precision helped psychology to establish its status as a mature science, sharing a characteristic concern with the natural sciences. We will analyse how psychologists set out to produce precision in 'mental chronometry', the measurement of the duration of psychological processes. In his Leipzig laboratory, Wundt inaugurated an elaborate research programme on mental chronometry. We will look at the problem of calibration of experimental apparatus and will describe the intricate material, literary, and social technologies involved in the manufacture of precision. First, we shall discuss some of the technical problems involved in the measurement of ever shorter time-spans. Next, the Cattell-Berger experiments will help us to argue against the received view that all the precision went into the hardware, and practically none into the social organization of experimentation. Experimenters made deliberate efforts to bring themselves and their subjects under a regime of control and calibration similar to that which reigned over the experimental machinery. In Leipzig psychology, the particular blend of material and social technology resulted in a specific object of study: the generalized mind. We will then show that the distribution of precision in experimental psychology outside Leipzig demanded a concerted effort of instruments, texts, and people. It will appear that the forceful attempts to produce precision and uniformity had some rather paradoxical consequences.

Monitoring scanner calibration using the image-derived arterial blood SUV in whole-body FDG-PET.

Science.gov (United States)

Maus, Jens; Hofheinz, Frank; Apostolova, Ivayla; Kreissl, Michael C; Kotzerke, Jörg; van den Hoff, Jörg

2018-05-15

The current de facto standard for quantification of tumor metabolism in oncological whole-body PET is the standardized uptake value (SUV) approach. SUV determination requires accurate scanner calibration. Residual inaccuracies of the calibration lead to biased SUV values. Especially, this can adversely affect multicenter trials where it is difficult to ensure reliable cross-calibration across participating sites. The goal of the present work was the evaluation of a new method for monitoring scanner calibration utilizing the image-derived arterial blood SUV (BSUV) averaged over a sufficiently large number of whole-body FDG-PET investigations. Data of 681 patients from three sites which underwent routine 18 F-FDG PET/CT or PET/MR were retrospectively analyzed. BSUV was determined in the descending aorta using a three-dimensional ROI concentric to the aorta's centerline. The ROI was delineated in the CT or MRI images and transferred to the PET images. A minimum ROI volume of 5 mL and a concentric safety margin to the aortic wall was observed. Mean BSUV, standard deviation (SD), and standard error of the mean (SE) were computed for three groups of patients at each site, investigated 2 years apart, respectively, with group sizes between 53 and 100 patients. Differences of mean BSUV between the individual groups and sites were determined. SD (SE) of BSUV in the different groups ranged from 14.3 to 20.7% (1.7 to 2.8%). Differences of mean BSUV between intra-site groups were small (1.1-6.3%). Only one out of nine of these differences reached statistical significance. Inter-site differences were distinctly larger (12.6-25.1%) and highly significant (PPET investigations is a viable approach for ensuring consistent scanner calibration over time and across different sites. We propose this approach as a quality control and cross-calibration tool augmenting established phantom-based procedures.

Spitzer/JWST Cross Calibration: IRAC Observations of Potential Calibrators for JWST

Science.gov (United States)

Carey, Sean J.; Gordon, Karl D.; Lowrance, Patrick; Ingalls, James G.; Glaccum, William J.; Grillmair, Carl J.; E Krick, Jessica; Laine, Seppo J.; Fazio, Giovanni G.; Hora, Joseph L.; Bohlin, Ralph

2017-06-01

We present observations at 3.6 and 4.5 microns using IRAC on the Spitzer Space Telescope of a set of main sequence A stars and white dwarfs that are potential calibrators across the JWST instrument suite. The stars range from brightnesses of 4.4 to 15 mag in K band. The calibration observations use a similar redundancy to the observing strategy for the IRAC primary calibrators (Reach et al. 2005) and the photometry is obtained using identical methods and instrumental photometric corrections as those applied to the IRAC primary calibrators (Carey et al. 2009). The resulting photometry is then compared to the predictions based on spectra from the CALSPEC Calibration Database (http://www.stsci.edu/hst/observatory/crds/calspec.html) and the IRAC bandpasses. These observations are part of an ongoing collaboration between IPAC and STScI investigating absolute calibration in the infrared.

Studies on MRI diagnostic accuracy of invasion to body muscular layer and cervix of endometrial cancer

International Nuclear Information System (INIS)

Takemoto, Yumi; Fujiyoshi, Keizou; Takemoto, Shuji; Kawano, Kouichirou; Ohta, Shunichirou; Murakami, Fumihiro; Komai, Kan; Ushijima, Kimio; Kamura, Toshiharu

2008-01-01

This study was conducted to know usefulness of preoperative MRI to detect invasions of endometrial cancer to uterine body muscular layer and cervix. Subjects were 132 patients (median age of 57 y, pre- and post-menopause, 11.2 and 78.8%, respectively) with the cancer at stage I (66 cases) and >II in authors' facility, who had undergone the preoperative MRI with 1.5T Siemens machine by imaging with T1 and T2 weighted, Gd-enhanced T1 weighted, dynamic study and STIR. Imaging findings were compared with histopathological ones to assess the accuracy of imaging diagnosis. Positive predictive accuracy for muscular invasion was found to be as high as 95.5% and negative one, as low as 29.5%: especially, in pre-menopause group, tendency of underestimation for the invasion was thought notable. In contrast, negative accuracy was found low for cervical invasion and positive one, high: overestimation was possibly occurring. Thus, MRI diagnosis of those invasions should be seriously judged with careful consideration of menopause state. (R.T.)

Development of Dual-Axis MEMS Accelerometers for Machine Tools Vibration Monitoring

Directory of Open Access Journals (Sweden)

Chih-Yung Huang

2016-07-01

Full Text Available With the development of intelligent machine tools, monitoring the vibration by the accelerometer is an important issue. Accelerometers used for measuring vibration signals during milling processes require the characteristics of high sensitivity, high resolution, and high bandwidth. A commonly used accelerometer is the lead zirconate titanate (PZT type; however, integrating it into intelligent modules is excessively expensive and difficult. Therefore, the micro electro mechanical systems (MEMS accelerometer is an alternative with the advantages of lower price and superior integration. In the present study, we integrated two MEMS accelerometer chips into a low-pass filter and housing to develop a low-cost dual-axis accelerometer with a bandwidth of 5 kHz and a full scale range of ±50 g for measuring machine tool vibration. In addition, a platform for measuring the linearity, cross-axis sensitivity and frequency response of the MEMS accelerometer by using the back-to-back calibration method was also developed. Finally, cutting experiments with steady and chatter cutting were performed to verify the results of comparing the MEMS accelerometer with the PZT accelerometer in the time and frequency domains. The results demonstrated that the dual-axis MEMS accelerometer is suitable for monitoring the vibration of machine tools at low cost.

Some relations between quantum Turing machines and Turing machines

OpenAIRE

Sicard, Andrés; Vélez, Mario

1999-01-01

For quantum Turing machines we present three elements: Its components, its time evolution operator and its local transition function. The components are related with the components of deterministic Turing machines, the time evolution operator is related with the evolution of reversible Turing machines and the local transition function is related with the transition function of probabilistic and reversible Turing machines.

A Tool for Creating Regionally Calibrated High-Resolution Land Cover Data Sets for the West African Sahel: Using Machine Learning to Scale Up Hand-Classified Maps in a Data-Sparse Environment

Science.gov (United States)

Van Gordon, M.; Van Gordon, S.; Min, A.; Sullivan, J.; Weiner, Z.; Tappan, G. G.

2017-12-01

Using support vector machine (SVM) learning and high-accuracy hand-classified maps, we have developed a publicly available land cover classification tool for the West African Sahel. Our classifier produces high-resolution and regionally calibrated land cover maps for the Sahel, representing a significant contribution to the data available for this region. Global land cover products are unreliable for the Sahel, and accurate land cover data for the region are sparse. To address this gap, the U.S. Geological Survey and the Regional Center for Agriculture, Hydrology and Meteorology (AGRHYMET) in Niger produced high-quality land cover maps for the region via hand-classification of Landsat images. This method produces highly accurate maps, but the time and labor required constrain the spatial and temporal resolution of the data products. By using these hand-classified maps alongside SVM techniques, we successfully increase the resolution of the land cover maps by 1-2 orders of magnitude, from 2km-decadal resolution to 30m-annual resolution. These high-resolution regionally calibrated land cover datasets, along with the classifier we developed to produce them, lay the foundation for major advances in studies of land surface processes in the region. These datasets will provide more accurate inputs for food security modeling, hydrologic modeling, analyses of land cover change and climate change adaptation efforts. The land cover classification tool we have developed will be publicly available for use in creating additional West Africa land cover datasets with future remote sensing data and can be adapted for use in other parts of the world.

Simultaneous calibration phantom commission and geometry calibration in cone beam CT

Science.gov (United States)

Xu, Yuan; Yang, Shuai; Ma, Jianhui; Li, Bin; Wu, Shuyu; Qi, Hongliang; Zhou, Linghong

2017-09-01

Geometry calibration is a vital step for describing the geometry of a cone beam computed tomography (CBCT) system and is a prerequisite for CBCT reconstruction. In current methods, calibration phantom commission and geometry calibration are divided into two independent tasks. Small errors in ball-bearing (BB) positioning in the phantom-making step will severely degrade the quality of phantom calibration. To solve this problem, we propose an integrated method to simultaneously realize geometry phantom commission and geometry calibration. Instead of assuming the accuracy of the geometry phantom, the integrated method considers BB centers in the phantom as an optimized parameter in the workflow. Specifically, an evaluation phantom and the corresponding evaluation contrast index are used to evaluate geometry artifacts for optimizing the BB coordinates in the geometry phantom. After utilizing particle swarm optimization, the CBCT geometry and BB coordinates in the geometry phantom are calibrated accurately and are then directly used for the next geometry calibration task in other CBCT systems. To evaluate the proposed method, both qualitative and quantitative studies were performed on simulated and realistic CBCT data. The spatial resolution of reconstructed images using dental CBCT can reach up to 15 line pair cm-1. The proposed method is also superior to the Wiesent method in experiments. This paper shows that the proposed method is attractive for simultaneous and accurate geometry phantom commission and geometry calibration.

Combined fMRI- and eye movement-based decoding of bistable plaid motion perception.

Science.gov (United States)

Wilbertz, Gregor; Ketkar, Madhura; Guggenmos, Matthias; Sterzer, Philipp

2018-05-01

The phenomenon of bistable perception, in which perception alternates spontaneously despite constant sensory stimulation, has been particularly useful in probing the neural bases of conscious perception. The study of such bistability requires access to the observer's perceptual dynamics, which is usually achieved via active report. This report, however, constitutes a confounding factor in the study of conscious perception and can also be biased in the context of certain experimental manipulations. One approach to circumvent these problems is to track perceptual alternations using signals from the eyes or the brain instead of observers' reports. Here we aimed to optimize such decoding of perceptual alternations by combining eye and brain signals. Eye-tracking and functional magnetic resonance imaging (fMRI) was performed in twenty participants while they viewed a bistable visual plaid motion stimulus and reported perceptual alternations. Multivoxel pattern analysis (MVPA) for fMRI was combined with eye-tracking in a Support vector machine to decode participants' perceptual time courses from fMRI and eye-movement signals. While both measures individually already yielded high decoding accuracies (on average 86% and 88% correct, respectively) classification based on the two measures together further improved the accuracy (91% correct). These findings show that leveraging on both fMRI and eye movement data may pave the way for optimized no-report paradigms through improved decodability of bistable motion perception and hence for a better understanding of the neural correlates of consciousness. Copyright © 2018 Elsevier Inc. All rights reserved.

Comparison of static MRI and pseudo-dynamic MRI in temporomandibular joint disorder patients

International Nuclear Information System (INIS)

Lee, Jin Ho; Yun, Kyoung In; Park, In Woo; Choi, Hang Moon; Park, Moon Soo

2006-01-01

The purpose of this study was to elevate comparison of static MRI and pseudo-dynamic (cine) MRI in temporomandibular joint (TMJ) disorder patients. In this investigation, 33 patients with TMJ disorders were examined using both conventional static MRI and pseudo-dynamic MRI. Multiple spoiled gradient recalled acquisition in the steady state (SPGR) images were obtained when mouth opened and closed. Proton density weighted images were obtained at the closed and open mouth position in static MRI. Two oral and maxillofacial radiologists evaluated location of the articular disk, movement of condyle and bony change respectively and the posterior boundary of articular disk was obtained. No statistically significant difference was found in the observation of articular disk position, mandibular condylar movement and posterior boundary of articular disk using static MRI and pseudo-dynamic MRI (P>0.05). Statistically significant difference was noted in bony changes of condyle using static MRI and pseudo-dynamic MRI (P<0.05). This study showed that pseudo-dynamic MRI didn't make a difference in diagnosing internal derangement of TMJ in comparison with static MRI. But it was considered as an additional method to be supplemented in observing bony change

Comparison of static MRI and pseudo-dynamic MRI in temporomandibular joint disorder patients

Energy Technology Data Exchange (ETDEWEB)

Lee, Jin Ho; Yun, Kyoung In [Eulji Univ. School of Medicine, Seoul (Korea, Republic of); Park, In Woo; Choi, Hang Moon; Park, Moon Soo [Kangnung National Univ. College of Dentistry, Kangnung (Korea, Republic of)

2006-12-15

The purpose of this study was to elevate comparison of static MRI and pseudo-dynamic (cine) MRI in temporomandibular joint (TMJ) disorder patients. In this investigation, 33 patients with TMJ disorders were examined using both conventional static MRI and pseudo-dynamic MRI. Multiple spoiled gradient recalled acquisition in the steady state (SPGR) images were obtained when mouth opened and closed. Proton density weighted images were obtained at the closed and open mouth position in static MRI. Two oral and maxillofacial radiologists evaluated location of the articular disk, movement of condyle and bony change respectively and the posterior boundary of articular disk was obtained. No statistically significant difference was found in the observation of articular disk position, mandibular condylar movement and posterior boundary of articular disk using static MRI and pseudo-dynamic MRI (P>0.05). Statistically significant difference was noted in bony changes of condyle using static MRI and pseudo-dynamic MRI (P<0.05). This study showed that pseudo-dynamic MRI didn't make a difference in diagnosing internal derangement of TMJ in comparison with static MRI. But it was considered as an additional method to be supplemented in observing bony change.

MRI dosimetry using an echo-quotient technique for high dose rate brachytherapy

International Nuclear Information System (INIS)

Ansbacher, W.

1996-01-01

MRI gel dosimetry is a relatively new technique that has many advantages over conventional methods, and is particularly suited to High Dose Rate (HDR) Brachytherapy. The dosimeter has high spatial resolution and a water-equivalent response over a wide range of photon energies. Because it is an integrating dosimeter, it allows for efficient mapping of the dynamically-produced distributions from an HDR source. As an example of this technique, the dose response, which is calibrated in terms of the change in spin-spin relaxation time, has been used to investigate the anisotropy of an HDR source. (author). 1 fig

SPRT Calibration Uncertainties and Internal Quality Control at a Commercial SPRT Calibration Facility

Science.gov (United States)

Wiandt, T. J.

2008-06-01

The Hart Scientific Division of the Fluke Corporation operates two accredited standard platinum resistance thermometer (SPRT) calibration facilities, one at the Hart Scientific factory in Utah, USA, and the other at a service facility in Norwich, UK. The US facility is accredited through National Voluntary Laboratory Accreditation Program (NVLAP), and the UK facility is accredited through UKAS. Both provide SPRT calibrations using similar equipment and procedures, and at similar levels of uncertainty. These uncertainties are among the lowest available commercially. To achieve and maintain low uncertainties, it is required that the calibration procedures be thorough and optimized. However, to minimize customer downtime, it is also important that the instruments be calibrated in a timely manner and returned to the customer. Consequently, subjecting the instrument to repeated calibrations or extensive repeated measurements is not a viable approach. Additionally, these laboratories provide SPRT calibration services involving a wide variety of SPRT designs. These designs behave differently, yet predictably, when subjected to calibration measurements. To this end, an evaluation strategy involving both statistical process control and internal consistency measures is utilized to provide confidence in both the instrument calibration and the calibration process. This article describes the calibration facilities, procedure, uncertainty analysis, and internal quality assurance measures employed in the calibration of SPRTs. Data will be reviewed and generalities will be presented. Finally, challenges and considerations for future improvements will be discussed.

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

SU-F-J-166: Volumetric Spatial Distortions Comparison for 1.5 Tesla Versus 3 Tesla MRI for Gamma Knife Radiosurgery Scans Using Frame Marker Fusion and Co-Registration Modes

International Nuclear Information System (INIS)

Neyman, G

2016-01-01

Purpose: To compare typical volumetric spatial distortions for 1.5 Tesla versus 3 Tesla MRI Gamma Knife radiosurgery scans in the frame marker fusion and co-registration frame-less modes. Methods: Quasar phantom by Modus Medical Devices Inc. with GRID image distortion software was used for measurements of volumetric distortions. 3D volumetric T1 weighted scans of the phantom were produced on 1.5 T Avanto and 3 T Skyra MRI Siemens scanners. The analysis was done two ways: for scans with localizer markers from the Leksell frame and relatively to the phantom only (simulated co-registration technique). The phantom grid contained a total of 2002 vertices or control points that were used in the assessment of volumetric geometric distortion for all scans. Results: Volumetric mean absolute spatial deviations relatively to the frame localizer markers for 1.5 and 3 Tesla machine were: 1.39 ± 0.15 and 1.63 ± 0.28 mm with max errors of 1.86 and 2.65 mm correspondingly. Mean 2D errors from the Gamma Plan were 0.3 and 1.0 mm. For simulated co-registration technique the volumetric mean absolute spatial deviations relatively to the phantom for 1.5 and 3 Tesla machine were: 0.36 ± 0.08 and 0.62 ± 0.13 mm with max errors of 0.57 and 1.22 mm correspondingly. Conclusion: Volumetric spatial distortions are lower for 1.5 Tesla versus 3 Tesla MRI machines localized with markers on frames and significantly lower for co-registration techniques with no frame localization. The results show the advantage of using co-registration technique for minimizing MRI volumetric spatial distortions which can be especially important for steep dose gradient fields typically used in Gamma Knife radiosurgery. Consultant for Elekta AB

SU-F-J-166: Volumetric Spatial Distortions Comparison for 1.5 Tesla Versus 3 Tesla MRI for Gamma Knife Radiosurgery Scans Using Frame Marker Fusion and Co-Registration Modes

Energy Technology Data Exchange (ETDEWEB)

Neyman, G [The Cleveland Clinic Foundation, Cleveland, OH (United States)

2016-06-15

Purpose: To compare typical volumetric spatial distortions for 1.5 Tesla versus 3 Tesla MRI Gamma Knife radiosurgery scans in the frame marker fusion and co-registration frame-less modes. Methods: Quasar phantom by Modus Medical Devices Inc. with GRID image distortion software was used for measurements of volumetric distortions. 3D volumetric T1 weighted scans of the phantom were produced on 1.5 T Avanto and 3 T Skyra MRI Siemens scanners. The analysis was done two ways: for scans with localizer markers from the Leksell frame and relatively to the phantom only (simulated co-registration technique). The phantom grid contained a total of 2002 vertices or control points that were used in the assessment of volumetric geometric distortion for all scans. Results: Volumetric mean absolute spatial deviations relatively to the frame localizer markers for 1.5 and 3 Tesla machine were: 1.39 ± 0.15 and 1.63 ± 0.28 mm with max errors of 1.86 and 2.65 mm correspondingly. Mean 2D errors from the Gamma Plan were 0.3 and 1.0 mm. For simulated co-registration technique the volumetric mean absolute spatial deviations relatively to the phantom for 1.5 and 3 Tesla machine were: 0.36 ± 0.08 and 0.62 ± 0.13 mm with max errors of 0.57 and 1.22 mm correspondingly. Conclusion: Volumetric spatial distortions are lower for 1.5 Tesla versus 3 Tesla MRI machines localized with markers on frames and significantly lower for co-registration techniques with no frame localization. The results show the advantage of using co-registration technique for minimizing MRI volumetric spatial distortions which can be especially important for steep dose gradient fields typically used in Gamma Knife radiosurgery. Consultant for Elekta AB.

Stereoscopic Machine-Vision System Using Projected Circles

Science.gov (United States)

Mackey, Jeffrey R.

2010-01-01

A machine-vision system capable of detecting obstacles large enough to damage or trap a robotic vehicle is undergoing development. The system includes (1) a pattern generator that projects concentric circles of laser light forward onto the terrain, (2) a stereoscopic pair of cameras that are aimed forward to acquire images of the circles, (3) a frame grabber and digitizer for acquiring image data from the cameras, and (4) a single-board computer that processes the data. The system is being developed as a prototype of machine- vision systems to enable robotic vehicles ( rovers ) on remote planets to avoid craters, large rocks, and other terrain features that could capture or damage the vehicles. Potential terrestrial applications of systems like this one could include terrain mapping, collision avoidance, navigation of robotic vehicles, mining, and robotic rescue. This system is based partly on the same principles as those of a prior stereoscopic machine-vision system in which the cameras acquire images of a single stripe of laser light that is swept forward across the terrain. However, this system is designed to afford improvements over some of the undesirable features of the prior system, including the need for a pan-and-tilt mechanism to aim the laser to generate the swept stripe, ambiguities in interpretation of the single-stripe image, the time needed to sweep the stripe across the terrain and process the data from many images acquired during that time, and difficulty of calibration because of the narrowness of the stripe. In this system, the pattern generator does not contain any moving parts and need not be mounted on a pan-and-tilt mechanism: the pattern of concentric circles is projected steadily in the forward direction. The system calibrates itself by use of data acquired during projection of the concentric-circle pattern onto a known target representing flat ground. The calibration- target image data are stored in the computer memory for use as a

Calibration and intercomparison methods of dose calibrators used in nuclear medicine facilities

International Nuclear Information System (INIS)

Costa, Alessandro Martins da

1999-01-01

Dose calibrators are used in most of the nuclear medicine facilities to determine the amount of radioactivity administered to a patient in a particular investigation or therapeutic procedure. It is therefore of vital importance that the equipment used presents good performance and is regular;y calibrated at a authorized laboratory. This occurs of adequate quality assurance procedures are carried out. Such quality control tests should be performed daily, other biannually or yearly, testing, for example, its accuracy and precision, the reproducibility and response linearity. In this work a commercial dose calibrator was calibrated with solution of radionuclides used in nuclear medicine. Simple instrument tests, such as response linearity and the response variation of the source volume increase at a constant source activity concentration, were performed. This instrument can now be used as a working standard for calibration of other dose calibrators/ An intercomparison procedure was proposed as a method of quality control of dose calibrators used in nuclear medicine facilities. (author)

Fuzzy Computer-Aided Alzheimer's Disease Diagnosis Based on MRI Data.

Science.gov (United States)

Krashenyi, Igor; Ramírez, Javier; Popov, Anton; Górriz, Juan Manuel; The Alzheimer's Disease Neuroimaging Initiative

2016-01-01

Alzheimer's disease (AD) is a chronic neurodegenerative disease of the central nervous system that has no cure and leads to death. One of the most prevalent tools for AD diagnosis is magnetic resonance imaging (MRI), because of its capability to visualize brain anatomical structures. There is a variety of classification methods for automatic diagnosis of AD, such as support vector machines, genetic algorithms, Bayes classifiers, neural networks, random forests, etc., but none of them provides robust information about the stage of the AD, they can just reveal the presence of disease. In this paper, a new approach for classification of MRI images using a fuzzy inference system is proposed. Two statistical moments (mean and standard deviation) of 116 anatomical regions of interests (ROIs) are used as input features for the classification system. A t-test feature selection method is used to identify the most discriminative ROIs. In order to evaluate the proposed system, MRI images from a database consisting of 818 subjects (229 normal, 401 mild cognitive impairment and 188 AD subjects) collected from the Alzheimer's disease neuroimaging initiative (ADNI) is analyzed. The receiver operating characteristics (ROC) curve and the area under the curve (AUC) of the proposed fuzzy inference system fed by statistical input features are employed as the evaluation criteria with k-fold cross validation. The proposed system yields promising results in normal vs. AD classification with AUC of 0.99 on the training set and 0.8622±0.0033 on the testing set.

Efficient solution methodology for calibrating the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements

KAUST Repository

Zambri, Brian

2015-11-05

Our aim is to propose a numerical strategy for retrieving accurately and efficiently the biophysiological parameters as well as the external stimulus characteristics corresponding to the hemodynamic mathematical model that describes changes in blood flow and blood oxygenation during brain activation. The proposed method employs the TNM-CKF method developed in [1], but in a prediction/correction framework. We present numerical results using both real and synthetic functional Magnetic Resonance Imaging (fMRI) measurements to highlight the performance characteristics of this computational methodology. © 2015 IEEE.

Efficient solution methodology for calibrating the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements

KAUST Repository

Zambri, Brian; Djellouli, Rabia; Laleg-Kirati, Taous-Meriem

2015-01-01

Our aim is to propose a numerical strategy for retrieving accurately and efficiently the biophysiological parameters as well as the external stimulus characteristics corresponding to the hemodynamic mathematical model that describes changes in blood flow and blood oxygenation during brain activation. The proposed method employs the TNM-CKF method developed in [1], but in a prediction/correction framework. We present numerical results using both real and synthetic functional Magnetic Resonance Imaging (fMRI) measurements to highlight the performance characteristics of this computational methodology. © 2015 IEEE.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

Science.gov (United States)

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Mechanics of log calibration

International Nuclear Information System (INIS)

Waller, W.C.; Cram, M.E.; Hall, J.E.

1975-01-01

For any measurement to have meaning, it must be related to generally accepted standard units by a valid and specified system of comparison. To calibrate well-logging tools, sensing systems are designed which produce consistent and repeatable indications over the range for which the tool was intended. The basics of calibration theory, procedures, and calibration record presentations are reviewed. Calibrations for induction, electrical, radioactivity, and sonic logging tools will be discussed. The authors' intent is to provide an understanding of the sources of errors, of the way errors are minimized in the calibration process, and of the significance of changes in recorded calibration data

Novel MRI techniques in the assessment of dementia

Energy Technology Data Exchange (ETDEWEB)

Teipel, Stefan J. [Ludwig-Maximilian University, Dementia and Neuroimaging Section, Department of Psychiatry, Alzheimer Memorial Center, Munich (Germany); University Rostock, Department of Psychiatry, Rostock (Germany); Meindl, Thomas [Ludwig-Maximilian University, Department of Clinical Radiology, University Hospitals-Grosshadern, Munich (Germany); Grinberg, Lea [Departamento di Patologia da FMUSP, Sao Paulo (Brazil); Instituto Israelita de Ensino e Pesquisa Albert Einstein, Sao Paulo (Brazil); Heinsen, Helmut [University Wuerzburg, Morphological Brain Research Unit, Wuerzburg (Germany); Hampel, Harald [Ludwig-Maximilian University, Dementia and Neuroimaging Section, Department of Psychiatry, Alzheimer Memorial Center, Munich (Germany); The Adelaide and Meath Hospital, Incorporating The National Children' s Hospital and Trinity College Institute of Neuroscience, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin (Ireland)

2008-03-15

Positive markers of Alzheimer's disease (AD) have been established in MRI that may allow early detection of AD in at-risk groups. In the near future, these markers will be of high relevance for the selection of at-risk subjects in secondary preventive trials. We describe the methodology and diagnostic value of manual volumetry of the hippocampus and entorhinal cortex, automated voxel-based morphometry, cortical thickness measurement, basal forebrain volumetry and deformation-based morphometry, implementing multivariate statistics and machine learning algorithms to improve group separation and prediction of AD in at-risk groups. We also describe the methodological basis and results obtained in AD using the recently developed technique of diffusion tensor-based morphometry (DTI). This technique gives access to the integrity of subcortical fibre systems in the human brain. The best established structural biomarker of AD to date is hippocampus volume that already has been implemented as secondary endpoint in clinical trials on disease modification in AD. Automated approaches will gain an increasing role as endpoints of clinical trials in the near future given the interest in these techniques expressed by the regulatory authorities. DTI is still a developing field where analysis techniques are presently being devised to make optimal use of the multivariate data. Data on changes of fibre tract in preclinical AD are still limited, but the first results are promising in respect to a further enhancement of diagnostic accuracy by combining MRI and DTI. Besides their diagnostic use, MRI and DTI will broaden our understanding of the pathophysiology of AD and the structural and functional basis of normal cognition. (orig.)

Novel MRI techniques in the assessment of dementia

International Nuclear Information System (INIS)

Teipel, Stefan J.; Meindl, Thomas; Grinberg, Lea; Heinsen, Helmut; Hampel, Harald

2008-01-01

Positive markers of Alzheimer's disease (AD) have been established in MRI that may allow early detection of AD in at-risk groups. In the near future, these markers will be of high relevance for the selection of at-risk subjects in secondary preventive trials. We describe the methodology and diagnostic value of manual volumetry of the hippocampus and entorhinal cortex, automated voxel-based morphometry, cortical thickness measurement, basal forebrain volumetry and deformation-based morphometry, implementing multivariate statistics and machine learning algorithms to improve group separation and prediction of AD in at-risk groups. We also describe the methodological basis and results obtained in AD using the recently developed technique of diffusion tensor-based morphometry (DTI). This technique gives access to the integrity of subcortical fibre systems in the human brain. The best established structural biomarker of AD to date is hippocampus volume that already has been implemented as secondary endpoint in clinical trials on disease modification in AD. Automated approaches will gain an increasing role as endpoints of clinical trials in the near future given the interest in these techniques expressed by the regulatory authorities. DTI is still a developing field where analysis techniques are presently being devised to make optimal use of the multivariate data. Data on changes of fibre tract in preclinical AD are still limited, but the first results are promising in respect to a further enhancement of diagnostic accuracy by combining MRI and DTI. Besides their diagnostic use, MRI and DTI will broaden our understanding of the pathophysiology of AD and the structural and functional basis of normal cognition. (orig.)

Development of the Triple Theta assembly station with machine vision feedback

International Nuclear Information System (INIS)

Schmidt, Derek William

2008-01-01

Increased requirements for tighter tolerances on assembled target components in complex three-dimensional geometries with only days to assemble complete campaigns require the implementation of a computer-controlled high-precision assembly station. Over the last year, an 11-axis computer-controlled assembly station has been designed and built with custom software to handle the multiple coordinate systems and automatically calculate all relational positions. Preliminary development efforts have also been done to explore the benefit of a machine vision feedback module with a dual-camera viewing system to automate certain basic features like crosshair calibration, component leveling, and component centering.

Research on self-calibration biaxial autocollimator based on ZYNQ

Science.gov (United States)

Guo, Pan; Liu, Bingguo; Liu, Guodong; Zhong, Yao; Lu, Binghui

2018-01-01

Autocollimators are mainly based on computers or the electronic devices that can be connected to the internet, and its precision, measurement range and resolution are all defective, and external displays are needed to display images in real time. What's more, there is no real-time calibration for autocollimator in the market. In this paper, we propose a biaxial autocollimator based on the ZYNQ embedded platform to solve the above problems. Firstly, the traditional optical system is improved and a light path is added for real-time calibration. Then, in order to improve measurement speed, the embedded platform based on ZYNQ that combines Linux operating system with autocollimator is designed. In this part, image acquisition, image processing, image display and the man-machine interaction interface based on Qt are achieved. Finally, the system realizes two-dimensional small angle measurement. Experimental results showed that the proposed method can improve the angle measurement accuracy. The standard deviation of the close distance (1.5m) is 0.15" in horizontal direction of image and 0.24"in vertical direction, the repeatability of measurement of the long distance (10m) is improved by 0.12 in horizontal direction of image and 0.3 in vertical direction.

Assessment of ameloblastomas using MRI and dynamic contrast-enhanced MRI

International Nuclear Information System (INIS)

Asaumi, Jun-ichi; Hisatomi, Miki; Yanagi, Yoshinobu; Matsuzaki, Hidenobu; Choi, Yong Suk; Kawai, Noriko; Konouchi, Hironobu; Kishi, Kanji

2005-01-01

We retrospectively evaluated magnetic resonance images (MRI) and dynamic contrast-enhanced MRI (DCE-MRI) of ameloblastomas. MRI and DCE-MRI were performed for 10 ameloblastomas. We obtained the following results from the MRI and DCE-MRI. (a) Ameloblastomas can be divided into solid and cystic portions on the basis of MR signal intensities. (b) Ameloblastomas show a predilection for intermediate signal intensity on T1WI, high signal intensity on T2WI, and well enhancement in the solid portion; they also show a homogeneous intermediate signal intensity on T1WI and homogeneous high signal intensity on T2WI, and no enhancement in the cystic portion. (c) The mural nodule or thick wall can be detected in ameloblastomas lesions. (d) CI curves of ameloblastomas show two patterns: the first pattern increases, reaches a plateau at 100-300 s, then sustains the plateau or decreases gradually to 600-900 s, while the other increases relatively rapidly, reaches a plateau at 90-120 s, then decreases relatively rapidly to 300 s, and decreases gradually thereafter. There was no difference in the CI curve patterns among primary and recurrent cases, a case with glandular odontogenic tumor in ameloblastoma or among histopathological types such as plexiform, follicular, mixed, desmoplastic, and unicystic type

Effect on Shear Strength of Machining Methods in Pinus nigra Arnold Bonded with Polyurethane and Polyvinyl Acetate Adhesives

Directory of Open Access Journals (Sweden)

Murat Kılıç

2016-06-01

Full Text Available Specimens taken from Pinus nigra Arnold were subject to surfacing techniques by being cut with a circular saw, planed with a thickness machine, and sanded with a calibrating sanding machine (with P80 grit sandpaper. First, their surface roughness values were measured; then, the specimens were processed in the machines in a radial and tangential process. Afterwards, the change in shear strength (adhesiveness resistance was analyzed as a result of bonding with various adhesive types (PVAc, PU and pressure applications (0.45 N/mm² or 0.9 N/mm². Approximately 600 specimens were prepared with the purpose of identifying the effect of variables on the bonding performance, and they were subjected to shear testing. The greatest shear strength achieved for both the tangential and radial surfaces in terms of cutting was observed in specimens processed in the thickness machine, on which polyvinyl acetate adhesive and 0.9 N/mm². pressure were applied. Specimens bonded with polyvinyl acetate adhesive displayed higher shear strength in general in comparison to those bonded with polyurethane for both tangential and radial surfaces.

Surface-Based fMRI-Driven Diffusion Tractography in the Presence of Significant Brain Pathology: A Study Linking Structure and Function in Cerebral Palsy

Science.gov (United States)

Cunnington, Ross; Boyd, Roslyn N.; Rose, Stephen E.

2016-01-01

Diffusion MRI (dMRI) tractography analyses are difficult to perform in the presence of brain pathology. Automated methods that rely on cortical parcellation for structural connectivity studies often fail, while manually defining regions is extremely time consuming and can introduce human error. Both methods also make assumptions about structure-function relationships that may not hold after cortical reorganisation. Seeding tractography with functional-MRI (fMRI) activation is an emerging method that reduces these confounds, but inherent smoothing of fMRI signal may result in the inclusion of irrelevant pathways. This paper describes a novel fMRI-seeded dMRI-analysis pipeline based on surface-meshes that reduces these issues and utilises machine-learning to generate task specific white matter pathways, minimising the requirement for manually-drawn ROIs. We directly compared this new strategy to a standard voxelwise fMRI-dMRI approach, by investigating correlations between clinical scores and dMRI metrics of thalamocortical and corticomotor tracts in 31 children with unilateral cerebral palsy. The surface-based approach successfully processed more participants (87%) than the voxel-based approach (65%), and provided significantly more-coherent tractography. Significant correlations between dMRI metrics and five clinical scores of function were found for the more superior regions of these tracts. These significant correlations were stronger and more frequently found with the surface-based method (15/20 investigated were significant; R2 = 0.43–0.73) than the voxelwise analysis (2 sig. correlations; 0.38 & 0.49). More restricted fMRI signal, better-constrained tractography, and the novel track-classification method all appeared to contribute toward these differences. PMID:27487011

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

Science.gov (United States)

Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

2008-01-01

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

Multidetector CT and MRI in diseases of the GI tract

International Nuclear Information System (INIS)

Bruel, J.M.; Gallix, B.

2003-01-01

With the introduction of spiral scanning then multidetector technologies, the accuracy for diagnosing digestive tract diseases with CT has been highly improved, and CT is used more and more in the evaluation of patients with suspected gastrointestinal disorders. CT is able to demonstrate both the intramural and the extra-mural components of the disease, and has a major role in the preoperative staging and the follow-up Improvements of CT protocols, such as CT-enteroclysis, or multiplanar 2D and 3D post-processing, including now techniques for 'virtual endoscopy', lead to discuss new indications in which CT could now compete with conventional X-rays series and video-endoscopy. This precise study of the digestive wall, the peri-digestive fat, the digestive tract blood supply, may be performed by MRI, under the condition of access to high level machines and standardized protocols. MR-enteroclysis and MR-virtual colonoscopy could be performed with much lower risk for the patient, in terms of radiation dose or contrast adverse effects. Endo-luminal coils should give to MR an ultra-high resolution for analysing the different layers of the gastrointestinal wall. Learning objectives: to review how to perform CT and MRI protocols for digestive tract imaging, to recognize the CT arid MR patterns of the main digestive tract diseases, to discuss the value, limits and role of CT and MR in digestive tract diseases, to discuss the potential role of CT and MR new technological developments for digestive tract imaging in the upcoming future Conclusion: CT is nowadays a modality of choice for digestive imaging. Improvements in technologies and indications, the necessary discussion of the risks and benefits for the patient should let the radiologists consider MRI in gastrointestinal disorders as an important part of the routine activity in clinical MRI. (authors)

Scanner calibration revisited

Directory of Open Access Journals (Sweden)

Pozhitkov Alexander E

2010-07-01

Full Text Available Abstract Background Calibration of a microarray scanner is critical for accurate interpretation of microarray results. Shi et al. (BMC Bioinformatics, 2005, 6, Art. No. S11 Suppl. 2. reported usage of a Full Moon BioSystems slide for calibration. Inspired by the Shi et al. work, we have calibrated microarray scanners in our previous research. We were puzzled however, that most of the signal intensities from a biological sample fell below the sensitivity threshold level determined by the calibration slide. This conundrum led us to re-investigate the quality of calibration provided by the Full Moon BioSystems slide as well as the accuracy of the analysis performed by Shi et al. Methods Signal intensities were recorded on three different microarray scanners at various photomultiplier gain levels using the same calibration slide from Full Moon BioSystems. Data analysis was conducted on raw signal intensities without normalization or transformation of any kind. Weighted least-squares method was used to fit the data. Results We found that initial analysis performed by Shi et al. did not take into account autofluorescence of the Full Moon BioSystems slide, which led to a grossly distorted microarray scanner response. Our analysis revealed that a power-law function, which is explicitly accounting for the slide autofluorescence, perfectly described a relationship between signal intensities and fluorophore quantities. Conclusions Microarray scanners respond in a much less distorted fashion than was reported by Shi et al. Full Moon BioSystems calibration slides are inadequate for performing calibration. We recommend against using these slides.

Simulations of Quantum Turing Machines by Quantum Multi-Stack Machines

OpenAIRE

Qiu, Daowen

2005-01-01

As was well known, in classical computation, Turing machines, circuits, multi-stack machines, and multi-counter machines are equivalent, that is, they can simulate each other in polynomial time. In quantum computation, Yao [11] first proved that for any quantum Turing machines $M$, there exists quantum Boolean circuit $(n,t)$-simulating $M$, where $n$ denotes the length of input strings, and $t$ is the number of move steps before machine stopping. However, the simulations of quantum Turing ma...

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

Science.gov (United States)

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

Multiparametric and molecular imaging of breast tumors with MRI and PET/MRI

International Nuclear Information System (INIS)

Pinker, K.; Marino, M.A.; Meyer-Baese, A.; Helbich, T.H.

2016-01-01

Magnetic resonance imaging (MRI) of the breast is an indispensable tool in breast imaging for many indications. Several functional parameters with MRI and positron emission tomography (PET) have been assessed for imaging of breast tumors and their combined application is defined as multiparametric imaging. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the hallmarks of cancer and may provide additional specificity. Multiparametric and molecular imaging of the breast comprises established MRI parameters, such as dynamic contrast-enhanced MRI, diffusion-weighted imaging (DWI), MR proton spectroscopy ( 1 H-MRSI) as well as combinations of radiological and MRI techniques (e.g. PET/CT and PET/MRI) using radiotracers, such as fluorodeoxyglucose (FDG). Multiparametric and molecular imaging of the breast can be performed at different field-strengths (range 1.5-7 T). Emerging parameters comprise novel promising techniques, such as sodium imaging ( 23 Na MRI), phosphorus spectroscopy ( 31 P-MRSI), chemical exchange saturation transfer (CEST) imaging, blood oxygen level-dependent (BOLD) and hyperpolarized MRI as well as various specific radiotracers. Multiparametric and molecular imaging has multiple applications in breast imaging. Multiparametric and molecular imaging of the breast is an evolving field that will enable improved detection, characterization, staging and monitoring for personalized medicine in breast cancer. (orig.) [de

Longitudinal MRI assessment: the identification of relevant features in the development of Posterior Fossa Syndrome in children

Science.gov (United States)

Spiteri, M.; Lewis, E.; Windridge, D.; Avula, S.

2015-03-01

Up to 25% of children who undergo brain tumour resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterised by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in lobes within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Intra-operative MRI (IoMRI) is used during surgical procedures at the Alder Hey Children's Hospital, Liver- pool, England, in the treatment of Posterior Fossa tumours and allows visualisation of the brain during surgery. The final MR scan on the IoMRI allows early assessment of the ION immediately after the surgical procedure. The longitudinal MRI data of 28 patients was analysed in a collaborative study with Alder Hey Children's Hospital, in order to identify the most relevant imaging features that relate to the development of PFS, specifically related to HOD. A semi-automated segmentation process was carried out to delineate the ION on each MRI. Feature selection techniques were used to identify the most relevant features amongst the MRI data, demographics and clinical data provided by the hospital. A support vector machine (SVM) was used to analyse the discriminative ability of the selected features. The results indicate the presence of HOD as the most efficient feature that correlates with the development of PFS, followed by the change in intensity and size of the ION and whether HOD occurred bilaterally or unilaterally.

Chest MRI

Science.gov (United States)

... resonance imaging - chest; NMR - chest; MRI of the thorax; Thoracic MRI Patient Instructions ... Gotway MB, Panse PM, Gruden JF, Elicker BM. Thoracic radiology. In: Broaddus VC, Mason RJ, Ernst JD, et ...

High-precision and low-cost vibration generator for low-frequency calibration system

Science.gov (United States)

Li, Rui-Jun; Lei, Ying-Jun; Zhang, Lian-Sheng; Chang, Zhen-Xin; Fan, Kuang-Chao; Cheng, Zhen-Ying; Hu, Peng-Hao

2018-03-01

Low-frequency vibration is one of the harmful factors that affect the accuracy of micro-/nano-measuring machines because its amplitude is significantly small and it is very difficult to avoid. In this paper, a low-cost and high-precision vibration generator was developed to calibrate an optical accelerometer, which is self-designed to detect low-frequency vibration. A piezoelectric actuator is used as vibration exciter, a leaf spring made of beryllium copper is used as an elastic component, and a high-resolution, low-thermal-drift eddy current sensor is applied to investigate the vibrator’s performance. Experimental results demonstrate that the vibration generator can achieve steady output displacement with frequency range from 0.6 Hz to 50 Hz, an analytical displacement resolution of 3.1 nm and an acceleration range from 3.72 mm s-2 to 1935.41 mm s-2 with a relative standard deviation less than 1.79%. The effectiveness of the high-precision and low-cost vibration generator was verified by calibrating our optical accelerometer.

CIRP Interlaboratory Comparison of Coordinate Measuring Machines using an Optomechanical Hole Plate - Final Report

DEFF Research Database (Denmark)

De Chiffre, Leonardo; Hansen, Hans Nørgaard; Morace, Renata Erica

2005-01-01

be expected that the optomechanical hole plates can be calibrated using the DKD procedure with an uncertainty in the range between 0.5 µm and 2 µm. Using the hole plate, it is possible to compare the performance of measurements obtained using optical and mechanical CMMs. Optical CMM measurements can...... be divided in two groups. A group leading to deviations larger than 2 µm, and a group with deviations that are comparable to those using mechanical machines. All but one laboratory could perform reversal measurements. Transfer of traceability was established as follows: 8 using gauge blocks, 2 laser...... interferometers, 1 zerodur hole plate, 2 callipers, and 1 quartz standard. Out of the 23 measurement campaigns, 5 optical and 2 mechanical machines were not provided with establishment of traceability. The optomechanical hole plate is a suitable reference artefact providing traceability of CMMs, in particular...

Machine tool structures

CERN Document Server

Koenigsberger, F

1970-01-01

Machine Tool Structures, Volume 1 deals with fundamental theories and calculation methods for machine tool structures. Experimental investigations into stiffness are discussed, along with the application of the results to the design of machine tool structures. Topics covered range from static and dynamic stiffness to chatter in metal cutting, stability in machine tools, and deformations of machine tool structures. This volume is divided into three sections and opens with a discussion on stiffness specifications and the effect of stiffness on the behavior of the machine under forced vibration c

MRI in psychiatry

International Nuclear Information System (INIS)

Mulert, Christoph; Shenton, Martha E.

2014-01-01

This is the first comprehensive textbook on the use of MRI in psychiatry covering imaging techniques, brain systems and a review of findings in different psychiatric disorders. The book is divided into three sections, the first of which covers in detail all the major MRI-based methodological approaches available today, including fMRI, EEG-fMRI, DTI, and MR spectroscopy. In addition, the role of MRI in imaging genetics and combined brain stimulation and imaging is carefully explained. The second section provides an overview of the different brain systems that are relevant for psychiatric disorders, including the systems for perception, emotion, cognition, and reward. The final part of the book presents the MRI findings that are obtained in all the major psychiatric disorders using the previously discussed techniques. Numerous carefully chosen images support the informative text, making this an ideal reference work for all practitioners and trainees with an interest in this flourishing field.

MRI in psychiatry

Energy Technology Data Exchange (ETDEWEB)

Mulert, Christoph [UKE, Hamburg (Germany). Psychiatry Neuroimaging Branch; Shenton, Martha E. (ed.) [Harvard Medical School, Boston, MA (United States). Dept. of Psychiatry and Radiology

2014-07-01

This is the first comprehensive textbook on the use of MRI in psychiatry covering imaging techniques, brain systems and a review of findings in different psychiatric disorders. The book is divided into three sections, the first of which covers in detail all the major MRI-based methodological approaches available today, including fMRI, EEG-fMRI, DTI, and MR spectroscopy. In addition, the role of MRI in imaging genetics and combined brain stimulation and imaging is carefully explained. The second section provides an overview of the different brain systems that are relevant for psychiatric disorders, including the systems for perception, emotion, cognition, and reward. The final part of the book presents the MRI findings that are obtained in all the major psychiatric disorders using the previously discussed techniques. Numerous carefully chosen images support the informative text, making this an ideal reference work for all practitioners and trainees with an interest in this flourishing field.

Larger Optics and Improved Calibration Techniques for Small Satellite Observations with the ERAU OSCOM System

Science.gov (United States)

Bilardi, S.; Barjatya, A.; Gasdia, F.

OSCOM, Optical tracking and Spectral characterization of CubeSats for Operational Missions, is a system capable of providing time-resolved satellite photometry using commercial-off-the-shelf (COTS) hardware and custom tracking and analysis software. This system has acquired photometry of objects as small as CubeSats using a Celestron 11” RASA and an inexpensive CMOS machine vision camera. For satellites with known shapes, these light curves can be used to verify a satellite’s attitude and the state of its deployed solar panels or antennae. While the OSCOM system can successfully track satellites and produce light curves, there is ongoing improvement towards increasing its automation while supporting additional mounts and telescopes. A newly acquired Celestron 14” Edge HD can be used with a Starizona Hyperstar to increase the SNR for small objects as well as extend beyond the limiting magnitude of the 11” RASA. OSCOM currently corrects instrumental brightness measurements for satellite range and observatory site average atmospheric extinction, but calibrated absolute brightness is required to determine information about satellites other than their spin rate, such as surface albedo. A calibration method that automatically detects and identifies background stars can use their catalog magnitudes to calibrate the brightness of the satellite in the image. We present a photometric light curve from both the 14” Edge HD and 11” RASA optical systems as well as plans for a calibration method that will perform background star photometry to efficiently determine calibrated satellite brightness in each frame.

Absolute radiometric calibration of Landsat using a pseudo invariant calibration site

Science.gov (United States)

Helder, D.; Thome, K.J.; Mishra, N.; Chander, G.; Xiong, Xiaoxiong; Angal, A.; Choi, Tae-young

2013-01-01

Pseudo invariant calibration sites (PICS) have been used for on-orbit radiometric trending of optical satellite systems for more than 15 years. This approach to vicarious calibration has demonstrated a high degree of reliability and repeatability at the level of 1-3% depending on the site, spectral channel, and imaging geometries. A variety of sensors have used this approach for trending because it is broadly applicable and easy to implement. Models to describe the surface reflectance properties, as well as the intervening atmosphere have also been developed to improve the precision of the method. However, one limiting factor of using PICS is that an absolute calibration capability has not yet been fully developed. Because of this, PICS are primarily limited to providing only long term trending information for individual sensors or cross-calibration opportunities between two sensors. This paper builds an argument that PICS can be used more extensively for absolute calibration. To illustrate this, a simple empirical model is developed for the well-known Libya 4 PICS based on observations by Terra MODIS and EO-1 Hyperion. The model is validated by comparing model predicted top-of-atmosphere reflectance values to actual measurements made by the Landsat ETM+ sensor reflective bands. Following this, an outline is presented to develop a more comprehensive and accurate PICS absolute calibration model that can be Système international d'unités (SI) traceable. These initial concepts suggest that absolute calibration using PICS is possible on a broad scale and can lead to improved on-orbit calibration capabilities for optical satellite sensors.

Development of a computer aided diagnosis model for prostate cancer classification on multi-parametric MRI

Science.gov (United States)

Alfano, R.; Soetemans, D.; Bauman, G. S.; Gibson, E.; Gaed, M.; Moussa, M.; Gomez, J. A.; Chin, J. L.; Pautler, S.; Ward, A. D.

2018-02-01

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score work will form the basis for a tool that enhances the radiologist's ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life.

Learning a common dictionary for subject-transfer decoding with resting calibration.

Science.gov (United States)

Morioka, Hiroshi; Kanemura, Atsunori; Hirayama, Jun-ichiro; Shikauchi, Manabu; Ogawa, Takeshi; Ikeda, Shigeyuki; Kawanabe, Motoaki; Ishii, Shin

2015-05-01

Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. Copyright © 2015 Elsevier Inc. All rights reserved.

[Fusion of MRI, fMRI and intraoperative MRI data. Methods and clinical significance exemplified by neurosurgical interventions].

Science.gov (United States)

Moche, M; Busse, H; Dannenberg, C; Schulz, T; Schmitgen, A; Trantakis, C; Winkler, D; Schmidt, F; Kahn, T

2001-11-01

The aim of this work was to realize and clinically evaluate an image fusion platform for the integration of preoperative MRI and fMRI data into the intraoperative images of an interventional MRI system with a focus on neurosurgical procedures. A vertically open 0.5 T MRI scanner was equipped with a dedicated navigation system enabling the registration of additional imaging modalities (MRI, fMRI, CT) with the intraoperatively acquired data sets. These merged image data served as the basis for interventional planning and multimodal navigation. So far, the system has been used in 70 neurosurgical interventions (13 of which involved image data fusion--requiring 15 minutes extra time). The augmented navigation system is characterized by a higher frame rate and a higher image quality as compared to the system-integrated navigation based on continuously acquired (near) real time images. Patient movement and tissue shifts can be immediately detected by monitoring the morphological differences between both navigation scenes. The multimodal image fusion allowed a refined navigation planning especially for the resection of deeply seated brain lesions or pathologies close to eloquent areas. Augmented intraoperative orientation and instrument guidance improve the safety and accuracy of neurosurgical interventions.

Diagnostic value of MRI in meniscus injury. Comparison of MRI and arthrography

International Nuclear Information System (INIS)

Iso, Yoshinori; Nozaki, Hiroyuki; Emoto, Mari; Miyairi, Taro; Hirata, Aya; Hirasawa, Seiichi; Suguro, Toru; Igata, Atsuomi; Kudo, Yukihiko.

1995-01-01

Magnetic resonance imaging (MRI) and arthrography were performed on 90 knees to compare the diagnostic value for meniscus injury with these techniques. The diagnostic accuracy of MRI and arthrography was 89.1% and 87.1%, respectively. Imaging of the medial meniscus was somewhat better with arthrography, and delineation of the lateral meniscus was somewhat better with MRI. MRI was superior in diagnoses of horizontal and degenerative lacerations, but showed the shape of the injuries less clearly than arthrography. The diagnostic accuracy of MRI decreased with the age of the patients and was inferior to arthrography for patients in their forties or older. In conclusion, MRI is a less invasive approach with high diagnostic accuracy for meniscus injury and is a promising substitute for arthrography. (author)

Electricity of machine tool

International Nuclear Information System (INIS)

Gijeon media editorial department

1977-10-01

This book is divided into three parts. The first part deals with electricity machine, which can taints from generator to motor, motor a power source of machine tool, electricity machine for machine tool such as switch in main circuit, automatic machine, a knife switch and pushing button, snap switch, protection device, timer, solenoid, and rectifier. The second part handles wiring diagram. This concludes basic electricity circuit of machine tool, electricity wiring diagram in your machine like milling machine, planer and grinding machine. The third part introduces fault diagnosis of machine, which gives the practical solution according to fault diagnosis and the diagnostic method with voltage and resistance measurement by tester.

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

Energy Technology Data Exchange (ETDEWEB)

Wang, Jing [CFDA, Center for Medical Device Evaluation, Beijing (China); Wu, Chen-Jiang; Zhang, Jing; Wang, Xiao-Ning; Zhang, Yu-Dong [First Affiliated Hospital with Nanjing Medical University, Department of Radiology, Nanjing, Jiangsu Province (China); Bao, Mei-Ling [First Affiliated Hospital with Nanjing Medical University, Department of Pathology, Nanjing (China)

2017-10-15

To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. (orig.)

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

International Nuclear Information System (INIS)

Wang, Jing; Wu, Chen-Jiang; Zhang, Jing; Wang, Xiao-Ning; Zhang, Yu-Dong; Bao, Mei-Ling

2017-01-01

To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. (orig.)

First Calibrations of Alanine and Radio-Photo-Luminescence Dosemeters to a Hadronic Radiation Environment

CERN Document Server

Fürstner, Markus; Floret, Idelette; Forkel-Wirth, Doris; Mayer, Sabine; Menzel, Hans Gregor; Vincke, Helmut H

2005-01-01

Alanine and Radio-Photo-Luminescence (RPL) dosimeters are used to monitor radiation doses occurring inside the tunnels of all CERN accelerators including the Large Hadron Collider (LHC). They are placed close to radiation sensitive machine components like cables or insulation of magnet coils to predict their remaining lifetime. The dosimeters are exposed to mixed high-energy radiation fields. However, up to now both dosimeter types are calibrated to 60Co-photons only. In order to study the response of RPL and alanine dosimeters to mixed particle fields like those occurring at CERN's accelerators, an irradiation campaign at the CERN-EC High-Energy Reference field Facility (CERF-field) was performed. Moreover, the dosimeters were first time calibrated to a proton radiation field of a constant momentum of 24 GeV/c. In addition to the experiment FLUKA Monte Carlo simulations were carried out, which provide information concerning the energy deposition and the radiation field at the dosimeter locations.

Integrated navigation and control software system for MRI-guided robotic prostate interventions.

Science.gov (United States)

Tokuda, Junichi; Fischer, Gregory S; DiMaio, Simon P; Gobbi, David G; Csoma, Csaba; Mewes, Philip W; Fichtinger, Gabor; Tempany, Clare M; Hata, Nobuhiko

2010-01-01

A software system to provide intuitive navigation for MRI-guided robotic transperineal prostate therapy is presented. In the system, the robot control unit, the MRI scanner, and the open-source navigation software are connected together via Ethernet to exchange commands, coordinates, and images using an open network communication protocol, OpenIGTLink. The system has six states called "workphases" that provide the necessary synchronization of all components during each stage of the clinical workflow, and the user interface guides the operator linearly through these workphases. On top of this framework, the software provides the following features for needle guidance: interactive target planning; 3D image visualization with current needle position; treatment monitoring through real-time MR images of needle trajectories in the prostate. These features are supported by calibration of robot and image coordinates by fiducial-based registration. Performance tests show that the registration error of the system was 2.6mm within the prostate volume. Registered real-time 2D images were displayed 1.97 s after the image location is specified. Copyright 2009 Elsevier Ltd. All rights reserved.

Integrated navigation and control software system for MRI-guided robotic prostate interventions

Science.gov (United States)

Tokuda, Junichi; Fischer, Gregory S.; DiMaio, Simon P.; Gobbi, David G.; Csoma, Csaba; Mewes, Philip W.; Fichtinger, Gabor; Tempany, Clare M.; Hata, Nobuhiko

2010-01-01

A software system to provide intuitive navigation for MRI-guided robotic transperineal prostate therapy is presented. In the system, the robot control unit, the MRI scanner, and the open-source navigation software are connected together via Ethernet to exchange commands, coordinates, and images using an open network communication protocol, OpenIGTLink. The system has six states called “workphases” that provide the necessary synchronization of all components during each stage of the clinical workflow, and the user interface guides the operator linearly through these workphases. On top of this framework, the software provides the following features for needle guidance: interactive target planning; 3D image visualization with current needle position; treatment monitoring through real-time MR images of needle trajectories in the prostate. These features are supported by calibration of robot and image coordinates by fiducial-based registration. Performance tests show that the registration error of the system was 2.6 mm within the prostate volume. Registered real-time 2D images were displayed 1.97 s after the image location is specified. PMID:19699057

Machine medical ethics

CERN Document Server

Pontier, Matthijs

2015-01-01

The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

Humanizing machines: Anthropomorphization of slot machines increases gambling.

Science.gov (United States)

Riva, Paolo; Sacchi, Simona; Brambilla, Marco

2015-12-01

Do people gamble more on slot machines if they think that they are playing against humanlike minds rather than mathematical algorithms? Research has shown that people have a strong cognitive tendency to imbue humanlike mental states to nonhuman entities (i.e., anthropomorphism). The present research tested whether anthropomorphizing slot machines would increase gambling. Four studies manipulated slot machine anthropomorphization and found that exposing people to an anthropomorphized description of a slot machine increased gambling behavior and reduced gambling outcomes. Such findings emerged using tasks that focused on gambling behavior (Studies 1 to 3) as well as in experimental paradigms that included gambling outcomes (Studies 2 to 4). We found that gambling outcomes decrease because participants primed with the anthropomorphic slot machine gambled more (Study 4). Furthermore, we found that high-arousal positive emotions (e.g., feeling excited) played a role in the effect of anthropomorphism on gambling behavior (Studies 3 and 4). Our research indicates that the psychological process of gambling-machine anthropomorphism can be advantageous for the gaming industry; however, this may come at great expense for gamblers' (and their families') economic resources and psychological well-being. (c) 2015 APA, all rights reserved).

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

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Agner, Shannon C; Soman, Salil; Libfeld, Edward; McDonald, Margie; Thomas, Kathleen; Englander, Sarah; Rosen, Mark A; Chin, Deanna; Nosher, John; Madabhushi, Anant

2011-06-01

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

MRI EVALUATION OF PAINFUL KNEE: A STUDY AT KATURI TERTIARY REFERRAL CENTER

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Bhimeswarao Pasupuleti

2015-02-01

Full Text Available INTRODUCTION: Normal knee joint functional activity is essential for day to day life . The number of patients with complaints of painful knee joint is quite significant and therefore magnetic resonance imaging (MRI of the knee is of great value in understanding and to diagnose the varied pathologies causing painful knee joint. The information obtained from conventional radiographs of the knee is limited, and by CT scans is limited to bone pathog l y with limited information about ligaments and synovium. AIMS AND OBJECTIVES : a To describe the MRI features in various types of traumatic and non - traumatic knee pain . b To identify the common lesions seen in the knee joint . METHODOLOGY : The study population included 100 patients who underwent MR imaging of the knee who presented with knee pain to the DEPARTMENT OF RADIOLOGY, KATURI MEDICALCOLLEGE referred by the clinician. STUDY PERIOD: Nov 2010 to Oct 2012 . STUDY DESIGN : Descriptive study . All the MRI scans of the knee in this study were performed using GE Signa Profile EXCITE MR machine with a 0.2 tesla field strength magnet in a closely coupled extremity coil. RESULTS AND DISCUSSION : The pathology of knee joint is broadly classified as traumatic and non - traumatic. Traumatic pathol o gy mainly included the ligament injuries and non - traumatic included arthritis, cysts and neoplastic lesions

Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease

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Elaheh Moradi

2017-01-01

Full Text Available Rey's Auditory Verbal Learning Test (RAVLT is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in RAVLT scores reflect well the underlying pathology caused by Alzheimer's disease (AD, thus making RAVLT an effective early marker to detect AD in persons with memory complaints. We investigated the association between RAVLT scores (RAVLT Immediate and RAVLT Percent Forgetting and the structural brain atrophy caused by AD. The aim was to comprehensively study to what extent the RAVLT scores are predictable based on structural magnetic resonance imaging (MRI data using machine learning approaches as well as to find the most important brain regions for the estimation of RAVLT scores. For this, we built a predictive model to estimate RAVLT scores from gray matter density via elastic net penalized linear regression model. The proposed approach provided highly significant cross-validated correlation between the estimated and observed RAVLT Immediate (R = 0.50 and RAVLT Percent Forgetting (R = 0.43 in a dataset consisting of 806 AD, mild cognitive impairment (MCI or healthy subjects. In addition, the selected machine learning method provided more accurate estimates of RAVLT scores than the relevance vector regression used earlier for the estimation of RAVLT based on MRI data. The top predictors were medial temporal lobe structures and amygdala for the estimation of RAVLT Immediate and angular gyrus, hippocampus and amygdala for the estimation of RAVLT Percent Forgetting. Further, the conversion of MCI subjects to AD in 3-years could be predicted based on either observed or estimated RAVLT scores with an accuracy comparable to MRI-based biomarkers.

Comparison of MRI-based and CT/MRI fusion-based postimplant dosimetric analysis of prostate brachytherapy

International Nuclear Information System (INIS)

Tanaka, Osamu; Hayashi, Shinya; Matsuo, Masayuki; Sakurai, Kota; Nakano, Masahiro; Maeda, Sunaho; Kajita, Kimihiro R.T.; Deguchi, Takashi; Hoshi, Hiroaki

2006-01-01

Purpose: The aim of this study was to compare the outcomes between magnetic resonance imaging (MRI)-based and computed tomography (CT)/MRI fusion-based postimplant dosimetry methods in permanent prostate brachytherapy. Methods and Materials: Between October 2004 and March 2006, a total of 52 consecutive patients with prostate cancer were treated by brachytherapy, and postimplant dosimetry was performed using CT/MRI fusion. The accuracy and reproducibility were prospectively compared between MRI-based dosimetry and CT/MRI fusion-based dosimetry based on the dose-volume histogram (DVH) related parameters as recommended by the American Brachytherapy Society. Results: The prostate volume was 15.97 ± 6.17 cc (mean ± SD) in MRI-based dosimetry, and 15.97 ± 6.02 cc in CT/MRI fusion-based dosimetry without statistical difference. The prostate V100 was 94.5% and 93.0% in MRI-based and CT/MRI fusion-based dosimetry, respectively, and the difference was statistically significant (p = 0.002). The prostate D90 was 119.4% and 114.4% in MRI-based and CT/MRI fusion-based dosimetry, respectively, and the difference was statistically significant (p = 0.004). Conclusion: Our current results suggested that, as with fusion images, MR images allowed accurate contouring of the organs, but they tended to overestimate the analysis of postimplant dosimetry in comparison to CT/MRI fusion images. Although this MRI-based dosimetric discrepancy was negligible, MRI-based dosimetry was acceptable and reproducible in comparison to CT-based dosimetry, because the difference between MRI-based and CT/MRI fusion-based results was smaller than that between CT-based and CT/MRI fusion-based results as previously reported

Diffusion, confusion and functional MRI

International Nuclear Information System (INIS)

Le Bihan, Denis

2012-01-01

Diffusion MRI has been introduced in 1985 and has had a very successful life on its own. While it has become a standard for imaging stroke and white matter disorders, the borders between diffusion MRI and the general field of fMRI have always remained fuzzy. First, diffusion MRI has been used to obtain images of brain function, based on the idea that diffusion MRI could also be made sensitive to blood flow, through the intra-voxel incoherent motion (IVIM) concept. Second, the IVIM concept helped better understand the contribution from different vasculature components to the BOLD fMRI signal. Third, it has been shown recently that a genuine fMRI signal can be obtained with diffusion MRI. This 'DfMRI' signal is notably different from the BOLD fMRI signal, especially for its much faster response to brain activation both at onset and offset, which points out to structural changes in the neural tissues, perhaps such as cell swelling, occurring in activated neural tissue. This short article reviews the major steps which have paved the way for this exciting development, underlying how technical progress with MRI equipment has each time been instrumental to expand the horizon of diffusion MRI toward the field of fMRI. (authors)

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

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Muhammad Faisal Siddiqui

Full Text Available A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT, principal component analysis (PCA, and least squares support vector machine (LS-SVM are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%. Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

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Akhbardeh, Alireza; Jacobs, Michael A. [Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States); Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States) and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 (United States)

2012-04-15

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

International Nuclear Information System (INIS)

Akhbardeh, Alireza; Jacobs, Michael A.

2012-01-01

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B 1 inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both

Code-expanded radio access protocol for machine-to-machine communications

DEFF Research Database (Denmark)

Thomsen, Henning; Kiilerich Pratas, Nuno; Stefanovic, Cedomir

2013-01-01

The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated b...... subframes and orthogonal preambles, the amount of available contention resources is drastically increased, enabling the massive support of Machine-Type Communication users that is beyond the reach of current systems.......The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated...... by the random access method employed in LTE, which significantly increases the amount of contention resources without increasing the system resources, such as contention subframes and preambles. This is accomplished by a logical, rather than physical, extension of the access method in which the available system...

MRI Customized Play Therapy in Children Reduces the Need for Sedation--A Randomized Controlled Trial.

Science.gov (United States)

Bharti, Bhavneet; Malhi, Prahbhjot; Khandelwal, N

2016-03-01

To evaluate the effectiveness of an MRI-specific play therapy intervention on the need for sedation in young children. All children in the age group of 4-10 y, who were advised an MRI scan over a period of one year were randomized. Exclusion criteria included children with neurodevelopmental disorders impairing cognition and children who had previously undergone diagnostic MRI. A total of 79 children were randomized to a control or an intervention condition. The intervention involved familiarizing the child with the MRI model machine, listing the steps involved in the scan to the child in vivid detail, training the child to stand still for 5 min, and conducting several dry runs with a doll or a favorite toy. The study was approved by the Institute ethical committee. The need for sedation was 41 % (n = 16) in the control group and this declined to 20 % (n = 8) in the intervention group (χ(2) = 4.13; P = 0.04). The relative risk of sedation decreased by 49 % in the intervention group as compared to the control group (RR 0.49; 95 % CI: 0.24-1.01) and this difference was statistically significant (P = 0.04). The absolute risk difference in sedation use between intervention and control group was 21 % (95 % CI 1.3 %-40.8 %). Even on adjusting for age, relative risk of sedation remained significantly lower in children undergoing play therapy as compared to the control (RR 0.57, 95 % CI: 0.32-0.98) with P value of 0.04. The use of an MRI customized play therapy with pediatric patients undergoing diagnostic MRI resulted in significant reduction of the use of sedation.

“Calibration-on-the-spot”: How to calibrate an EMCCD camera from its images

DEFF Research Database (Denmark)

Mortensen, Kim; Flyvbjerg, Henrik

2016-01-01

In order to count photons with a camera, the camera must be calibrated. Photon counting is necessary, e.g., to determine the precision of localization-based super-resolution microscopy. Here we present a protocol that calibrates an EMCCD camera from information contained in isolated, diffraction-......-limited spots in any image taken by the camera, thus making dedicated calibration procedures redundant by enabling calibration post festum, from images filed without calibration information....

ADHD-200 Global Competition: Diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements

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Matthew R G Brown

2012-09-01

Full Text Available Neuroimaging-based diagnostics could potentially assist clinicians to make more accurate diagnoses resulting in faster, more effective treatment. We participated in the 2011 ADHD-200 Global Competition which involved analyzing a large dataset of 973 participants including ADHD patients and healthy controls. Each participant's data included a resting state functional magnetic resonance imaging (fMRI scan as well as personal characteristic and diagnostic data. The goal was to learn a machine learning classifier that used a participant's resting state fMRI scan to diagnose (classify that individual into one of three categories: healthy control, ADHD combined type, or ADHD inattentive type. We used participants' personal characteristic data (site of data collection, age, gender, handedness, performance IQ, verbal IQ, and full scale IQ, without any fMRI data, as input to a logistic classifier to generate diagnostic predictions. Surprisingly, this approach achieved the highest diagnostic accuracy (62.52% as well as the highest score (124 of 195 of any of the 21 teams participating in the competition. These results demonstrate the importance of accounting for differences in age, gender, and other personal characteristics in imaging diagnostics research. We discuss further implications of these results for fMRI-based diagnosis as well as fMRI-based clinical research. We also document our tests with a variety of imaging-based diagnostic methods, none of which performed as well as the logistic classifier using only personal characteristic data.

Calibration of reference KAP-meters at SSDL and cross calibration of clinical KAP-meters

International Nuclear Information System (INIS)

Hetland, Per O.; Friberg, Eva G.; Oevreboe, Kirsti M.; Bjerke, Hans H.

2009-01-01

In the summer of 2007 the secondary standard dosimetry laboratory (SSDL) in Norway established a calibration service for reference air-kerma product meter (KAP-meter). The air-kerma area product, PKA, is a dosimetric quantity that can be directly related to the patient dose and used for risk assessment associated with different x-ray examinations. The calibration of reference KAP-meters at the SSDL gives important information on parameters influencing the calibration factor for different types of KAP-meters. The use of reference KAP-meters calibrated at the SSDL is an easy and reliable way to calibrate or verify the PKA indicated by the x-ray equipment out in the clinics. Material and methods. Twelve KAP-meters were calibrated at the SSDL by use of the substitution method at five diagnostic radiation qualities (RQRs). Results. The calibration factors varied from 0.94 to 1.18. The energy response of the individual KAP-meters varied by a total of 20% between the different RQRs and the typical chamber transmission factors ranged from 0.78 to 0.91. Discussion. It is important to use a calibrated reference KAP-meter and a harmonised calibration method in the PKA calibration in hospitals. The obtained uncertainty in the PKA readings is comparable with other calibration methods if the information in the calibration certificate is correct used, corrections are made and proper positioning of the KAP-chamber is performed. This will ensure a reliable estimate of the patient dose and a proper optimisation of conventional x-ray examinations and interventional procedures

Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

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R. Rajesh Sharma

2015-01-01

algorithm (RGSA. Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002. The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

The application of automatic chemiluminescence machine in rapid immune detection

International Nuclear Information System (INIS)

Lin Aizhen; Li Xuanwei; Chen Binhong; Li Zhenqian; Chen Zhaoxuan

2004-01-01

Objective: To provide high-quality, rapid and dependable result for clinical practice, and give satisfactory service to patients of different economical status by supplementation with other labeling immune examination. With an innovative attitude, we carried out efficient technical reform on ACS180 automatic chemiluminescence machine, making it possible for patients to complete the whole process including examination, check-out, diagnosis and getting drugs. The reported will be issued within an hour, thus a rapid immune detection service was established in out-patients department. Methods: 1. ACS-180 automatic chemiluminescence machine is used based on the principle of chemiluminescence immune methods. 2. The reagents are provided by Ciba-Comig Company of USA, composed of anti acridinium ester antibody of liquid phase and particulate antigen of solid phase wrapped in magnetic powder. 3. Calibration and quality control: high and low concentration are set for each calibration fluid with attached standard curve. Product for quality controlling includes three concentration of low, moderate and high. Results: 1. rapid machine detection for sample: serum is replaced with plasma coagulated by heparin, and comparison among series of methods using serum or plasma suggest no significant difference exists. 2. The problem about fasting detection: chemiluminescence machine measure optical density directly, with the results hardly being influenced by turbidity. But attention should be paid to the treatment of lipid turbid samples. 3. Other innovations: (1) direct placement of sample tube on machine: a cushion is placed on sample plate to transfer sample to machine directly after centrifugation, saving time and reducing the accident in sample transference. (2) for HCG quantification in blood and urine, 'gold criteria' is used firstly in screening to determine approximately the dilution range, with an advantage of saving time and reagent as well as accuracy. (3) we design a

IClinfMRI Software for Integrating Functional MRI Techniques in Presurgical Mapping and Clinical Studies.