Iterative feature refinement for accurate undersampled MR image reconstruction
Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong
2016-05-01
Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.
Accurate reconstruction of hyperspectral images from compressive sensing measurements
Greer, John B.; Flake, J. C.
2013-05-01
The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.
Jørgensen, Jakob H; Pan, Xiaochuan
2011-01-01
Breast X-ray CT imaging is being considered in screening as an extension to mammography. As a large fraction of the population will be exposed to radiation, low-dose imaging is essential. Iterative image reconstruction based on solving an optimization problem, such as Total-Variation minimization, shows potential for reconstruction from sparse-view data. For iterative methods it is important to ensure convergence to an accurate solution, since important image features, such as presence of microcalcifications indicating breast cancer, may not be visible in a non-converged reconstruction, and this can have clinical significance. To prevent excessively long computational times, which is a practical concern for the large image arrays in CT, it is desirable to keep the number of iterations low, while still ensuring a sufficiently accurate reconstruction for the specific imaging task. This motivates the study of accurate convergence criteria for iterative image reconstruction. In simulation studies with a realistic...
Use of a ray-based reconstruction algorithm to accurately quantify preclinical microSPECT images
Vandeghinste, Bert; Van Holen, Roel; Vanhove, Christian; De Vos, Filip; Vandenberghe, Stefaan; Staelens, Steven
2014-01-01
This work aimed to measure the in vivo quantification errors obtained when ray-based iterative reconstruction is used in micro-singlephoton emission computed tomography (SPECT). This was investigated with an extensive phantom-based evaluation and two typical in vivo studies using (99m) Tc and In-111, measured on a commercially available cadmium zinc telluride (CZT)-based small-animal scanner. Iterative reconstruction was implemented on the GPU using ray tracing, including (1) scatter correcti...
Use of a ray-based reconstruction algorithm to accurately quantify preclinical microSPECT images.
Vandeghinste, Bert; Van Holen, Roel; Vanhove, Christian; De Vos, Filip; Vandenberghe, Stefaan; Staelens, Steven
2014-01-01
This work aimed to measure the in vivo quantification errors obtained when ray-based iterative reconstruction is used in micro-single-photon emission computed tomography (SPECT). This was investigated with an extensive phantom-based evaluation and two typical in vivo studies using 99mTc and 111In, measured on a commercially available cadmium zinc telluride (CZT)-based small-animal scanner. Iterative reconstruction was implemented on the GPU using ray tracing, including (1) scatter correction, (2) computed tomography-based attenuation correction, (3) resolution recovery, and (4) edge-preserving smoothing. It was validated using a National Electrical Manufacturers Association (NEMA) phantom. The in vivo quantification error was determined for two radiotracers: [99mTc]DMSA in naive mice (n = 10 kidneys) and [111In]octreotide in mice (n = 6) inoculated with a xenograft neuroendocrine tumor (NCI-H727). The measured energy resolution is 5.3% for 140.51 keV (99mTc), 4.8% for 171.30 keV, and 3.3% for 245.39 keV (111In). For 99mTc, an uncorrected quantification error of 28 ± 3% is reduced to 8 ± 3%. For 111In, the error reduces from 26 ± 14% to 6 ± 22%. The in vivo error obtained with 99mTc-dimercaptosuccinic acid ([99mTc]DMSA) is reduced from 16.2 ± 2.8% to -0.3 ± 2.1% and from 16.7 ± 10.1% to 2.2 ± 10.6% with [111In]octreotide. Absolute quantitative in vivo SPECT is possible without explicit system matrix measurements. An absolute in vivo quantification error smaller than 5% was achieved and exemplified for both [99mTc]DMSA and [111In]octreotide. PMID:24824961
Hui-Hui, Xia; Rui-Feng, Kan; Jian-Guo, Liu; Zhen-Yu, Xu; Ya-Bai, He
2016-06-01
An improved algebraic reconstruction technique (ART) combined with tunable diode laser absorption spectroscopy(TDLAS) is presented in this paper for determining two-dimensional (2D) distribution of H2O concentration and temperature in a simulated combustion flame. This work aims to simulate the reconstruction of spectroscopic measurements by a multi-view parallel-beam scanning geometry and analyze the effects of projection rays on reconstruction accuracy. It finally proves that reconstruction quality dramatically increases with the number of projection rays increasing until more than 180 for 20 × 20 grid, and after that point, the number of projection rays has little influence on reconstruction accuracy. It is clear that the temperature reconstruction results are more accurate than the water vapor concentration obtained by the traditional concentration calculation method. In the present study an innovative way to reduce the error of concentration reconstruction and improve the reconstruction quality greatly is also proposed, and the capability of this new method is evaluated by using appropriate assessment parameters. By using this new approach, not only the concentration reconstruction accuracy is greatly improved, but also a suitable parallel-beam arrangement is put forward for high reconstruction accuracy and simplicity of experimental validation. Finally, a bimodal structure of the combustion region is assumed to demonstrate the robustness and universality of the proposed method. Numerical investigation indicates that the proposed TDLAS tomographic algorithm is capable of detecting accurate temperature and concentration profiles. This feasible formula for reconstruction research is expected to resolve several key issues in practical combustion devices. Project supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61205151), the National Key Scientific Instrument and Equipment Development Project of China (Grant
Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT
Sidky, Emil Y; Pan, Xiaochuan
2009-01-01
In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image reconstruction from few views would enable rapid scanning with a reduced x-ray dose delivered to the patient. Limited-angle problems are also of practical significance in CT. In this work, we develop and investigate an iterative image reconstruction algorithm based on the minimization of the image total variation (TV) that applies to divergent-beam CT. Numerical demonstrations of our TV algorithm are performed with various insufficient data problems in fan-beam CT. The TV algorithm can be generalized to cone-beam CT as well as other tomographic imaging modalities.
Image Reconstruction. Chapter 13
This chapter discusses how 2‑D or 3‑D images of tracer distribution can be reconstructed from a series of so-called projection images acquired with a gamma camera or a positron emission tomography (PET) system [13.1]. This is often called an ‘inverse problem’. The reconstruction is the inverse of the acquisition. The reconstruction is called an inverse problem because making software to compute the true tracer distribution from the acquired data turns out to be more difficult than the ‘forward’ direction, i.e. making software to simulate the acquisition. There are basically two approaches to image reconstruction: analytical reconstruction and iterative reconstruction. The analytical approach is based on mathematical inversion, yielding efficient, non-iterative reconstruction algorithms. In the iterative approach, the reconstruction problem is reduced to computing a finite number of image values from a finite number of measurements. That simplification enables the use of iterative instead of mathematical inversion. Iterative inversion tends to require more computer power, but it can cope with more complex (and hopefully more accurate) models of the acquisition process
Accurate modeling of the photon acquisition process in pinhole SPECT is essential for optimizing resolution. In this work, the authors develop an accurate system model in which pinhole finite aperture and depth-dependent geometric sensitivity are explicitly included. To achieve high-resolution pinhole SPECT, the voxel size is usually set in the range of sub-millimeter so that the total number of image voxels increase accordingly. It is inevitably that a system matrix that models a variety of favorable physical factors will become extremely sophisticated. An efficient implementation for such an accurate system model is proposed in this research. We first use the geometric symmetries to reduce redundant entries in the matrix. Due to the sparseness of the matrix, only non-zero terms are stored. A novel center-to-radius recording rule is also developed to effectively describe the relation between a voxel and its related detectors at every projection angle. The proposed system matrix is also suitable for multi-threaded computing. Finally, the accuracy and effectiveness of the proposed system model is evaluated in a workstation equipped with two Quad-Core Intel X eon processors.
The conversion from polar to Cartesian coordinates can be carried out with two-pass algorithms. The paper describes two different methods based on concentric square frames and octagonal frames and their results, obtained with accurate interpolations based on the 'moving window Shannon reconstruction' (MWSR). The embedding of these algorithms in direct Fourier methods (DFMs) of tomographic reconstruction is discussed. With respect to ne-pass methods and to the use of octagonal frames, the square frame method makes it possible to carry out the first pass, a radial resampling, in the direct space, before computing 1D Fourier transforms (FTs) of projections. Reconstructions of clinical images from the raw data of a third-generation x-ray tomograph are presented and compared with those obtained with one-pass FMs and with the convolution back-projection method (CBPM) performed by the instrument. The simple algorithm using square frames yields results in complete agreement with other DFM protocols and the CBPM. On a general-purpose computer, the execution of DFM protocols based on one-pass and two-pass coordinate transformations is 35 to 55 times faster than the BPM and make the algorithms attractive for modern instrumentation. (author)
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Daftry, Shreyansh; Hoppe, Christof; Bischof, Horst
2015-01-01
Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools ubiquitous for large number of Architecture, Engineering and Construction applications among audiences, mostly unskilled in computer vision. However, to obtain high-resolution and accurate reconstruc...
Overview of Image Reconstruction
Marr, R. B.
1980-04-01
Image reconstruction (or computerized tomography, etc.) is any process whereby a function, f, on R^{n} is estimated from empirical data pertaining to its integrals, ∫f(x) dx, for some collection of hyperplanes of dimension k < n. The paper begins with background information on how image reconstruction problems have arisen in practice, and describes some of the application areas of past or current interest; these include radioastronomy, optics, radiology and nuclear medicine, electron microscopy, acoustical imaging, geophysical tomography, nondestructive testing, and NMR zeugmatography. Then the various reconstruction algorithms are discussed in five classes: summation, or simple back-projection; convolution, or filtered back-projection; Fourier and other functional transforms; orthogonal function series expansion; and iterative methods. Certain more technical mathematical aspects of image reconstruction are considered from the standpoint of uniqueness, consistency, and stability of solution. The paper concludes by presenting certain open problems. 73 references. (RWR)
Overview of image reconstruction
Image reconstruction (or computerized tomography, etc.) is any process whereby a function, f, on R/sup n/ is estimated from empirical data pertaining to its integrals, ∫f(x) dx, for some collection of hyperplanes of dimension k < n. The paper begins with background information on how image reconstruction problems have arisen in practice, and describes some of the application areas of past or current interest; these include radioastronomy, optics, radiology and nuclear medicine, electron microscopy, acoustical imaging, geophysical tomography, nondestructive testing, and NMR zeugmatography. Then the various reconstruction algorithms are discussed in five classes: summation, or simple back-projection; convolution, or filtered back-projection; Fourier and other functional transforms; orthogonal function series expansion; and iterative methods. Certain more technical mathematical aspects of image reconstruction are considered from the standpoint of uniqueness, consistency, and stability of solution. The paper concludes by presenting certain open problems. 73 references
LOFAR sparse image reconstruction
Garsden, H.; Girard, J. N.; Starck, J. L.; Corbel, S.; Tasse, C.; Woiselle, A.; McKean, J. P.; van Amesfoort, A. S.; Anderson, J.; Avruch, I. M.; Beck, R.; Bentum, M. J.; Best, P.; Breitling, F.; Broderick, J.; Brüggen, M.; Butcher, H. R.; Ciardi, B.; de Gasperin, F.; de Geus, E.; de Vos, M.; Duscha, S.; Eislöffel, J.; Engels, D.; Falcke, H.; Fallows, R. A.; Fender, R.; Ferrari, C.; Frieswijk, W.; Garrett, M. A.; Grießmeier, J.; Gunst, A. W.; Hassall, T. E.; Heald, G.; Hoeft, M.; Hörandel, J.; van der Horst, A.; Juette, E.; Karastergiou, A.; Kondratiev, V. I.; Kramer, M.; Kuniyoshi, M.; Kuper, G.; Mann, G.; Markoff, S.; McFadden, R.; McKay-Bukowski, D.; Mulcahy, D. D.; Munk, H.; Norden, M. J.; Orru, E.; Paas, H.; Pandey-Pommier, M.; Pandey, V. N.; Pietka, G.; Pizzo, R.; Polatidis, A. G.; Renting, A.; Röttgering, H.; Rowlinson, A.; Schwarz, D.; Sluman, J.; Smirnov, O.; Stappers, B. W.; Steinmetz, M.; Stewart, A.; Swinbank, J.; Tagger, M.; Tang, Y.; Tasse, C.; Thoudam, S.; Toribio, C.; Vermeulen, R.; Vocks, C.; van Weeren, R. J.; Wijnholds, S. J.; Wise, M. W.; Wucknitz, O.; Yatawatta, S.; Zarka, P.; Zensus, A.
2015-03-01
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods. Aims: Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework. Methods: We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data. Results: We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions: Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKA.
Underwater Image Reconstruction Using Image Fusion Technique.
Ms. Kulkarni Aparna S.
2013-01-01
In proposed work the image fusion technique is used for reconstruction of underwater image. The main purpose of proposed work is to improve resolution of underwater image. There are various method employed for reconstruction of underwater images but some have limitations such as low resolution. Resolution is one of the parameter which is important for quality of images. Wavelet based image reconstruction may improve resolution of underwater images. Image fusion technique has three levels 1) d...
IMAGE RECONSTRUCTION AND OBJECT CLASSIFICATION IN CT IMAGING SYSTEM
张晓明; 蒋大真; 等
1995-01-01
By obtaining a feasible filter function,reconstructed images can be got with linear interpolation and filtered backoprojection techniques.Considering the gray and spatial correlation neighbour informations of each pixel,a new supervised classification method is put forward for the reconstructed images,and an experiment with noise image is done,the result shows that the method is feasible and accurate compared with ideal phantoms.
Spectral image reconstruction through the PCA transform
Ma, Long; Qiu, Xuewei; Cong, Yangming
2015-12-01
Digital color image reproduction based on spectral information has become a field of much interest and practical importance in recent years. The representation of color in digital form with multi-band images is not very accurate, hence the use of spectral image is justified. Reconstructing high-dimensional spectral reflectance images from relatively low-dimensional camera signals is generally an ill-posed problem. The aim of this study is to use the Principal component analysis (PCA) transform in spectral reflectance images reconstruction. The performance is evaluated by the mean, median and standard deviation of color difference values. The values of mean, median and standard deviation of root mean square (GFC) errors between the reconstructed and the actual spectral image were also calculated. Simulation experiments conducted on a six-channel camera system and on spectral test images show the performance of the suggested method.
An iterative image reconstruction algorithm for SPECT
Properties of two algorithms for iterative reconstruction of SPECT images, LS-MLEM and LS-OSEM, are studied and compared with the ML-EM algorithm in this paper. By using projection data of heavy-noise, their effectiveness in improving SPECT image quality is evaluated. A phantom with hot and cold lesion is used in the investigation. The reconstructed images using LS-MLEM or LS-OSEM show that there is not a rapid increase in image noise, and the 'best' estimate is assuming that the reconstructed images satisfy the statistical model. The major advantage of using LS-MLEM or LS-OSEM algorithm in SPECT imaging is in their ability to accurately control for heavy-noise. And LS-OSEM algorithm obviously improves the convergence rate. (authors)
Computational Imaging for VLBI Image Reconstruction
Bouman, Katherine L.; Johnson, Michael D; Zoran, Daniel; Fish, Vincent L.; Doeleman, Sheperd Samuel; Freeman, William T.
2016-01-01
Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other ...
Computational Imaging for VLBI Image Reconstruction
Bouman, Katherine L.; Johnson, Michael D; Zoran, Daniel; Fish, Vincent L.; Doeleman, Sheperd S.; Freeman, William T.
2016-01-01
Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other m...
Convergence of iterative image reconstruction algorithms for Digital Breast Tomosynthesis
Sidky, Emil; Jørgensen, Jakob Heide; Pan, Xiaochuan
Most iterative image reconstruction algorithms are based on some form of optimization, such as minimization of a data-fidelity term plus an image regularizing penalty term. While achieving the solution of these optimization problems may not directly be clinically relevant, accurate optimization...... solutions can aid in iterative image reconstruction algorithm design. This issue is particularly acute for iterative image reconstruction in Digital Breast Tomosynthesis (DBT), where the corresponding data model IS particularly poorly conditioned. The impact of this poor conditioning is that iterative....... Math. Imag. Vol. 40, pgs 120-145) and apply it to iterative image reconstruction in DBT....
Andreas Fhager; Shantanu K. Padhi; Mikael Persson; John Howard
2013-01-01
Nonlinear microwave imaging heavily relies on an accurate numerical electromagnetic model of the antenna system. The model is used to simulate scattering data that is compared to its measured counterpart in order to reconstruct the image. In this paper an antenna system immersed in water is used to image different canonical objects in order to investigate the implication of modeling errors on the final reconstruction using a time domain-based iterative inverse reconstruction algorithm and thr...
Invariant Image Watermarking Using Accurate Zernike Moments
Ismail A. Ismail
2010-01-01
Full Text Available problem statement: Digital image watermarking is the most popular method for image authentication, copyright protection and content description. Zernike moments are the most widely used moments in image processing and pattern recognition. The magnitudes of Zernike moments are rotation invariant so they can be used just as a watermark signal or be further modified to carry embedded data. The computed Zernike moments in Cartesian coordinate are not accurate due to geometrical and numerical error. Approach: In this study, we employed a robust image-watermarking algorithm using accurate Zernike moments. These moments are computed in polar coordinate, where both approximation and geometric errors are removed. Accurate Zernike moments are used in image watermarking and proved to be robust against different kind of geometric attacks. The performance of the proposed algorithm is evaluated using standard images. Results: Experimental results show that, accurate Zernike moments achieve higher degree of robustness than those approximated ones against rotation, scaling, flipping, shearing and affine transformation. Conclusion: By computing accurate Zernike moments, the embedded bits watermark can be extracted at low error rate.
Reconstruction techniques for optoacoustic imaging
Frenz, Martin; Koestli, Kornel P.; Paltauf, Guenther; Schmidt-Kloiber, Heinz; Weber, Heinz P.
2001-06-01
Optoacoustics is a method to gain information from inside a tissue. This is done by irradiating a tissue with a short light pulse, which generates a pressure distribution inside the tissue that mirrors the absorber distribution. The pressure distribution measured on the tissue-surface allows, by applying a back-projection method, to calculate a tomography image of the absorber distribution. This study presents a novel computational algorithm based on Fourier transform, which, at least in principle, yields an exact 3D reconstruction of the distribution of absorbed energy density inside turbid media. The reconstruction is based on 2D pressure distributions captured outside at different times. The FFT reconstruction algorithm is first tested in the back projection of simulated pressure transients of small model absorbers, and finally applied to reconstruct the distribution of artificial blood vessels in three dimensions.
Computational Imaging for VLBI Image Reconstruction
Bouman, Katherine L; Zoran, Daniel; Fish, Vincent L; Doeleman, Sheperd S; Freeman, William T
2015-01-01
Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other methods require careful tuning and parameter selection for different types of images, our method is robust and produces good results under different settings such as low SNR or extended emissions. The success of our method is demonstrated on realistic synthetic experiments as well as publicly available real data. We present this problem in a way that is accessible to members of the computer vision community, and provide a dataset website (vlbiimaging.csail.mit.edu) to allow for controlled comparisons across algorithms. Thi...
Towards an accurate volume reconstruction in atom probe tomography.
Beinke, Daniel; Oberdorfer, Christian; Schmitz, Guido
2016-06-01
An alternative concept for the reconstruction of atom probe data is outlined. It is based on the calculation of realistic trajectories of the evaporated ions in a recursive refinement process. To this end, the electrostatic problem is solved on a Delaunay tessellation. To enable the trajectory calculation, the order of reconstruction is inverted with respect to previous reconstruction schemes: the last atom detected is reconstructed first. In this way, the emitter shape, which controls the trajectory, can be defined throughout the duration of the reconstruction. A proof of concept is presented for 3D model tips, containing spherical precipitates or embedded layers of strongly contrasting evaporation thresholds. While the traditional method following Bas et al. generates serious distortions in these cases, a reconstruction with the proposed electrostatically informed approach improves the geometry of layers and particles significantly. PMID:27062338
Reconstruction of Cloud Contaminated Remote Sensing Images Using Inpainting Strategy
D.Linett Sophia
2013-05-01
Full Text Available This paper focuses a method for cloud detection and their reconstruction technique. Detecting theseportions of an image and then filling in the missing data is an important photo editing work. The filling-in approach such as inpainting techniques, which aim at filling holes in remote sensing images by propagating surrounding structures and texture information. Inpainting technique is a novel method for completing missing parts caused by the detection of foreground or background elements from an image.Reconstruction of missing data in remotely sensed image is of great challenge due to its complexity. These images may be partly contaminated by cloud. Detection of these clouds and accurate reconstruction of cloud removed area in satellite image is done here. To improve the accuracy in remotely sensed images, efficient inpainting techniques can be applied for reconstruction of missing regions. Large areas with lots of information lost are harder to reconstruct, because information in other parts of the image is not enough to get an impression of what is missing. Image inpainting is not to recover the originalimage, but to create some image that has a close resemblance with the original image. In this paper two different methods are proposed for filling in process. The first method involves reconstruction process by Exemplar Inpainting whereas the other method uses the Modified Exemplar inpainting. The entire process is developed using MATLAB software. The proposed method of cloud detection here is simple and easily applied to all cloud cover images.
Image reconstruction algorithms from projections
Many physical or bio-physical phenomena can be analysed as 'images' that is to say a bi-dimensionnal representation of a characteristic parameter of the material (density, concentration of a given element, resistivity, etc...). The various algorithms reviewed in the paper lead to a numerical reconstitution of such an image from a finite set of measurements considered as 'projections' of the initial object. We first give a physical insight then the mathematical formulation of the various concepts necessary for the presentation of the problem; after that we show why and how many reconstruction algorithms are possible. These different strategies are quickly compared chiefly according to realization facilities, structure, volume and performances (speed, accuracy) of the processing system required
Accurate reconstruction of insertion-deletion histories by statistical phylogenetics.
Oscar Westesson
Full Text Available The Multiple Sequence Alignment (MSA is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history, it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.
3D Reconstruction of NMR Images
Peter Izak; Milan Smetana; Libor Hargas; Miroslav Hrianka; Pavol Spanik
2007-01-01
This paper introduces experiment of 3D reconstruction NMR images scanned from magnetic resonance device. There are described methods which can be used for 3D reconstruction magnetic resonance images in biomedical application. The main idea is based on marching cubes algorithm. For this task was chosen sophistication method by program Vision Assistant, which is a part of program LabVIEW.
Iterative image reconstruction in ECT
A series of preliminary studies has been performed in the authors laboratories to explore the use of a priori information in Bayesian image restoration and reconstruction. One piece of a priori information is the fact that intensities of neighboring pixels tend to be similar if they belong to the same region within which similar tissue characteristics are exhibited. this property of local continuity can be modeled by the use of Gibbs priors, as first suggested by German and Geman. In their investigation, they also included line sites between each pair of neighboring pixels in the Gibbs prior and used discrete binary numbers to indicate the absence or presence of boundaries between regions. These two features of the a priori model permit averaging within boundaries of homogeneous regions to alleviate the degradation caused by Poisson noise. with the use of this Gibbs prior in combination with the technique of stochastic relaxation, German and Geman demonstrated that noise levels can be reduced significantly in 2-D image restoration. They have developed a Bayesian method that utilizes a Gibbs prior to describe the spatial correlation of neighboring regions and takes into account the effect of limited spatial resolution as well. The statistical framework of the proposed approach is based on the data augmentation scheme suggested by Tanner and Wong. Briefly outlined here, this Bayesian method is based on Geman and Geman's approach
Running image reconstruction for intraoperative MRI
Recently there has been a growing interest in the use of MR-guided navigation to improve the safety and effectiveness of surgical procedures. The purpose of our work was to develop and demonstrate an imaging strategy which allows a physician to continue operating without the delays caused from imaging. Phase scrambling Fourier transform imaging technique allows for a localized image to be reconstructed from the segmented signal. Therefore, continuous imaging (a running reconstruction) of MR images is feasible by combining this localized imaging property with fast image reconstruction. Since local images are updated in several TR periods, even during data acquisition, the orientation or location of the subject (devices) can be monitored faster than by the conventional fast imaging technique. Feasibility studies of the proposed imaging were performed using a 0.0183T MRI scanner and a personal computer for image reconstruction. Since the image SNR obtained by the ultra-low field MRI scanner was quite small, experiments using the phantom were performed. The proposed running reconstruction strategy appears to be feasible for a variety of guided interventional MRI procedures. It also appears to provide some important advantages over conventional 2DFT imaging. The proposed technique can be readily applied in a well established MRI equipped with the fast imaging technique by pulsing the resistive shim coils that are already present. (author)
Large scale vision based navigation without an accurate global reconstruction
Segvic, S.; Remazeilles, A.; Diosi, A.; Chaumette, François
2007-01-01
International audience Autonomous cars will likely play an important role in the future. A vision system designed to support outdoor navigation for such vehicles has to deal with large dynamic environments, changing imaging conditions, and temporary occlusions by other moving objects. This paper presents a novel appearance-based navigation framework relying on a single perspective vision sensor, which is aimed towards resolving of the above issues. The solution is based on a hierarchical e...
Sheng, Qiwei; Matthews, Thomas P; Xia, Jun; Zhu, Liren; Wang, Lihong V; Anastasio, Mark A
2015-01-01
Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. When the imaging system employs conventional piezoelectric ultrasonic transducers, the ideal photoacoustic (PA) signals are degraded by the transducers' acousto-electric impulse responses (EIRs) during the measurement process. If unaccounted for, this can degrade the accuracy of the reconstructed image. In principle, the effect of the EIRs on the measured PA signals can be ameliorated via deconvolution; images can be reconstructed subsequently by application of a reconstruction method that assumes an idealized EIR. Alternatively, the effect of the EIR can be incorporated into an imaging model and implicitly compensated for during reconstruction. In either case, the efficacy of the correction can be limited by errors in the assumed EIRs. In this work, a joint optimization approach to PACT image r...
Reconstruction of images from neuromagnetic fields
In response to specific stimuli, the human brain emits a measurable magnetic field from regions actively involved in processing the stimulus. We have implemented an iterative algorithm to reconstruct images from the neuromagnetic field. Computer simulation studies performed to develop the algorithm are reported. Experimental measurements of a human visually-evoked field and images reconstructed therefrom are also reported. The results demonstrate, for the first time, the feasibility of imaging multiple sources within the brain that produce a magnetic field
Joint image reconstruction and sensitivity estimation in SENSE (JSENSE).
Ying, Leslie; Sheng, Jinhua
2007-06-01
Parallel magnetic resonance imaging (pMRI) using multichannel receiver coils has emerged as an effective tool to reduce imaging time in various applications. However, the issue of accurate estimation of coil sensitivities has not been fully addressed, which limits the level of speed enhancement achievable with the technology. The self-calibrating (SC) technique for sensitivity extraction has been well accepted, especially for dynamic imaging, and complements the common calibration technique that uses a separate scan. However, the existing method to extract the sensitivity information from the SC data is not accurate enough when the number of data is small, and thus erroneous sensitivities affect the reconstruction quality when they are directly applied to the reconstruction equation. This paper considers this problem of error propagation in the sequential procedure of sensitivity estimation followed by image reconstruction in existing methods, such as sensitivity encoding (SENSE) and simultaneous acquisition of spatial harmonics (SMASH), and reformulates the image reconstruction problem as a joint estimation of the coil sensitivities and the desired image, which is solved by an iterative optimization algorithm. The proposed method was tested on various data sets. The results from a set of in vivo data are shown to demonstrate the effectiveness of the proposed method, especially when a rather large net acceleration factor is used. PMID:17534910
Breast reconstruction - methods and imaging
Silicon implants are used for breast reconstruction or for cosmetic operations. The contribution outlines the role of mammography, sonography and MR for defect assessment, tumour detection and monitoring after prosthesis implantation. Instrument adjustment for mammographic screening of patients with implants is gone into. Autologic reconstruction techniques and protocols of secondary and tertiary early detection are presented. (orig.)
Algorithms for reconstructing images for industrial applications
Several algorithms for reconstructing objects from their projections are being studied in our Laboratory, for industrial applications. Such algorithms are useful locating the position and shape of different composition of materials in the object. A Comparative study of two algorithms is made. The two investigated algorithsm are: The MART (Multiplicative - Algebraic Reconstruction Technique) and the Convolution Method. The comparison are carried out from the point view of the quality of the image reconstructed, number of views and cost. (Author)
Detection geometry and reconstruction error in magnetic source imaging
Hughett, P.; Budinger, T.F. [Lawrence Berkeley Lab., CA (United States)]|[California Univ., Berkeley, CA (United States). Dept. of Electrical Engineering and Computer Sciences
1993-11-01
A recently developed reconstruction algorithm for magnetic source imaging exploits prior knowledge about source location, source power density, detector geometry, and detector noise power to obtain an explicit estimate of the reconstruction error. This paper demonstrates the application of the new algorithm to the optimal design of practical detector arrays to minimize the reconstruction error in specific applications. For a representative configuration for magnetocardiography, the optimal array width (for minimum reconstruction error) varies from 19 to 28 cm depending on the assumed source depth, number of detectors, source power and noise power. The reconstruction accuracy ranges from 5% of the a priori standard deviation for the sources nearest the detector plane to 95% of the a priori deviation for the deepest sources. The reconstruction error was found to depend on accidental alignments between dipole sources and point detectors, indicating that a more sophisticated model is required for accurate estimates of reconstruction error. The error calculation is fast, taking about a second for this problem on a workstation-class computer. The availability of a method for rapidly computing the reconstruction error for any given source characteristics and detector geometry will facilitate the optimal design of magnetometer array size, element spacing, and orientation for specific applications in biomagnetic and geomagnetic source imaging.
4D image reconstruction for emission tomography
An overview of the theory of 4D image reconstruction for emission tomography is given along with a review of the current state of the art, covering both positron emission tomography and single photon emission computed tomography (SPECT). By viewing 4D image reconstruction as a matter of either linear or non-linear parameter estimation for a set of spatiotemporal functions chosen to approximately represent the radiotracer distribution, the areas of so-called ‘fully 4D’ image reconstruction and ‘direct kinetic parameter estimation’ are unified within a common framework. Many choices of linear and non-linear parameterization of these functions are considered (including the important case where the parameters have direct biological meaning), along with a review of the algorithms which are able to estimate these often non-linear parameters from emission tomography data. The other crucial components to image reconstruction (the objective function, the system model and the raw data format) are also covered, but in less detail due to the relatively straightforward extension from their corresponding components in conventional 3D image reconstruction. The key unifying concept is that maximum likelihood or maximum a posteriori (MAP) estimation of either linear or non-linear model parameters can be achieved in image space after carrying out a conventional expectation maximization (EM) update of the dynamic image series, using a Kullback-Leibler distance metric (comparing the modeled image values with the EM image values), to optimize the desired parameters. For MAP, an image-space penalty for regularization purposes is required. The benefits of 4D and direct reconstruction reported in the literature are reviewed, and furthermore demonstrated with simple simulation examples. It is clear that the future of reconstructing dynamic or functional emission tomography images, which often exhibit high levels of spatially correlated noise, should ideally exploit these 4D
Scattering Correction For Image Reconstruction In Flash Radiography
Cao, Liangzhi; Wang, Mengqi; Wu, Hongchun; Liu, Zhouyu; Cheng, Yuxiong; Zhang, Hongbo [Xi' an Jiaotong Univ., Xi' an (China)
2013-08-15
Scattered photons cause blurring and distortions in flash radiography, reducing the accuracy of image reconstruction significantly. The effect of the scattered photons is taken into account and an iterative deduction of the scattered photons is proposed to amend the scattering effect for image restoration. In order to deduct the scattering contribution, the flux of scattered photons is estimated as the sum of two components. The single scattered component is calculated accurately together with the uncollided flux along the characteristic ray, while the multiple scattered component is evaluated using correction coefficients pre-obtained from Monte Carlo simulations.The arbitrary geometry pretreatment and ray tracing are carried out based on the customization of AutoCAD. With the above model, an Iterative Procedure for image restORation code, IPOR, is developed. Numerical results demonstrate that the IPOR code is much more accurate than the direct reconstruction solution without scattering correction and it has a very high computational efficiency.
Scattering Correction For Image Reconstruction In Flash Radiography
Scattered photons cause blurring and distortions in flash radiography, reducing the accuracy of image reconstruction significantly. The effect of the scattered photons is taken into account and an iterative deduction of the scattered photons is proposed to amend the scattering effect for image restoration. In order to deduct the scattering contribution, the flux of scattered photons is estimated as the sum of two components. The single scattered component is calculated accurately together with the uncollided flux along the characteristic ray, while the multiple scattered component is evaluated using correction coefficients pre-obtained from Monte Carlo simulations.The arbitrary geometry pretreatment and ray tracing are carried out based on the customization of AutoCAD. With the above model, an Iterative Procedure for image restORation code, IPOR, is developed. Numerical results demonstrate that the IPOR code is much more accurate than the direct reconstruction solution without scattering correction and it has a very high computational efficiency
Medical image reconstruction a conceptual tutorial
Zeng, Gengsheng
2010-01-01
This text introduces classical and modern image reconstruction technologies. It presents both analytical and iterative methods of these technologies and their applications in X-ray CT, SPECT, PET and MRI.
Structured illumination microscopy image reconstruction algorithm
Lal, Amit; Shan, Chunyan; Xi, Peng
2016-01-01
Structured illumination microscopy (SIM) is a very important super-resolution microscopy technique, which provides high speed super-resolution with about two-fold spatial resolution enhancement. Several attempts aimed at improving the performance of SIM reconstruction algorithm have been reported. However, most of these highlight only one specific aspect of the SIM reconstruction -- such as the determination of the illumination pattern phase shift accurately -- whereas other key elements -- s...
3D Reconstruction of NMR Images
Peter Izak
2007-01-01
Full Text Available This paper introduces experiment of 3D reconstruction NMR images scanned from magnetic resonance device. There are described methods which can be used for 3D reconstruction magnetic resonance images in biomedical application. The main idea is based on marching cubes algorithm. For this task was chosen sophistication method by program Vision Assistant, which is a part of program LabVIEW.
Bayesian image reconstruction: Application to emission tomography
Nunez, J.; Llacer, J.
1989-02-01
In this paper we propose a Maximum a Posteriori (MAP) method of image reconstruction in the Bayesian framework for the Poisson noise case. We use entropy to define the prior probability and likelihood to define the conditional probability. The method uses sharpness parameters which can be theoretically computed or adjusted, allowing us to obtain MAP reconstructions without the problem of the grey'' reconstructions associated with the pre Bayesian reconstructions. We have developed several ways to solve the reconstruction problem and propose a new iterative algorithm which is stable, maintains positivity and converges to feasible images faster than the Maximum Likelihood Estimate method. We have successfully applied the new method to the case of Emission Tomography, both with simulated and real data. 41 refs., 4 figs., 1 tab.
Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography
wang, Kun; Oraevsky, Alexander A; Anastasio, Mark A
2012-01-01
Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response, and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image reconstruction algorithms for improving image quality in three-dimensional (3D) OAT. In this work, we implement and investigate two iterative image reconstruction methods for use with a 3D OAT small animal imager: namely, a penalized least-squares (PLS) method employing a quadratic smoothness penalty and a PLS method employing a total variation norm penalty. The reconstruction algorithms employ accurate models of the ultrasonic transducer impulse responses. Experimental data sets are employed to compare the performances of the iterative reconstruction algorithms to that of a 3D filtered backprojection (FBP) algorithm. By ...
3-D Reconstruction of Medical Image Using Wavelet Transform and Snake Model
Jinyong Cheng
2009-12-01
Full Text Available Medical image segmentation is an important step in 3-D reconstruction, and 3-D reconstruction from medical images is an important application of computer graphics and biomedicine image processing. An improved image segmentation method which is suitable for 3-D reconstruction is presented in this paper. A 3-D reconstruction algorithm is used to reconstruct the 3-D model from medical images. Rough edge is obtained by multi-scale wavelet transform at first. With the rough edge, improved gradient vector flow snake model is used and the object contour in the image is found. In the experiments, we reconstruct 3-D models of kidney, liver and brain putamen. The performances of the experiments indicate that the new algorithm can produce accurate 3-D reconstruction.
Reconstruction Algorithms in Undersampled AFM Imaging
Arildsen, Thomas; Oxvig, Christian Schou; Pedersen, Patrick Steffen;
2016-01-01
This paper provides a study of spatial undersampling in atomic force microscopy (AFM) imaging followed by different image reconstruction techniques based on sparse approximation as well as interpolation. The main reasons for using undersampling is that it reduces the path length and thereby the...... scanning time as well as the amount of interaction between the AFM probe and the specimen. It can easily be applied on conventional AFM hardware. Due to undersampling, it is then necessary to further process the acquired image in order to reconstruct an approximation of the image. Based on real AFM cell...
Heuristic optimization in penumbral image for high resolution reconstructed image
Penumbral imaging is a technique which uses the fact that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. The size of the penumbral image on the detector can be mathematically determined as its aperture size, object size, and magnification. Conventional reconstruction methods are very sensitive to noise. On the other hand, the heuristic reconstruction method is very tolerant of noise. However, the aperture size influences the accuracy and resolution of the reconstructed image. In this article, we propose the optimization of the aperture size for the neutron penumbral imaging.
Sparse Image Reconstruction in Computed Tomography
Jørgensen, Jakob Sauer
In recent years, increased focus on the potentially harmful effects of x-ray computed tomography (CT) scans, such as radiation-induced cancer, has motivated research on new low-dose imaging techniques. Sparse image reconstruction methods, as studied for instance in the field of compressed sensing...
Efficient MR image reconstruction for compressed MR imaging.
Huang, Junzhou; Zhang, Shaoting; Metaxas, Dimitris
2011-10-01
In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (TV) and L1 norm regularization. This has been shown to be very powerful for the MR image reconstruction. First, we decompose the original problem into L1 and TV norm regularization subproblems respectively. Then, these two subproblems are efficiently solved by existing techniques. Finally, the reconstructed image is obtained from the weighted average of solutions from two subproblems in an iterative framework. We compare the proposed algorithm with previous methods in term of the reconstruction accuracy and computation complexity. Numerous experiments demonstrate the superior performance of the proposed algorithm for compressed MR image reconstruction. PMID:21742542
Techniques in Iterative Proton CT Image Reconstruction
Penfold, Scott
2015-01-01
This is a review paper on some of the physics, modeling, and iterative algorithms in proton computed tomography (pCT) image reconstruction. The primary challenge in pCT image reconstruction lies in the degraded spatial resolution resulting from multiple Coulomb scattering within the imaged object. Analytical models such as the most likely path (MLP) have been proposed to predict the scattered trajectory from measurements of individual proton location and direction before and after the object. Iterative algorithms provide a flexible tool with which to incorporate these models into image reconstruction. The modeling leads to a large and sparse linear system of equations that can efficiently be solved by projection methods-based iterative algorithms. Such algorithms perform projections of the iterates onto the hyperlanes that are represented by the linear equations of the system. They perform these projections in possibly various algorithmic structures, such as block-iterative projections (BIP), string-averaging...
Image formation and tomogram reconstruction in optical coherence microscopy
Villiger, Martin; Lasser, Theo
2010-01-01
In this work we present a model for image formation in optical coherence microscopy. In the spectral domain detection, each wavenumber has a specific coherent transfer function that samples a different part of the object's spatial frequency spectrum. The reconstruction of the tomogram is usually accurate only in a short depth of field. Using numerical simulations based on the developed model, we identified two distinct mechanisms that influence the signal of out-of-focus sample information. B...
Mingjian Sun
2015-01-01
Full Text Available Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO optimized support vector machine (SVM interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.
Bayesian Image Reconstruction Based on Voronoi Diagrams
Cabrera, G F; Hitschfeld, N
2007-01-01
We present a Bayesian Voronoi image reconstruction technique (VIR) for interferometric data. Bayesian analysis applied to the inverse problem allows us to derive the a-posteriori probability of a novel parameterization of interferometric images. We use a variable Voronoi diagram as our model in place of the usual fixed pixel grid. A quantization of the intensity field allows us to calculate the likelihood function and a-priori probabilities. The Voronoi image is optimized including the number of polygons as free parameters. We apply our algorithm to deconvolve simulated interferometric data. Residuals, restored images and chi^2 values are used to compare our reconstructions with fixed grid models. VIR has the advantage of modeling the image with few parameters, obtaining a better image from a Bayesian point of view.
Terahertz Imaging for Biomedical Applications Pattern Recognition and Tomographic Reconstruction
Yin, Xiaoxia; Abbott, Derek
2012-01-01
Terahertz Imaging for Biomedical Applications: Pattern Recognition and Tomographic Reconstruction presents the necessary algorithms needed to assist screening, diagnosis, and treatment, and these algorithms will play a critical role in the accurate detection of abnormalities present in biomedical imaging. Terahertz biomedical imaging has become an area of interest due to its ability to simultaneously acquire both image and spectral information. Terahertz imaging systems are being commercialized with an increasing number of trials performed in a biomedical setting. Terahertz tomographic imaging and detection technology contributes to the ability to identify opaque objects with clear boundaries,and would be useful to both in vivo and ex vivo environments. This book also: Introduces terahertz radiation techniques and provides a number of topical examples of signal and image processing, as well as machine learning Presents the most recent developments in an emerging field, terahertz radiation Utilizes new methods...
Superresolution images reconstructed from aliased images
Vandewalle, Patrick; Süsstrunk, Sabine; Vetterli, Martin
2003-01-01
In this paper, we present a simple method to almost quadruple the spatial resolution of aliased images. From a set of four low resolution, undersampled and shifted images, a new image is constructed with almost twice the resolution in each dimension. The resulting image is aliasing-free. A small aliasing-free part of the frequency domain of the images is used to compute the exact subpixel shifts. When the relative image positions are known, a higher resolution image can be constructed using t...
Thimo Hugger
Full Text Available In this article we aim at improving the performance of whole brain functional imaging at very high temporal resolution (100 ms or less. This is achieved by utilizing a nonlinear regularized parallel image reconstruction scheme, where the penalty term of the cost function is set to the L(1-norm measured in some transform domain. This type of image reconstruction has gained much attention recently due to its application in compressed sensing and has proven to yield superior spatial resolution and image quality over e.g. Tikhonov regularized image reconstruction. We demonstrate that by using nonlinear regularization it is possible to more accurately localize brain activation from highly undersampled k-space data at the expense of an increase in computation time.
STATISTICAL ANALYSIS OF TOMOGRAPHIC RECONSTRUCTION ALGORITHMS BY MORPHOLOGICAL IMAGE CHARACTERISTICS
Sebastian Lǖck
2011-05-01
Full Text Available We suggest a procedure for quantitative quality control of tomographic reconstruction algorithms. Our task-oriented evaluation focuses on the correct reproduction of phase boundary length and has thus a clear implication for morphological image analysis of tomographic data. Indirectly the method monitors accurate reproduction of a variety of locally defined critical image features within tomograms such as interface positions and microstructures, debonding, cracks and pores. Tomographic errors of such local nature are neglected if only global integral characteristics such as mean squared deviation are considered for the evaluation of an algorithm. The significance of differences in reconstruction quality between algorithms is assessed using a sample of independent random scenes to be reconstructed. These are generated by a Boolean model and thus exhibit a substantial stochastic variability with respect to image morphology. It is demonstrated that phase boundaries in standard reconstructions by filtered backprojection exhibit substantial errors. In the setting of our simulations, these could be significantly reduced by the use of the innovative reconstruction algorithm DIRECTT.
Purpose: In high dose rate brachytherapy (HDR-B), current catheter reconstruction protocols are relatively slow and error prone. The purpose of this technical note is to evaluate the accuracy and the robustness of an electromagnetic (EM) tracking system for automated and real-time catheter reconstruction. Methods: For this preclinical study, a total of ten catheters were inserted in gelatin phantoms with different trajectories. Catheters were reconstructed using a 18G biopsy needle, used as an EM stylet and equipped with a miniaturized sensor, and the second generation Aurora® Planar Field Generator from Northern Digital Inc. The Aurora EM system provides position and orientation value with precisions of 0.7 mm and 0.2°, respectively. Phantoms were also scanned using a μCT (GE Healthcare) and Philips Big Bore clinical computed tomography (CT) system with a spatial resolution of 89 μm and 2 mm, respectively. Reconstructions using the EM stylet were compared to μCT and CT. To assess the robustness of the EM reconstruction, five catheters were reconstructed twice and compared. Results: Reconstruction time for one catheter was 10 s, leading to a total reconstruction time inferior to 3 min for a typical 17-catheter implant. When compared to the μCT, the mean EM tip identification error was 0.69 ± 0.29 mm while the CT error was 1.08 ± 0.67 mm. The mean 3D distance error was found to be 0.66 ± 0.33 mm and 1.08 ± 0.72 mm for the EM and CT, respectively. EM 3D catheter trajectories were found to be more accurate. A maximum difference of less than 0.6 mm was found between successive EM reconstructions. Conclusions: The EM reconstruction was found to be more accurate and precise than the conventional methods used for catheter reconstruction in HDR-B. This approach can be applied to any type of catheters and applicators
Poulin, Eric; Racine, Emmanuel; Beaulieu, Luc, E-mail: Luc.Beaulieu@phy.ulaval.ca [Département de physique, de génie physique et d’optique et Centre de recherche sur le cancer de l’Université Laval, Université Laval, Québec, Québec G1V 0A6, Canada and Département de radio-oncologie et Axe Oncologie du Centre de recherche du CHU de Québec, CHU de Québec, 11 Côte du Palais, Québec, Québec G1R 2J6 (Canada); Binnekamp, Dirk [Integrated Clinical Solutions and Marketing, Philips Healthcare, Veenpluis 4-6, Best 5680 DA (Netherlands)
2015-03-15
Purpose: In high dose rate brachytherapy (HDR-B), current catheter reconstruction protocols are relatively slow and error prone. The purpose of this technical note is to evaluate the accuracy and the robustness of an electromagnetic (EM) tracking system for automated and real-time catheter reconstruction. Methods: For this preclinical study, a total of ten catheters were inserted in gelatin phantoms with different trajectories. Catheters were reconstructed using a 18G biopsy needle, used as an EM stylet and equipped with a miniaturized sensor, and the second generation Aurora{sup ®} Planar Field Generator from Northern Digital Inc. The Aurora EM system provides position and orientation value with precisions of 0.7 mm and 0.2°, respectively. Phantoms were also scanned using a μCT (GE Healthcare) and Philips Big Bore clinical computed tomography (CT) system with a spatial resolution of 89 μm and 2 mm, respectively. Reconstructions using the EM stylet were compared to μCT and CT. To assess the robustness of the EM reconstruction, five catheters were reconstructed twice and compared. Results: Reconstruction time for one catheter was 10 s, leading to a total reconstruction time inferior to 3 min for a typical 17-catheter implant. When compared to the μCT, the mean EM tip identification error was 0.69 ± 0.29 mm while the CT error was 1.08 ± 0.67 mm. The mean 3D distance error was found to be 0.66 ± 0.33 mm and 1.08 ± 0.72 mm for the EM and CT, respectively. EM 3D catheter trajectories were found to be more accurate. A maximum difference of less than 0.6 mm was found between successive EM reconstructions. Conclusions: The EM reconstruction was found to be more accurate and precise than the conventional methods used for catheter reconstruction in HDR-B. This approach can be applied to any type of catheters and applicators.
Image Reconstruction Image reconstruction by using local inverse for full field of view
Yang, Kang; Yang, Xintie; Zhao, Shuang-Ren
2015-01-01
The iterative refinement method (IRM) has been very successfully applied in many different fields for examples the modern quantum chemical calculation and CT image reconstruction. It is proved that the refinement method can create an exact inverse from an approximate inverse with a few iterations. The IRM has been used in CT image reconstruction to lower the radiation dose. The IRM utilize the errors between the original measured data and the recalculated data to correct the reconstructed images. However if it is not smooth inside the object, there often is an over-correction along the boundary of the organs in the reconstructed images. The over-correction increase the noises especially on the edges inside the image. One solution to reduce the above mentioned noises is using some kind of filters. Filtering the noise before/after/between the image reconstruction processing. However filtering the noises also means reduce the resolution of the reconstructed images. The filtered image is often applied to the imag...
Image reconstructions with the rotating RF coil.
Trakic, A; Wang, H; Weber, E; Li, B K; Poole, M; Liu, F; Crozier, S
2009-12-01
Recent studies have shown that rotating a single RF transceive coil (RRFC) provides a uniform coverage of the object and brings a number of hardware advantages (i.e. requires only one RF channel, averts coil-coil coupling interactions and facilitates large-scale multi-nuclear imaging). Motion of the RF coil sensitivity profile however violates the standard Fourier Transform definition of a time-invariant signal, and the images reconstructed in this conventional manner can be degraded by ghosting artifacts. To overcome this problem, this paper presents Time Division Multiplexed-Sensitivity Encoding (TDM-SENSE), as a new image reconstruction scheme that exploits the rotation of the RF coil sensitivity profile to facilitate ghost-free image reconstructions and reductions in image acquisition time. A transceive RRFC system for head imaging at 2 Tesla was constructed and applied in a number of in vivo experiments. In this initial study, alias-free head images were obtained in half the usual scan time. It is hoped that new sequences and methods will be developed by taking advantage of coil motion. PMID:19800824
Image reconstructions with the rotating RF coil
Trakic, A.; Wang, H.; Weber, E.; Li, B. K.; Poole, M.; Liu, F.; Crozier, S.
2009-12-01
Recent studies have shown that rotating a single RF transceive coil (RRFC) provides a uniform coverage of the object and brings a number of hardware advantages (i.e. requires only one RF channel, averts coil-coil coupling interactions and facilitates large-scale multi-nuclear imaging). Motion of the RF coil sensitivity profile however violates the standard Fourier Transform definition of a time-invariant signal, and the images reconstructed in this conventional manner can be degraded by ghosting artifacts. To overcome this problem, this paper presents Time Division Multiplexed — Sensitivity Encoding (TDM-SENSE), as a new image reconstruction scheme that exploits the rotation of the RF coil sensitivity profile to facilitate ghost-free image reconstructions and reductions in image acquisition time. A transceive RRFC system for head imaging at 2 Tesla was constructed and applied in a number of in vivo experiments. In this initial study, alias-free head images were obtained in half the usual scan time. It is hoped that new sequences and methods will be developed by taking advantage of coil motion.
Optimizing modelling in iterative image reconstruction for preclinical pinhole PET
Goorden, Marlies C.; van Roosmalen, Jarno; van der Have, Frans; Beekman, Freek J.
2016-05-01
The recently developed versatile emission computed tomography (VECTor) technology enables high-energy SPECT and simultaneous SPECT and PET of small animals at sub-mm resolutions. VECTor uses dedicated clustered pinhole collimators mounted in a scanner with three stationary large-area NaI(Tl) gamma detectors. Here, we develop and validate dedicated image reconstruction methods that compensate for image degradation by incorporating accurate models for the transport of high-energy annihilation gamma photons. Ray tracing software was used to calculate photon transport through the collimator structures and into the gamma detector. Input to this code are several geometric parameters estimated from system calibration with a scanning 99mTc point source. Effects on reconstructed images of (i) modelling variable depth-of-interaction (DOI) in the detector, (ii) incorporating photon paths that go through multiple pinholes (‘multiple-pinhole paths’ (MPP)), and (iii) including various amounts of point spread function (PSF) tail were evaluated. Imaging 18F in resolution and uniformity phantoms showed that including large parts of PSFs is essential to obtain good contrast-noise characteristics and that DOI modelling is highly effective in removing deformations of small structures, together leading to 0.75 mm resolution PET images of a hot-rod Derenzo phantom. Moreover, MPP modelling reduced the level of background noise. These improvements were also clearly visible in mouse images. Performance of VECTor can thus be significantly improved by accurately modelling annihilation gamma photon transport.
Yuan, Xiaohui; Ozturk, Cengizhan; Chi-Fishman, Gloria
2007-03-01
This paper describe our work on tagline detection and tissue strain synthesis. The tagline detection method extends our previous work 16 using pseudo-wavelet reconstruction. The novelty in tagline detection is that we integrated an active contour model and successfully improved the detection and indexing performance. Using pseudo-wavelet reconstruction-based method, prominent wavelet coefficients were retained while others were eliminated. Taglines were then extracted from the reconstructed images using thresholding. Due to noise and artifacts, a tagline can be broken into segments. We employed an active contour model that tracks the most likely segments and bridges them. Experiments demonstrated that our method extracts taglines automatically with greater robustness. Tissue strain was also reconstructed using extracted taglines.
Image reconstruction for brain CT slices
吴建明; 施鹏飞
2004-01-01
Different modalities in biomedical images, like CT, MRI and PET scanners, provide detailed cross-sectional views of human anatomy. This paper introduces three-dimensional brain reconstruction based on CT slices. It contains filtering, fuzzy segmentation, matching method of contours, cell array structure and image animation. Experimental results have shown its validity. The innovation is matching method of contours and fuzzy segmentation algorithm of CT slices.
Connections model for tomographic images reconstruction
This paper shows an artificial neural network with an adequately topology for tomographic image reconstruction. The associated error function is derived and the learning algorithm is make. The simulated results are presented and demonstrate the existence of a generalized solution for nets with linear activation function. (Author)
3D Reconstruction in Magnetic Resonance Imaging
Mikulka, J.; Bartušek, Karel
2010-01-01
Roč. 6, č. 7 (2010), s. 617-620. ISSN 1931-7360 R&D Projects: GA ČR GA102/09/0314 Institutional research plan: CEZ:AV0Z20650511 Keywords : reconstruction methods * magnetic resonance imaging Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering
3D Reconstruction in Magnetic Resonance Imaging
Mikulka, J.; Bartušek, Karel
Cambridge : The Electromagnetics Academy, 2010, s. 1043-1046. ISBN 978-1-934142-14-1. [PIERS 2010 Cambridge. Cambridge (US), 05.07.2010-08.07.2010] R&D Projects: GA ČR GA102/09/0314 Institutional research plan: CEZ:AV0Z20650511 Keywords : 3D reconstruction * magnetic resonance imaging Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering
Implementation of efficient image reconstruction for CT
Jie Liu; Guangfei Wang
2005-01-01
@@ The operational procedures for efficiently reconstructing the two-dimensional image of a body by the filtered back projection are described in this paper. The projections are interpolated for four times of original projection by zero-padding the original projection in frequency-domain and then inverse fast Fourier transform (FFT) is taken to improve accuracy.
Stochastic image reconstruction for a dual-particle imaging system
Hamel, M. C.; Polack, J. K.; Poitrasson-Rivière, A.; Flaska, M.; Clarke, S. D.; Pozzi, S. A.; Tomanin, A.; Peerani, P.
2016-02-01
Stochastic image reconstruction has been applied to a dual-particle imaging system being designed for nuclear safeguards applications. The dual-particle imager (DPI) is a combined Compton-scatter and neutron-scatter camera capable of producing separate neutron and photon images. The stochastic origin ensembles (SOE) method was investigated as an imaging method for the DPI because only a minimal estimation of system response is required to produce images with quality that is comparable to common maximum-likelihood methods. This work contains neutron and photon SOE image reconstructions for a 252Cf point source, two mixed-oxide (MOX) fuel canisters representing point sources, and the MOX fuel canisters representing a distributed source. Simulation of the DPI using MCNPX-PoliMi is validated by comparison of simulated and measured results. Because image quality is dependent on the number of counts and iterations used, the relationship between these quantities is investigated.
Sampling conditions for gradient-magnitude sparsity based image reconstruction algorithms
Sidky, Emil Y.; Jørgensen, Jakob H.; Pan, Xiaochuan
2012-03-01
Image reconstruction from sparse-view data in 2D fan-beam CT is investigated by constrained, total-variation minimization. This optimization problem exploits possible sparsity in the gradient magnitude image (GMI). The investigation is performed in simulation under ideal, noiseless data conditions in order to reveal a possible link between GMI sparsity and the necessary number of projection views for reconstructing an accurate image. Results are shown for two, quite different phantoms of similar GMI sparsity.
Simultaneous algebraic reconstruction technique based on guided image filtering.
Ji, Dongjiang; Qu, Gangrong; Liu, Baodong
2016-07-11
The challenge of computed tomography is to reconstruct high-quality images from few-view projections. Using a prior guidance image, guided image filtering smoothes images while preserving edge features. The prior guidance image can be incorporated into the image reconstruction process to improve image quality. We propose a new simultaneous algebraic reconstruction technique based on guided image filtering. Specifically, the prior guidance image is updated in the image reconstruction process, merging information iteratively. To validate the algorithm practicality and efficiency, experiments were performed with numerical phantom projection data and real projection data. The results demonstrate that the proposed method is effective and efficient for nondestructive testing and rock mechanics. PMID:27410859
3-D Reconstruction From Satellite Images
Denver, Troelz
1999-01-01
The aim of this project has been to implement a software system, that is able to create a 3-D reconstruction from two or more 2-D photographic images made from different positions. The height is determined from the disparity difference of the images. The general purpose of the system is mapping of......, where various methods have been tested in order to optimize the performance. The match results are used in the reconstruction part to establish a 3-D digital representation and finally, different presentation forms are discussed....... treated individually. A detailed treatment of various lens distortions is required, in order to correct for these problems. This subject is included in the acquisition part. In the calibration part, the perspective distortion is removed from the images. Most attention has been paid to the matching problem...
Calibration of time-of-flight cameras for accurate intraoperative surface reconstruction
Purpose: In image-guided surgery (IGS) intraoperative image acquisition of tissue shape, motion, and morphology is one of the main challenges. Recently, time-of-flight (ToF) cameras have emerged as a new means for fast range image acquisition that can be used for multimodal registration of the patient anatomy during surgery. The major drawbacks of ToF cameras are systematic errors in the image acquisition technique that compromise the quality of the measured range images. In this paper, we propose a calibration concept that, for the first time, accounts for all known systematic errors affecting the quality of ToF range images. Laboratory and in vitro experiments assess its performance in the context of IGS.Methods: For calibration the camera-related error sources depending on the sensor, the sensor temperature and the set integration time are corrected first, followed by the scene-specific errors, which are modeled as function of the measured distance, the amplitude and the radial distance to the principal point of the camera. Accounting for the high accuracy demands in IGS, we use a custom-made calibration device to provide reference distance data, the cameras are calibrated too. To evaluate the mitigation of the error, the remaining residual error after ToF depth calibration was compared with that arising from using the manufacturer routines for several state-of-the-art ToF cameras. The accuracy of reconstructed ToF surfaces was investigated after multimodal registration with computed tomography (CT) data of liver models by assessment of the target registration error (TRE) of markers introduced in the livers.Results: For the inspected distance range of up to 2 m, our calibration approach yielded a mean residual error to reference data ranging from 1.5 ± 4.3 mm for the best camera to 7.2 ± 11.0 mm. When compared to the data obtained from the manufacturer routines, the residual error was reduced by at least 78% from worst calibration result to most accurate
Calibration of time-of-flight cameras for accurate intraoperative surface reconstruction
Mersmann, Sven; Seitel, Alexander; Maier-Hein, Lena [Division of Medical and Biological Informatics, Junior Group Computer-assisted Interventions, German Cancer Research Center (DKFZ), Heidelberg, Baden-Wurttemberg 69120 (Germany); Erz, Michael; Jähne, Bernd [Heidelberg Collaboratory for Image Processing (HCI), University of Heidelberg, Baden-Wurttemberg 69115 (Germany); Nickel, Felix; Mieth, Markus; Mehrabi, Arianeb [Department of General, Visceral and Transplant Surgery, University of Heidelberg, Baden-Wurttemberg 69120 (Germany)
2013-08-15
Purpose: In image-guided surgery (IGS) intraoperative image acquisition of tissue shape, motion, and morphology is one of the main challenges. Recently, time-of-flight (ToF) cameras have emerged as a new means for fast range image acquisition that can be used for multimodal registration of the patient anatomy during surgery. The major drawbacks of ToF cameras are systematic errors in the image acquisition technique that compromise the quality of the measured range images. In this paper, we propose a calibration concept that, for the first time, accounts for all known systematic errors affecting the quality of ToF range images. Laboratory and in vitro experiments assess its performance in the context of IGS.Methods: For calibration the camera-related error sources depending on the sensor, the sensor temperature and the set integration time are corrected first, followed by the scene-specific errors, which are modeled as function of the measured distance, the amplitude and the radial distance to the principal point of the camera. Accounting for the high accuracy demands in IGS, we use a custom-made calibration device to provide reference distance data, the cameras are calibrated too. To evaluate the mitigation of the error, the remaining residual error after ToF depth calibration was compared with that arising from using the manufacturer routines for several state-of-the-art ToF cameras. The accuracy of reconstructed ToF surfaces was investigated after multimodal registration with computed tomography (CT) data of liver models by assessment of the target registration error (TRE) of markers introduced in the livers.Results: For the inspected distance range of up to 2 m, our calibration approach yielded a mean residual error to reference data ranging from 1.5 ± 4.3 mm for the best camera to 7.2 ± 11.0 mm. When compared to the data obtained from the manufacturer routines, the residual error was reduced by at least 78% from worst calibration result to most accurate
Optimized 3D Street Scene Reconstruction from Driving Recorder Images
Yongjun Zhang
2015-07-01
Full Text Available The paper presents an automatic region detection based method to reconstruct street scenes from driving recorder images. The driving recorder in this paper is a dashboard camera that collects images while the motor vehicle is moving. An enormous number of moving vehicles are included in the collected data because the typical recorders are often mounted in the front of moving vehicles and face the forward direction, which can make matching points on vehicles and guardrails unreliable. Believing that utilizing these image data can reduce street scene reconstruction and updating costs because of their low price, wide use, and extensive shooting coverage, we therefore proposed a new method, which is called the Mask automatic detecting method, to improve the structure results from the motion reconstruction. Note that we define vehicle and guardrail regions as “mask” in this paper since the features on them should be masked out to avoid poor matches. After removing the feature points in our new method, the camera poses and sparse 3D points that are reconstructed with the remaining matches. Our contrast experiments with the typical pipeline of structure from motion (SfM reconstruction methods, such as Photosynth and VisualSFM, demonstrated that the Mask decreased the root-mean-square error (RMSE of the pairwise matching results, which led to more accurate recovering results from the camera-relative poses. Removing features from the Mask also increased the accuracy of point clouds by nearly 30%–40% and corrected the problems of the typical methods on repeatedly reconstructing several buildings when there was only one target building.
Sparse image reconstruction for molecular imaging
Ting, Michael; Hero, Alfred O
2008-01-01
The application that motivates this paper is molecular imaging at the atomic level. When discretized at sub-atomic distances, the volume is inherently sparse. Noiseless measurements from an imaging technology can be modeled by convolution of the image with the system point spread function (psf). Such is the case with magnetic resonance force microscopy (MRFM), an emerging technology where imaging of an individual tobacco mosaic virus was recently demonstrated with nanometer resolution. We also consider additive white Gaussian noise (AWGN) in the measurements. Many prior works of sparse estimators have focused on the case when H has low coherence; however, the system matrix H in our application is the convolution matrix for the system psf. A typical convolution matrix has high coherence. The paper therefore does not assume a low coherence H. A discrete-continuous form of the Laplacian and atom at zero (LAZE) p.d.f. used by Johnstone and Silverman is formulated, and two sparse estimators derived by maximizing t...
Jørgensen, Jakob H; Pan, Xiaochuan
2011-01-01
Discrete-to-discrete imaging models for computed tomography (CT) are becoming increasingly ubiquitous as the interest in iterative image reconstruction algorithms has heightened. Despite this trend, all the intuition for algorithm and system design derives from analysis of continuous-to-continuous models such as the X-ray and Radon transform. While the similarity between these models justifies some crossover, questions such as what are sufficient sampling conditions can be quite different for the two models. This sampling issue is addressed extensively in the first half of the article using singular value decomposition analysis for determining sufficient number of views and detector bins. The question of full sampling for CT is particularly relevant to current attempts to adapt compressive sensing (CS) motivated methods to application in CT image reconstruction. The second half goes in depth on this subject and discusses the link between object sparsity and sufficient sampling for accurate reconstruction. Par...
Propagation phasor approach for holographic image reconstruction
Luo, Wei; Zhang, Yibo; Göröcs, Zoltán; Feizi, Alborz; Ozcan, Aydogan
2016-01-01
To achieve high-resolution and wide field-of-view, digital holographic imaging techniques need to tackle two major challenges: phase recovery and spatial undersampling. Previously, these challenges were separately addressed using phase retrieval and pixel super-resolution algorithms, which utilize the diversity of different imaging parameters. Although existing holographic imaging methods can achieve large space-bandwidth-products by performing pixel super-resolution and phase retrieval sequentially, they require large amounts of data, which might be a limitation in high-speed or cost-effective imaging applications. Here we report a propagation phasor approach, which for the first time combines phase retrieval and pixel super-resolution into a unified mathematical framework and enables the synthesis of new holographic image reconstruction methods with significantly improved data efficiency. In this approach, twin image and spatial aliasing signals, along with other digital artifacts, are interpreted as noise terms that are modulated by phasors that analytically depend on the lateral displacement between hologram and sensor planes, sample-to-sensor distance, wavelength, and the illumination angle. Compared to previous holographic reconstruction techniques, this new framework results in five- to seven-fold reduced number of raw measurements, while still achieving a competitive resolution and space-bandwidth-product. We also demonstrated the success of this approach by imaging biological specimens including Papanicolaou and blood smears. PMID:26964671
Propagation phasor approach for holographic image reconstruction
Luo, Wei; Zhang, Yibo; Göröcs, Zoltán; Feizi, Alborz; Ozcan, Aydogan
2016-03-01
To achieve high-resolution and wide field-of-view, digital holographic imaging techniques need to tackle two major challenges: phase recovery and spatial undersampling. Previously, these challenges were separately addressed using phase retrieval and pixel super-resolution algorithms, which utilize the diversity of different imaging parameters. Although existing holographic imaging methods can achieve large space-bandwidth-products by performing pixel super-resolution and phase retrieval sequentially, they require large amounts of data, which might be a limitation in high-speed or cost-effective imaging applications. Here we report a propagation phasor approach, which for the first time combines phase retrieval and pixel super-resolution into a unified mathematical framework and enables the synthesis of new holographic image reconstruction methods with significantly improved data efficiency. In this approach, twin image and spatial aliasing signals, along with other digital artifacts, are interpreted as noise terms that are modulated by phasors that analytically depend on the lateral displacement between hologram and sensor planes, sample-to-sensor distance, wavelength, and the illumination angle. Compared to previous holographic reconstruction techniques, this new framework results in five- to seven-fold reduced number of raw measurements, while still achieving a competitive resolution and space-bandwidth-product. We also demonstrated the success of this approach by imaging biological specimens including Papanicolaou and blood smears.
It is well known that the quantitative potential of emission computed tomography (ECT) relies on the ability to compensate for resolution, attenuation and scatter effects. Reconstruction algorithms which are able to take these effects into account are highly demanding in terms of computing resources. The reported work aimed to investigate the use of a parallel high-performance computing platform for ECT reconstruction taking into account an accurate model of the acquisition of single-photon emission tomographic (SPET) data. An iterative algorithm with an accurate model of the variable system response was ported on the MIMD (Multiple Instruction Multiple Data) parallel architecture of a 64-node Cray T3D massively parallel computer. The system was organized to make it easily accessible even from low-cost PC-based workstations through standard TCP/IP networking. A complete brain study of 30 (64 x 64) slices could be reconstructed from a set of 90 (64 x 64) projections with ten iterations of the conjugate gradients algorithm in 9 s, corresponding to an actual speed-up factor of 135. This work demonstrated the possibility of exploiting remote high-performance computing and networking resources from hospital sites by means of low-cost workstations using standard communication protocols without particular problems for routine use. The achievable speed-up factors allow the assessment of the clinical benefit of advanced reconstruction techniques which require a heavy computational burden for the compensation effects such as variable spatial resolution, scatter and attenuation. The possibility of using the same software on the same hardware platform with data acquired in different laboratories with various kinds of SPET instrumentation is appealing for software quality control and for the evaluation of the clinical impact of the reconstruction methods. (orig.). With 4 figs., 1 tab
Passeri, A. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy); Formiconi, A.R. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy); De Cristofaro, M.T.E.R. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy); Pupi, A. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy); Meldolesi, U. [Dipartimento di Fisiopatologia Clinica - Sezione di Medicina Nucleare, Universita` di Firenze (Italy)
1997-04-01
It is well known that the quantitative potential of emission computed tomography (ECT) relies on the ability to compensate for resolution, attenuation and scatter effects. Reconstruction algorithms which are able to take these effects into account are highly demanding in terms of computing resources. The reported work aimed to investigate the use of a parallel high-performance computing platform for ECT reconstruction taking into account an accurate model of the acquisition of single-photon emission tomographic (SPET) data. An iterative algorithm with an accurate model of the variable system response was ported on the MIMD (Multiple Instruction Multiple Data) parallel architecture of a 64-node Cray T3D massively parallel computer. The system was organized to make it easily accessible even from low-cost PC-based workstations through standard TCP/IP networking. A complete brain study of 30 (64 x 64) slices could be reconstructed from a set of 90 (64 x 64) projections with ten iterations of the conjugate gradients algorithm in 9 s, corresponding to an actual speed-up factor of 135. This work demonstrated the possibility of exploiting remote high-performance computing and networking resources from hospital sites by means of low-cost workstations using standard communication protocols without particular problems for routine use. The achievable speed-up factors allow the assessment of the clinical benefit of advanced reconstruction techniques which require a heavy computational burden for the compensation effects such as variable spatial resolution, scatter and attenuation. The possibility of using the same software on the same hardware platform with data acquired in different laboratories with various kinds of SPET instrumentation is appealing for software quality control and for the evaluation of the clinical impact of the reconstruction methods. (orig.). With 4 figs., 1 tab.
Accurate focal spot diagnostics based on a single shot coherent modulation imaging
A single-shot method based on coherent modulation imaging is presented for the diagnostics of the focal spot of laser facilities. The laser beam to be measured first illuminates a highly random phase plate with a known structure and subsequently the intensity of the resulting diffraction pattern is recorded by a charge-coupled device positioned behind the phase plate. Intensity distribution at the focus of the laser beam is accurately reconstructed with the coherent modulation imaging method. The feasibility of this method is demonstrated with an experiment involving a He–Ne laser. (letter)
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, Jiwon; Lee, Jung Kwon; Lee, Kyoung Mu
2015-01-01
We present a highly accurate single-image super-resolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification \\cite{simonyan2015very}. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, ho...
Interacting with image hierarchies for fast and accurate object segmentation
Beard, David V.; Eberly, David H.; Hemminger, Bradley M.; Pizer, Stephen M.; Faith, R. E.; Kurak, Charles; Livingston, Mark
1994-05-01
Object definition is an increasingly important area of medical image research. Accurate and fairly rapid object definition is essential for measuring the size and, perhaps more importantly, the change in size of anatomical objects such as kidneys and tumors. Rapid and fairly accurate object definition is essential for 3D real-time visualization including both surgery planning and Radiation oncology treatment planning. One approach to object definition involves the use of 3D image hierarchies, such as Eberly's Ridge Flow. However, the image hierarchy segmentation approach requires user interaction in selecting regions and subtrees. Further, visualizing and comprehending the anatomy and the selected portions of the hierarchy can be problematic. In this paper we will describe the Magic Crayon tool which allows a user to define rapidly and accurately various anatomical objects by interacting with image hierarchies such as those generated with Eberly's Ridge Flow algorithm as well as other 3D image hierarchies. Preliminary results suggest that fairly complex anatomical objects can be segmented in under a minute with sufficient accuracy for 3D surgery planning, 3D radiation oncology treatment planning, and similar applications. Potential modifications to the approach for improved accuracy are summarized.
A Wiener filtering based back projection algorithm for image reconstruction
In the context of computed tomography (CT), a key techniques is the image reconstruction from projection data. The filtered back projection (FBP) algorithm is commonly used in image reconstruction. Based on cause analysis of the artifacts, we propose a new image reconstruction algorithm combining the Wiener filter and FBP algorithm. The conventional FBP image reconstruction algorithm is improved by adopting Wiener filter: and artifacts in the reconstructed images are obviously reduced. Experimental results of typical flow regimes show that the improved algorithm can effectively improve the image quality. (authors)
Probabilistic image reconstruction for radio interferometers
Sutter, P M; McEwen, Jason D; Bunn, Emory F; Karakci, Ata; Korotkov, Andrei; Timbie, Peter; Tucker, Gregory S; Zhang, Le
2013-01-01
We present a novel, general-purpose method for deconvolving and denoising images from gridded radio interferometric visibilities using Bayesian inference based on a Gaussian process model. The method automatically takes into account incomplete coverage of the uv-plane and mode coupling due to the beam. Our method uses Gibbs sampling to efficiently explore the full posterior distribution of the underlying signal image given the data. We use a set of widely diverse mock images with a realistic interferometer setup and level of noise to assess the method. Compared to results from a proxy for the CLEAN method we find that in terms of RMS error and signal-to-noise ratio our approach performs better than traditional deconvolution techniques, regardless of the structure of the source image in our test suite. Our implementation scales as O(np log np), provides full statistical and uncertainty information of the reconstructed image, requires no supervision, and provides a robust, consistent framework for incorporating...
Stute, S.; Benoit, D.; Martineau, A.; Rehfeld, N. S.; Buvat, I.
2011-02-01
Positron emission tomography (PET) images suffer from low spatial resolution and signal-to-noise ratio. Accurate modelling of the effects affecting resolution within iterative reconstruction algorithms can improve the trade-off between spatial resolution and signal-to-noise ratio in PET images. In this work, we present an original approach for modelling the resolution loss introduced by physical interactions between and within the crystals of the tomograph and we investigate the impact of such modelling on the quality of the reconstructed images. The proposed model includes two components: modelling of the inter-crystal scattering and penetration (interC) and modelling of the intra-crystal count distribution (intraC). The parameters of the model were obtained using a Monte Carlo simulation of the Philips GEMINI GXL response. Modelling was applied to the raw line-of-response geometric histograms along the four dimensions and introduced in an iterative reconstruction algorithm. The impact of modelling interC, intraC or combined interC and intraC on spatial resolution, contrast recovery and noise was studied using simulated phantoms. The feasibility of modelling interC and intraC in two clinical 18F-NaF scans was also studied. Measurements on Monte Carlo simulated data showed that, without any crystal interaction modelling, the radial spatial resolution in air varied from 5.3 mm FWHM at the centre of the field-of-view (FOV) to 10 mm at 266 mm from the centre. Resolution was improved with interC modelling (from 4.4 mm in the centre to 9.6 mm at the edge), or with intraC modelling only (from 4.8 mm in the centre to 4.3 mm at the edge), and it became stationary across the FOV (4.2 mm FWHM) when combining interC and intraC modelling. This improvement in resolution yielded significant contrast enhancement, e.g. from 65 to 76% and 55.5 to 68% for a 6.35 mm radius sphere with a 3.5 sphere-to-background activity ratio at 55 and 215 mm from the centre of the FOV, respectively
Positron emission tomography (PET) images suffer from low spatial resolution and signal-to-noise ratio. Accurate modelling of the effects affecting resolution within iterative reconstruction algorithms can improve the trade-off between spatial resolution and signal-to-noise ratio in PET images. In this work, we present an original approach for modelling the resolution loss introduced by physical interactions between and within the crystals of the tomograph and we investigate the impact of such modelling on the quality of the reconstructed images. The proposed model includes two components: modelling of the inter-crystal scattering and penetration (interC) and modelling of the intra-crystal count distribution (intraC). The parameters of the model were obtained using a Monte Carlo simulation of the Philips GEMINI GXL response. Modelling was applied to the raw line-of-response geometric histograms along the four dimensions and introduced in an iterative reconstruction algorithm. The impact of modelling interC, intraC or combined interC and intraC on spatial resolution, contrast recovery and noise was studied using simulated phantoms. The feasibility of modelling interC and intraC in two clinical 18F-NaF scans was also studied. Measurements on Monte Carlo simulated data showed that, without any crystal interaction modelling, the radial spatial resolution in air varied from 5.3 mm FWHM at the centre of the field-of-view (FOV) to 10 mm at 266 mm from the centre. Resolution was improved with interC modelling (from 4.4 mm in the centre to 9.6 mm at the edge), or with intraC modelling only (from 4.8 mm in the centre to 4.3 mm at the edge), and it became stationary across the FOV (4.2 mm FWHM) when combining interC and intraC modelling. This improvement in resolution yielded significant contrast enhancement, e.g. from 65 to 76% and 55.5 to 68% for a 6.35 mm radius sphere with a 3.5 sphere-to-background activity ratio at 55 and 215 mm from the centre of the FOV, respectively
Reconstructing building mass models from UAV images
Li, Minglei
2015-07-26
We present an automatic reconstruction pipeline for large scale urban scenes from aerial images captured by a camera mounted on an unmanned aerial vehicle. Using state-of-the-art Structure from Motion and Multi-View Stereo algorithms, we first generate a dense point cloud from the aerial images. Based on the statistical analysis of the footprint grid of the buildings, the point cloud is classified into different categories (i.e., buildings, ground, trees, and others). Roof structures are extracted for each individual building using Markov random field optimization. Then, a contour refinement algorithm based on pivot point detection is utilized to refine the contour of patches. Finally, polygonal mesh models are extracted from the refined contours. Experiments on various scenes as well as comparisons with state-of-the-art reconstruction methods demonstrate the effectiveness and robustness of the proposed method.
A two-step Hilbert transform method for 2D image reconstruction
Noo, Frederic; Clackdoyle, Rolf; Pack, Jed D [UCAIR, Department of Radiology, University of Utah, UT (United States)
2004-09-07
The paper describes a new accurate two-dimensional (2D) image reconstruction method consisting of two steps. In the first step, the backprojected image is formed after taking the derivative of the parallel projection data. In the second step, a Hilbert filtering is applied along certain lines in the differentiated backprojection (DBP) image. Formulae for performing the DBP step in fan-beam geometry are also presented. The advantage of this two-step Hilbert transform approach is that in certain situations, regions of interest (ROIs) can be reconstructed from truncated projection data. Simulation results are presented that illustrate very similar reconstructed image quality using the new method compared to standard filtered backprojection, and that show the capability to correctly handle truncated projections. In particular, a simulation is presented of a wide patient whose projections are truncated laterally yet for which highly accurate ROI reconstruction is obtained.
A two-step Hilbert transform method for 2D image reconstruction
The paper describes a new accurate two-dimensional (2D) image reconstruction method consisting of two steps. In the first step, the backprojected image is formed after taking the derivative of the parallel projection data. In the second step, a Hilbert filtering is applied along certain lines in the differentiated backprojection (DBP) image. Formulae for performing the DBP step in fan-beam geometry are also presented. The advantage of this two-step Hilbert transform approach is that in certain situations, regions of interest (ROIs) can be reconstructed from truncated projection data. Simulation results are presented that illustrate very similar reconstructed image quality using the new method compared to standard filtered backprojection, and that show the capability to correctly handle truncated projections. In particular, a simulation is presented of a wide patient whose projections are truncated laterally yet for which highly accurate ROI reconstruction is obtained
A two-step Hilbert transform method for 2D image reconstruction.
Noo, Frédéric; Clackdoyle, Rolf; Pack, Jed D
2004-09-01
The paper describes a new accurate two-dimensional (2D) image reconstruction method consisting of two steps. In the first step, the backprojected image is formed after taking the derivative of the parallel projection data. In the second step, a Hilbert filtering is applied along certain lines in the differentiated backprojection (DBP) image. Formulae for performing the DBP step in fanbeam geometry are also presented. The advantage of this two-step Hilbert transform approach is that in certain situations, regions of interest (ROIs) can be reconstructed from truncated projection data. Simulation results are presented that illustrate very similar reconstructed image quality using the new method compared to standard filtered backprojection, and that show the capability to correctly handle truncated projections. In particular, a simulation is presented of a wide patient whose projections are truncated laterally yet for which highly accurate ROI reconstruction is obtained. PMID:15470913
Deep Reconstruction Models for Image Set Classification.
Hayat, Munawar; Bennamoun, Mohammed; An, Senjian
2015-04-01
Image set classification finds its applications in a number of real-life scenarios such as classification from surveillance videos, multi-view camera networks and personal albums. Compared with single image based classification, it offers more promises and has therefore attracted significant research attention in recent years. Unlike many existing methods which assume images of a set to lie on a certain geometric surface, this paper introduces a deep learning framework which makes no such prior assumptions and can automatically discover the underlying geometric structure. Specifically, a Template Deep Reconstruction Model (TDRM) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The initialized TDRM is then separately trained for images of each class and class-specific DRMs are learnt. Based on the minimum reconstruction errors from the learnt class-specific models, three different voting strategies are devised for classification. Extensive experiments are performed to demonstrate the efficacy of the proposed framework for the tasks of face and object recognition from image sets. Experimental results show that the proposed method consistently outperforms the existing state of the art methods. PMID:26353289
Chen, Yujia; Wang, Kun; Gursoy, Doga; Soriano, Carmen; De Carlo, Francesco; Anastasio, Mark A.
2016-03-01
Propagation-based X-ray phase-contrast tomography (XPCT) provides the opportunity to image weakly absorbing objects and is being explored actively for a variety of important pre-clinical applications. Quantitative XPCT image reconstruction methods typically involve a phase retrieval step followed by application of an image reconstruction algorithm. Most approaches to phase retrieval require either acquiring multiple images at different object-to-detector distances or introducing simplifying assumptions, such as a single-material assumption, to linearize the imaging model. In order to overcome these limitations, a non-linear image reconstruction method has been proposed previously that jointly estimates the absorption and refractive properties of an object from XPCT projection data acquired at a single propagation distance, without the need to linearize the imaging model. However, the numerical properties of the associated non-convex optimization problem remain largely unexplored. In this study, computer simulations are conducted to investigate the feasibility of the joint reconstruction problem in practice. We demonstrate that the joint reconstruction problem is ill-posed and sensitive to system inconsistencies. Particularly, the method can generate accurate refractive index images only if the object is thin and has no phase-wrapping in the data. However, we also observed that, for weakly absorbing objects, the refractive index images reconstructed by the joint reconstruction method are, in general, more accurate than those reconstructed using methods that simply ignore the object's absorption.
Image Reconstruction from 2D stack of MRI/CT to 3D using Shapelets
Arathi T
2014-12-01
Full Text Available Image reconstruction is an active research field, due to the increasing need for geometric 3D models in movie industry, games, virtual environments and in medical fields. 3D image reconstruction aims to arrive at the 3D model of an object, from its 2D images taken at different viewing angles. Medical images are multimodal, which includes MRI, CT scan image, PET and SPECT images. Of these, MRI and CT scan images of an organ when taken, is available as a stack of 2D images, taken at different angles. This 2D stack of images is used to get a 3D view of the organ of interest, to aid doctors in easier diagnosis. Existing 3D reconstruction techniques are voxel based techniques, which tries to reconstruct the 3D view based on the intensity value stored at each voxel location. These techniques don’t make use of the shape/depth information available in the 2D image stack. In this work, a 3D reconstruction technique for MRI/CT 2D image stack, based on Shapelets has been proposed. Here, the shape/depth information available in each 2D image in the image stack is manipulated to get a 3D reconstruction, which gives a more accurate 3D view of the organ of interest. Experimental results exhibit the efficiency of this proposed technique.
MR imaging of the reconstructed breast
The etiology of pain is difficult to evaluate in a reconstructed breast because of capsular contracture and compression of tissue. MR images were obtained in 45 patients with a variety of pulse techniques. In hands, STIR (short inversion recovery) imaging was the most sensitive sequence. Suppression of the fat signal and increased contrast between normal and pathologic tissue, coupled with the synergistic effects of prolonged T1 and T2, were a major practical advantage. MR imaging can demonstrate recurrent tumor involving the chest wall, internal mammary nodes and rupture of the implant bag. The authors believe MR imaging can play a strong supplementary role in determining the etiology of pain in a postmastectomy patient
Image-reconstruction methods in positron tomography
Townsend, David W; CERN. Geneva
1993-01-01
Physics and mathematics for medical imaging\tIn the two decades since the introduction of the X-ray scanner into radiology, medical imaging techniques have become widely established as essential tools in the diagnosis of disease. As a consequence of recent technological and mathematical advances, the non-invasive, three-dimensional imaging of internal organs such as the brain and the heart is now possible, not only for anatomical investigations using X-rays but also for studies which explore the functional status of the body using positron-emitting radioisotopes and nuclear magnetic resonance. Mathematical methods which enable three-dimentional distributions to be reconstructed from projection data acquired by radiation detectors suitably positioned around the patient will be described in detail. The lectures will trace the development of medical imaging from simpleradiographs to the present-day non-invasive measurement of in vivo boichemistry. Powerful techniques to correlate anatomy and function that are cur...
Fully 3D PET image reconstruction with a 4D sinogram blurring kernel
Tohme, Michel S.; Qi, Jinyi [California Univ., Davis, CA (United States). Dept. of Biomedical Engineering; Zhou, Jian
2011-07-01
Accurately modeling PET system response is essential for high-resolution image reconstruction. Traditionally, sinogram blurring effects are modeled as a 2D blur in each sinogram plane. Such 2D blurring kernel is insufficient for fully 3D PET data, which has four dimensions. In this paper, we implement a fully 3D PET image reconstruction using a 4D sinogram blurring kernel estimated from point source scans and perform phantom experiments to evaluate the improvements in image quality over methods with existing 2D blurring kernels. The results show that the proposed reconstruction method can achieve better spatial resolution and contrast recovery than existing methods. (orig.)
Research about Three Dimensional Reconstruction of Medical Image
Qiechun Chen
2013-02-01
Full Text Available In this paper, through comparison of different reconstruction algorithms for volume rendering, we put forward Ray Casting algorithm as the scheme of 3D reconstruction of medical image. We improved the image synthesis operator, and combined section sampling mode to reconstruct the image. Finally we rendered images on GPU. By using improved operator, we not only made the rendering speed accelerated, but also made the quality of rendering images improved.
Research about Three Dimensional Reconstruction of Medical Image
Qiechun Chen; Guangyuan Zhang; Xin Wang; Linan Fan
2013-01-01
In this paper, through comparison of different reconstruction algorithms for volume rendering, we put forward Ray Casting algorithm as the scheme of 3D reconstruction of medical image. We improved the image synthesis operator, and combined section sampling mode to reconstruct the image. Finally we rendered images on GPU. By using improved operator, we not only made the rendering speed accelerated, but also made the quality of rendering images improved.
Portable and accurate 3D scanner for breast implant design and reconstructive plastic surgery
Rigotti, Camilla; Borghese, Nunzio A.; Ferrari, Stefano; Baroni, Guido; Ferrigno, Giancarlo
1998-06-01
In order to evaluate the proper breast implant, the surgeon relies on a standard set of measurements manually taken on the subject. This approach does not allow to obtain an accurate reconstruction of the breast shape and asymmetries can easily arise after surgery. The purpose of this work is to present a method which can help the surgeon in the choice of the shape and dimensions of a prosthesis allowing for a perfect symmetry between the prosthesis and the controlateral breast and can be used as a 3D visual feedback in plastic surgery.
Image reconstruction by NMR Fresnel diffractive imaging technique
A new approach to MR angiography, the NMR diffractive imaging technique, has been investigated. The expression for NMR signals obtained in the NMR diffractive imaging technique is similar to the equation for Fresnel diffraction in light waves or sound waves. Therefore, it is possible to reconstruct an image focusing on any plane in the depth direction from data scanned two-dimensionally by changing an imaging parameter in the reconstruction step. To support this imaging technique, a coil system composed of six coils was designed. Experiments were performed using an ultra-low-field MRI scanner to acquire two-dimensional data in the proposed technique. Even though blurred images outside the focus are superimposed on the image in the focal plane, the three-dimensional distribution of the object can be recognized by moving the focal plane in the depth direction. To obtain supplemental information for the object, acquiring images from different angles is helpful for recognizing the spatial distribution of the object more precisely. Although the image obtained contains blurred images outside the focus the proposed imaging technique is expected to be useful in MR fast angiography. (author)
Adaptive Kaczmarz method for image reconstruction in electrical impedance tomography
We present an adaptive Kaczmarz method for solving the inverse problem in electrical impedance tomography and determining the conductivity distribution inside an object from electrical measurements made on the surface. To best characterize an unknown conductivity distribution and avoid inverting the Jacobian-related term JTJ which could be expensive in terms of computation cost and memory in large-scale problems, we propose solving the inverse problem by applying the optimal current patterns for distinguishing the actual conductivity from the conductivity estimate between each iteration of the block Kaczmarz algorithm. With a novel subset scheme, the memory-efficient reconstruction algorithm which appropriately combines the optimal current pattern generation with the Kaczmarz method can produce more accurate and stable solutions adaptively as compared to traditional Kaczmarz- and Gauss–Newton-type methods. Choices of initial current pattern estimates are discussed in this paper. Several reconstruction image metrics are used to quantitatively evaluate the performance of the simulation results. (paper)
A sparse reconstruction algorithm for ultrasonic images in nondestructive testing.
Guarneri, Giovanni Alfredo; Pipa, Daniel Rodrigues; Neves Junior, Flávio; de Arruda, Lúcia Valéria Ramos; Zibetti, Marcelo Victor Wüst
2015-01-01
Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, ω-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan-about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR). PMID:25905700
A Sparse Reconstruction Algorithm for Ultrasonic Images in Nondestructive Testing
Giovanni Alfredo Guarneri
2015-04-01
Full Text Available Ultrasound imaging systems (UIS are essential tools in nondestructive testing (NDT. In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, !-k SAFT and regularized least squares (RLS. The method demonstrates significant resolution improvement when compared with B-scan—about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR.
Automated Image-Based Procedures for Accurate Artifacts 3D Modeling and Orthoimage Generation
Marc Pierrot-Deseilligny
2011-12-01
Full Text Available The accurate 3D documentation of architectures and heritages is getting very common and required in different application contexts. The potentialities of the image-based approach are nowadays very well-known but there is a lack of reliable, precise and flexible solutions, possibly open-source, which could be used for metric and accurate documentation or digital conservation and not only for simple visualization or web-based applications. The article presents a set of photogrammetric tools developed in order to derive accurate 3D point clouds and orthoimages for the digitization of archaeological and architectural objects. The aim is also to distribute free solutions (software, methodologies, guidelines, best practices, etc. based on 3D surveying and modeling experiences, useful in different application contexts (architecture, excavations, museum collections, heritage documentation, etc. and according to several representations needs (2D technical documentation, 3D reconstruction, web visualization, etc..
Rumple, Christopher; Krane, Michael; Richter, Joseph; Craven, Brent
2013-11-01
The mammalian nose is a multi-purpose organ that houses a convoluted airway labyrinth responsible for respiratory air conditioning, filtering of environmental contaminants, and chemical sensing. Because of the complexity of the nasal cavity, the anatomy and function of these upper airways remain poorly understood in most mammals. However, recent advances in high-resolution medical imaging, computational modeling, and experimental flow measurement techniques are now permitting the study of respiratory airflow and olfactory transport phenomena in anatomically-accurate reconstructions of the nasal cavity. Here, we focus on efforts to manufacture an anatomically-accurate transparent model for stereoscopic particle image velocimetry (SPIV) measurements. Challenges in the design and manufacture of an index-matched anatomical model are addressed. PIV measurements are presented, which are used to validate concurrent computational fluid dynamics (CFD) simulations of mammalian nasal airflow. Supported by the National Science Foundation.
Fast and Accurate Brain Image Retrieval Using Gabor Wavelet Algorithm
J.Esther
2014-01-01
Full Text Available CBIR in medical image databases are used to assist physician in diagnosis the diseases and also used to aid diagnosis by identifying similar past cases. In order to retrieve a fast, accurate and an effective similarity of images from the large data set. The pre-processing step is extraction of brain. It removes the unwanted non-brain areas like scalp, skull, neck, eyes, ear etc from the MRI Head scan images. After removing the unwanted areas of non-brain region, it is very effective to retrieve the similar images. In this paper it is proposed a brain extraction technique using fuzzy morphological operators. For the experimental results 1200 MRI images are taken from scan centre and some brain images are collected from web and these have been implemented with popular brain extraction algorithm of Graph- Cut Algorithm (GCUT and Expectation Maximization algorithm (EMA. The experiment result shows that the proposed algorithm fuzzy morphological operator algorithm (FMOA is prompting the best promising results. Using this FMOA result retrieved the brain image from the large collection of databases using Gabor-Wavelet Transform.
Nonlinear dual reconstruction of SPECT activity and attenuation images.
Liu, Huafeng; Guo, Min; Hu, Zhenghui; Shi, Pengcheng; Hu, Hongjie
2014-01-01
In single photon emission computed tomography (SPECT), accurate attenuation maps are needed to perform essential attenuation compensation for high quality radioactivity estimation. Formulating the SPECT activity and attenuation reconstruction tasks as coupled signal estimation and system parameter identification problems, where the activity distribution and the attenuation parameter are treated as random variables with known prior statistics, we present a nonlinear dual reconstruction scheme based on the unscented Kalman filtering (UKF) principles. In this effort, the dynamic changes of the organ radioactivity distribution are described through state space evolution equations, while the photon-counting SPECT projection data are measured through the observation equations. Activity distribution is then estimated with sub-optimal fixed attenuation parameters, followed by attenuation map reconstruction given these activity estimates. Such coupled estimation processes are iteratively repeated as necessary until convergence. The results obtained from Monte Carlo simulated data, physical phantom, and real SPECT scans demonstrate the improved performance of the proposed method both from visual inspection of the images and a quantitative evaluation, compared to the widely used EM-ML algorithms. The dual estimation framework has the potential to be useful for estimating the attenuation map from emission data only and thus benefit the radioactivity reconstruction. PMID:25225796
Diwakar, S. V.; Das, Sarit K.; Sundararajan, T.
2009-12-01
A new Quadratic Spline based Interface (QUASI) reconstruction algorithm is presented which provides an accurate and continuous representation of the interface in a multiphase domain and facilitates the direct estimation of local interfacial curvature. The fluid interface in each of the mixed cells is represented by piecewise parabolic curves and an initial discontinuous PLIC approximation of the interface is progressively converted into a smooth quadratic spline made of these parabolic curves. The conversion is achieved by a sequence of predictor-corrector operations enforcing function ( C0) and derivative ( C1) continuity at the cell boundaries using simple analytical expressions for the continuity requirements. The efficacy and accuracy of the current algorithm has been demonstrated using standard test cases involving reconstruction of known static interface shapes and dynamically evolving interfaces in prescribed flow situations. These benchmark studies illustrate that the present algorithm performs excellently as compared to the other interface reconstruction methods available in literature. Quadratic rate of error reduction with respect to grid size has been observed in all the cases with curved interface shapes; only in situations where the interface geometry is primarily flat, the rate of convergence becomes linear with the mesh size. The flow algorithm implemented in the current work is designed to accurately balance the pressure gradients with the surface tension force at any location. As a consequence, it is able to minimize spurious flow currents arising from imperfect normal stress balance at the interface. This has been demonstrated through the standard test problem of an inviscid droplet placed in a quiescent medium. Finally, the direct curvature estimation ability of the current algorithm is illustrated through the coupled multiphase flow problem of a deformable air bubble rising through a column of water.
A new Level-set based Protocol for Accurate Bone Segmentation from CT Imaging
Pinheiro, Manuel
2015-01-01
In this work it is proposed a medical image segmentation pipeline for accurate bone segmentation from CT imaging. It is a two-step methodology, with a pre-segmentation step and a segmentation refinement step. First, the user performs a rough segmenting of the desired region of interest. Next, a fully automatic refinement step is applied to the pre-segmented data. The automatic segmentation refinement is composed by several sub-stpng, namely image deconvolution, image cropping and interpolation. The user-defined pre-segmentation is then refined over the deconvolved, cropped, and up-sampled version of the image. The algorithm is applied in the segmentation of CT images of a composite femur bone, reconstructed with different reconstruction protocols. Segmentation outcomes are validated against a gold standard model obtained with coordinate measuring machine Nikon Metris LK V20 with a digital line scanner LC60-D that guarantees an accuracy of 28 $\\mu m$. High sub-pixel accuracy models were obtained for all tested...
Three dimensional reconstruction of continuous ICT images by MATLAB
The three dimensional (3D) reconstruction of industrial computed tomography (ICT) images plays an important role in non-destructive test (NDT). In this paper, the 3D reconstruction work of a serial of continuous ICT images was done by MATLAB. The reconstructed cubic image was good. The inner structures of the object can be watched by divided and transparently displayed images. And more information of the detected object can be achieved. (authors)
Tscharf, A.; Rumpler, M.; Fraundorfer, F.; Mayer, G.; Bischof, H.
2015-08-01
During the last decades photogrammetric computer vision systems have been well established in scientific and commercial applications. Especially the increasing affordability of unmanned aerial vehicles (UAVs) in conjunction with automated multi-view processing pipelines have resulted in an easy way of acquiring spatial data and creating realistic and accurate 3D models. With the use of multicopter UAVs, it is possible to record highly overlapping images from almost terrestrial camera positions to oblique and nadir aerial images due to the ability to navigate slowly, hover and capture images at nearly any possible position. Multi-copter UAVs thus are bridging the gap between terrestrial and traditional aerial image acquisition and are therefore ideally suited to enable easy and safe data collection and inspection tasks in complex or hazardous environments. In this paper we present a fully automated processing pipeline for precise, metric and geo-accurate 3D reconstructions of complex geometries using various imaging platforms. Our workflow allows for georeferencing of UAV imagery based on GPS-measurements of camera stations from an on-board GPS receiver as well as tie and control point information. Ground control points (GCPs) are integrated directly in the bundle adjustment to refine the georegistration and correct for systematic distortions of the image block. We discuss our approach based on three different case studies for applications in mining and archaeology and present several accuracy related analyses investigating georegistration, camera network configuration and ground sampling distance. Our approach is furthermore suited for seamlessly matching and integrating images from different view points and cameras (aerial and terrestrial as well as inside views) into one single reconstruction. Together with aerial images from a UAV, we are able to enrich 3D models by combining terrestrial images as well inside views of an object by joint image processing to
Sidky, Emil Y; Anastasio, Mark A; Pan, Xiaochuan
2010-05-10
Propagation-based X-ray phase-contrast tomography (PCT) seeks to reconstruct information regarding the complex-valued refractive index distribution of an object. In many applications, a boundary-enhanced image is sought that reveals the locations of discontinuities in the real-valued component of the refractive index distribution. We investigate two iterative algorithms for few-view image reconstruction in boundary-enhanced PCT that exploit the fact that a boundary-enhanced PCT image, or its gradient, is often sparse. In order to exploit object sparseness, the reconstruction algorithms seek to minimize the l(1)-norm or TV-norm of the image, subject to data consistency constraints. We demonstrate that the algorithms can reconstruct accurate boundary-enhanced images from highly incomplete few-view projection data. PMID:20588896
Three-dimensional reconstruction of laser-imploded targets from simulated pinhole images.
Xu, Peng; Bai, Yonglin; Bai, Xiaohong; Liu, Baiyu; Ouyang, Xian; Wang, Bo; Yang, Wenzheng; Gou, Yongsheng; Zhu, Bingli; Qin, Junjun
2012-11-10
This paper proposes an integral method to achieve a more accurate weighting matrix that makes very positive contributions to the image reconstruction in inertial confinement fusion research. Standard algebraic reconstruction techniques with a positivity constraint included are utilized. The final normalized mean-square error between the simulated and reconstructed projection images is 0.000365%, which is a nearly perfect result, indicating that the weighting matrix is very important. Compared with the error between the simulated and reconstructed phantoms, which is 2.35%, it seems that the improvement of the accuracy of the projection image does not mean the improvement of the phantom. The proposed method can reconstruct a simulated laser-imploded target consisting of 100×100×100 voxels. PMID:23142895
Prior image constrained image reconstruction in emerging computed tomography applications
Brunner, Stephen T.
Advances have been made in computed tomography (CT), especially in the past five years, by incorporating prior images into the image reconstruction process. In this dissertation, we investigate prior image constrained image reconstruction in three emerging CT applications: dual-energy CT, multi-energy photon-counting CT, and cone-beam CT in image-guided radiation therapy. First, we investigate the application of Prior Image Constrained Compressed Sensing (PICCS) in dual-energy CT, which has been called "one of the hottest research areas in CT." Phantom and animal studies are conducted using a state-of-the-art 64-slice GE Discovery 750 HD CT scanner to investigate the extent to which PICCS can enable radiation dose reduction in material density and virtual monochromatic imaging. Second, we extend the application of PICCS from dual-energy CT to multi-energy photon-counting CT, which has been called "one of the 12 topics in CT to be critical in the next decade." Numerical simulations are conducted to generate multiple energy bin images for a photon-counting CT acquisition and to investigate the extent to which PICCS can enable radiation dose efficiency improvement. Third, we investigate the performance of a newly proposed prior image constrained scatter correction technique to correct scatter-induced shading artifacts in cone-beam CT, which, when used in image-guided radiation therapy procedures, can assist in patient localization, and potentially, dose verification and adaptive radiation therapy. Phantom studies are conducted using a Varian 2100 EX system with an on-board imager to investigate the extent to which the prior image constrained scatter correction technique can mitigate scatter-induced shading artifacts in cone-beam CT. Results show that these prior image constrained image reconstruction techniques can reduce radiation dose in dual-energy CT by 50% in phantom and animal studies in material density and virtual monochromatic imaging, can lead to radiation
SU-E-I-73: Clinical Evaluation of CT Image Reconstructed Using Interior Tomography
Purpose: Radiation dose reduction has been a long standing challenge in CT imaging of obese patients. Recent advances in interior tomography (reconstruction of an interior region of interest (ROI) from line integrals associated with only paths through the ROI) promise to achieve significant radiation dose reduction without compromising image quality. This study is to investigate the application of this technique in CT imaging through evaluating imaging quality reconstructed from patient data. Methods: Projection data were directly obtained from patients who had CT examinations in a Dual Source CT scanner (DSCT). Two detectors in a DSCT acquired projection data simultaneously. One detector provided projection data for full field of view (FOV, 50 cm) while another detectors provided truncated projection data for a FOV of 26 cm. Full FOV CT images were reconstructed using both filtered back projection and iterative algorithm; while interior tomography algorithm was implemented to reconstruct ROI images. For comparison reason, FBP was also used to reconstruct ROI images. Reconstructed CT images were evaluated by radiologists and compared with images from CT scanner. Results: The results show that the reconstructed ROI image was in excellent agreement with the truth inside the ROI, obtained from images from CT scanner, and the detailed features in the ROI were quantitatively accurate. Radiologists evaluation shows that CT images reconstructed with interior tomography met diagnosis requirements. Radiation dose may be reduced up to 50% using interior tomography, depending on patient size. Conclusion: This study shows that interior tomography can be readily employed in CT imaging for radiation dose reduction. It may be especially useful in imaging obese patients, whose subcutaneous tissue is less clinically relevant but may significantly increase radiation dose
Model-based reconstruction for illumination variation in face images
B. J. Boom; L. J. Spreeuwers; Veldhuis, R.N.J.
2008-01-01
We propose a novel method to correct for arbitrary illumination variation in the face images. The main purpose is to improve recognition results of face images taken under uncontrolled illumination conditions. We correct the illumination variation in the face images using a face shape model, which allows us to estimate the face shape in the face image. Using this face shape, we can reconstruct a face image under frontal illumination. These reconstructed images improve the results in face iden...
Optimized Quasi-Interpolators for Image Reconstruction.
Sacht, Leonardo; Nehab, Diego
2015-12-01
We propose new quasi-interpolators for the continuous reconstruction of sampled images, combining a narrowly supported piecewise-polynomial kernel and an efficient digital filter. In other words, our quasi-interpolators fit within the generalized sampling framework and are straightforward to use. We go against standard practice and optimize for approximation quality over the entire Nyquist range, rather than focusing exclusively on the asymptotic behavior as the sample spacing goes to zero. In contrast to previous work, we jointly optimize with respect to all degrees of freedom available in both the kernel and the digital filter. We consider linear, quadratic, and cubic schemes, offering different tradeoffs between quality and computational cost. Experiments with compounded rotations and translations over a range of input images confirm that, due to the additional degrees of freedom and the more realistic objective function, our new quasi-interpolators perform better than the state of the art, at a similar computational cost. PMID:26390452
How accurately can digital images depict conventional radiographs
The purpose of this paper is to investigate how accurately the video image of a digitized chest radiograph can depict normal anatomic configurations of thoracic organs seen on a conventional radiograph. These configurations are important to diagnosis of diseases of the chest. Chest radiographs of 50 individuals diagnosed as normal were analyzed. Three chest physicians and one radiologist reviewed 50 pairs of digitized images (digitized in 0.125-mm pixel size, 10-bit gray scale, displayed on 1,024 x 1.536, 8-bit gray scale) constructed and conventional films. The visibility of eight structures (spinal process, trachea, right and left main bronchus, anterior tip of right fourth rib, vessels behind diaphragm and cardiac shadow, and descending aorta behind heart) was graded into five levels of confidence
Modeling of polychromatic attenuation using computed tomography reconstructed images
Yan, C. H.; Whalen, R. T.; Beaupre, G. S.; Yen, S. Y.; Napel, S.
1999-01-01
This paper presents a procedure for estimating an accurate model of the CT imaging process including spectral effects. As raw projection data are typically unavailable to the end-user, we adopt a post-processing approach that utilizes the reconstructed images themselves. This approach includes errors from x-ray scatter and the nonidealities of the built-in soft tissue correction into the beam characteristics, which is crucial to beam hardening correction algorithms that are designed to be applied directly to CT reconstructed images. We formulate this approach as a quadratic programming problem and propose two different methods, dimension reduction and regularization, to overcome ill conditioning in the model. For the regularization method we use a statistical procedure, Cross Validation, to select the regularization parameter. We have constructed step-wedge phantoms to estimate the effective beam spectrum of a GE CT-I scanner. Using the derived spectrum, we computed the attenuation ratios for the wedge phantoms and found that the worst case modeling error is less than 3% of the corresponding attenuation ratio. We have also built two test (hybrid) phantoms to evaluate the effective spectrum. Based on these test phantoms, we have shown that the effective beam spectrum provides an accurate model for the CT imaging process. Last, we used a simple beam hardening correction experiment to demonstrate the effectiveness of the estimated beam profile for removing beam hardening artifacts. We hope that this estimation procedure will encourage more independent research on beam hardening corrections and will lead to the development of application-specific beam hardening correction algorithms.
Image reconstruction design of industrial CT instrument for teaching
Industrial CT instrument for teaching is applied to teaching and study in field of physics and radiology major, image reconstruction is an important part of software on CT instrument. The paper expatiate on CT physical theory and first generation CT reconstruction algorithm, describe scan process of industrial CT instrument for teaching; analyze image artifact as result of displacement of rotation center, implement method of center displacement correcting, design and complete image reconstruction software, application shows that reconstructed image is very clear and qualitatively high. (authors)
Visual image reconstruction from human brain activity: A modular decoding approach
Brain activity represents our perceptual experience. But the potential for reading out perceptual contents from human brain activity has not been fully explored. In this study, we demonstrate constraint-free reconstruction of visual images perceived by a subject, from the brain activity pattern. We reconstructed visual images by combining local image bases with multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant voxels and exploiting their correlated patterns. Binary-contrast, 10 x 10-patch images (2100 possible states), were accurately reconstructed without any image prior by measuring brain activity only for several hundred random images. The results suggest that our approach provides an effective means to read out complex perceptual states from brain activity while discovering information representation in multi-voxel patterns.
Gibbs artifact reduction for POCS super-resolution image reconstruction
Chuangbai XIAO; Jing YU; Kaina SU
2008-01-01
The topic of super-resolution image reconstruc-tion has recently received considerable attention among the research community. Super-resolution image reconstruc-tion methods attempt to create a single high-resolution image from a number of low-resolution images (or a video sequence). The method of projections onto convex sets (POCS) for super-resolution image reconstruction attracts many researchers' attention. In this paper, we propose an improvement to reduce the amount of Gibbs artifacts pre-senting on the edges of the high-resolution image recon-structed by the POCS method. The proposed method weights the blur PSF centered at an edge pixel with an exponential function, and consequently decreases the coef-ficients of the PSF in the direction orthogonal to the edge. Experiment results show that the modification reduces effectively the visibility of Gibbs artifacts on edges and improves obviously the quality of the reconstructed high-resolution image.
Noo, Frederic; Defrise, Michel; Pack, Jed; Clackdoyle, Rolf
2007-01-01
We present a mathematical analysis of the problem of image reconstruction from truncated data in two-dimensional (2D) single-photon emission computed tomography (SPECT). Recent results in classical tomography have shown that accurate reconstruction of some parts of the object is possible in the presence of truncation. We have investigated how these results extend to 2D parallel-beam SPECT, assuming that the attenuation map is known and constant in a convex region $\\Omega$ that includes all ac...
Improvement of reconstructed image quality of neutron computed tomography
Neutron computed tomography has been studied. Endeavor has been given to obtain high image quality of CT reconstruction. Film method is comparatively preferred to dynamic neutron TV one. Some models for nuclear fuels have been reconstructed. Dispersion of 300 μm Eu-particles in TiO2 pellets, which simulate PuO2/UO2 nuclear fuel, have been reconstructed
Optimization of CT image reconstruction algorithms for the lung tissue research consortium (LTRC)
McCollough, Cynthia; Zhang, Jie; Bruesewitz, Michael; Bartholmai, Brian
2006-03-01
To create a repository of clinical data, CT images and tissue samples and to more clearly understand the pathogenetic features of pulmonary fibrosis and emphysema, the National Heart, Lung, and Blood Institute (NHLBI) launched a cooperative effort known as the Lung Tissue Resource Consortium (LTRC). The CT images for the LTRC effort must contain accurate CT numbers in order to characterize tissues, and must have high-spatial resolution to show fine anatomic structures. This study was performed to optimize the CT image reconstruction algorithms to achieve these criteria. Quantitative analyses of phantom and clinical images were conducted. The ACR CT accreditation phantom containing five regions of distinct CT attenuations (CT numbers of approximately -1000 HU, -80 HU, 0 HU, 130 HU and 900 HU), and a high-contrast spatial resolution test pattern, was scanned using CT systems from two manufacturers (General Electric (GE) Healthcare and Siemens Medical Solutions). Phantom images were reconstructed using all relevant reconstruction algorithms. Mean CT numbers and image noise (standard deviation) were measured and compared for the five materials. Clinical high-resolution chest CT images acquired on a GE CT system for a patient with diffuse lung disease were reconstructed using BONE and STANDARD algorithms and evaluated by a thoracic radiologist in terms of image quality and disease extent. The clinical BONE images were processed with a 3 x 3 x 3 median filter to simulate a thicker slice reconstructed in smoother algorithms, which have traditionally been proven to provide an accurate estimation of emphysema extent in the lungs. Using a threshold technique, the volume of emphysema (defined as the percentage of lung voxels having a CT number lower than -950 HU) was computed for the STANDARD, BONE, and BONE filtered. The CT numbers measured in the ACR CT Phantom images were accurate for all reconstruction kernels for both manufacturers. As expected, visual evaluation of the
Image reconstruction for field-focusing capacitance imaging
For online monitoring of multi-phase flows of non-conductive materials, a field-focusing capacitance imaging system has been developed. With a field focusing capacitance sensor, a tomographic image based on capacitance measurements is used directly to map the material distribution. However, it is difficult to achieve a required accuracy for measurement of void fraction from the capacitance image, because of the soft-field effect of the capacitance sensor. In this paper, the forward and inverse problems with a field-focusing capacitance sensor are described. Simulation and experimental results show that deconvolution-based algorithms can reduce the blurring artefact and can reconstruct an image close to the original distribution
Reconstruction from gamma radiography and ultrasonic images
This work deals with the three-dimensional reconstruction from gamma radiographic and ultrasonic images. Such an issue belongs to the field of data fusion since the data provide complementary information. The two sets of data are independently related to two sets of parameters: gamma ray attenuation and ultrasonic reflectivity. The fusion problem is addressed in a Bayesian framework; the kingpin of the task is then to define a joint a priori model for both attenuation and reflectivity. Thus, the developing of this model and the entailed joint estimation constitute the principal contribution of this work. The results of real data treatments demonstrate the validity of this method as compared to a sequential approach of the two sets of data
Photogrammetric 3D reconstruction using mobile imaging
Fritsch, Dieter; Syll, Miguel
2015-03-01
In our paper we demonstrate the development of an Android Application (AndroidSfM) for photogrammetric 3D reconstruction that works on smartphones and tablets likewise. The photos are taken with mobile devices, and can thereafter directly be calibrated using standard calibration algorithms of photogrammetry and computer vision, on that device. Due to still limited computing resources on mobile devices, a client-server handshake using Dropbox transfers the photos to the sever to run AndroidSfM for the pose estimation of all photos by Structure-from-Motion and, thereafter, uses the oriented bunch of photos for dense point cloud estimation by dense image matching algorithms. The result is transferred back to the mobile device for visualization and ad-hoc on-screen measurements.
鞍形CT的感兴趣区图像重建%ROI-image Reconstruction for a Saddle Trajectory
夏丹; 余立锋; 邹宇
2005-01-01
Recently, we have developed a general formula for 3D cone-beam CT reconstruction, which can accommondate general, smooth trajectories. From the formula, algorithms can be derived for image reconstruction within a region of interest (ROI) from truncated data. In this work, we apply the derived backprojection filteration (BPF) algorithm and the minimum-data filtered backprojection (MD-FBP) algorithm to reconstructing ROI images from cone-beam projection data acquired with a saddle trajec-tory. Our numerical results in these studies demonstrate that the BPF and MD-FBP algorithms can accurately reconstruct ROI images from truncated data.
Total variation minimization-based multimodality medical image reconstruction
Cui, Xuelin; Yu, Hengyong; Wang, Ge; Mili, Lamine
2014-09-01
Since its recent inception, simultaneous image reconstruction for multimodality fusion has received a great deal of attention due to its superior imaging performance. On the other hand, the compressed sensing (CS)-based image reconstruction methods have undergone a rapid development because of their ability to significantly reduce the amount of raw data. In this work, we combine computed tomography (CT) and magnetic resonance imaging (MRI) into a single CS-based reconstruction framework. From a theoretical viewpoint, the CS-based reconstruction methods require prior sparsity knowledge to perform reconstruction. In addition to the conventional data fidelity term, the multimodality imaging information is utilized to improve the reconstruction quality. Prior information in this context is that most of the medical images can be approximated as piecewise constant model, and the discrete gradient transform (DGT), whose norm is the total variation (TV), can serve as a sparse representation. More importantly, the multimodality images from the same object must share structural similarity, which can be captured by DGT. The prior information on similar distributions from the sparse DGTs is employed to improve the CT and MRI image quality synergistically for a CT-MRI scanner platform. Numerical simulation with undersampled CT and MRI datasets is conducted to demonstrate the merits of the proposed hybrid image reconstruction approach. Our preliminary results confirm that the proposed method outperforms the conventional CT and MRI reconstructions when they are applied separately.
Model-Based Reconstructive Elasticity Imaging Using Ultrasound
Salavat R. Aglyamov
2007-01-01
Full Text Available Elasticity imaging is a reconstructive imaging technique where tissue motion in response to mechanical excitation is measured using modern imaging systems, and the estimated displacements are then used to reconstruct the spatial distribution of Young's modulus. Here we present an ultrasound elasticity imaging method that utilizes the model-based technique for Young's modulus reconstruction. Based on the geometry of the imaged object, only one axial component of the strain tensor is used. The numerical implementation of the method is highly efficient because the reconstruction is based on an analytic solution of the forward elastic problem. The model-based approach is illustrated using two potential clinical applications: differentiation of liver hemangioma and staging of deep venous thrombosis. Overall, these studies demonstrate that model-based reconstructive elasticity imaging can be used in applications where the geometry of the object and the surrounding tissue is somewhat known and certain assumptions about the pathology can be made.
Block-based reconstructions for compressive spectral imaging
Correa, Claudia V.; Arguello, Henry; Arce, Gonzalo R.
2013-05-01
Coded Aperture Snapshot Spectral Imaging system (CASSI) captures spectral information of a scene using a reduced amount of focal plane array (FPA) projections. These projections are highly structured and localized such that each measurement contains information of a small portion of the data cube. Compressed sensing reconstruction algorithms are then used to recover the underlying 3-dimensional (3D) scene. The computational burden to recover a hyperspectral scene in CASSI is overwhelming for some applications such that reconstructions can take hours in desktop architectures. This paper presents a new method to reconstruct a hyperspectral signal from its compressive measurements using several overlapped block reconstructions. This approach exploits the structure of the CASSI sensing matrix to separately reconstruct overlapped regions of the 3D scene. The resultant reconstructions are then assembled to obtain the full recovered data cube. Typically, block-processing causes undesired artifacts in the recovered signal. Vertical and horizontal overlaps between adjacent blocks are then used to avoid these artifacts and increase the quality of reconstructed images. The reconstruction time and the quality of the reconstructed images are calculated as a function of the block-size and the amount of overlapped regions. Simulations show that the quality of the reconstructions is increased up to 6 dB and the reconstruction time is reduced up to 4 times when using block-based reconstruction instead of full data cube recovery at once. The proposed method is suitable for multi-processor architectures in which each core recovers one block at a time.
MR imaging of the augmented and reconstructed breast
Full text: Introduction: Various diagnostic methods are used to assess the changes in both the integrity of the implant, and the fibrous capsule of breast parenchyma. MRI has advantages over other diagnostic methods providing high tissue contrast, multi-faceted imaging and lack of ionizing radiation. What you will learn: MRI evaluation of breast augmentation approaches and their complications, MRI assessment of disease with malignant and benign characteristics in patients with breast implants, MRI assessment of breast reconstruction with autologous tissue. Discussion: Mammography after augmentation and reconstructive mammoplasty is hampered by the deformation of the breast parenchyma of the implant and the reduced compression. Postoperative scarring is also difficult to assess. MRI evaluation of implant rupture is accurate using the findings specific to it - linguine sign, teardrop sign or siliconomas. According to Gorczyca et al. MRI has a sensitivity 94% and specificity 97% in the evaluation of rupture. MRI mammography is highly sensitive - between 90 and 95%, in the detection of malignant, but it has limited specificity, which is its disadvantage. Malignant lesions can be represented as fibroadenomas, postoperative and inflammatory changes. Conclusion: Difficulties in the diagnosis of rupture of the implant, the primary and recurrent carcinoma based on clinical examination and inconclusive data from mammography and ultrasound imaging make MRI the method of choice in the evaluation of patients with breast implants
Current profile reconstruction using X-ray imaging on the PEGASUS toroidal experiment
Tritz, Kevin Lee
Internal plasma profiles, specifically the current profile, are necessary to accurately characterize the plasma equilibrium and perform detailed stability analyses of magnetically confined toroidal plasmas. External magnetic measurements alone are not sufficient to properly constrain the current profile for an equilibrium reconstruction. This work confirms the insensitivity of the profiles to external magnetics and demonstrates the successful incorporation of tangential X-ray imaging into a modified equilibrium code for current profile reconstruction in highly shaped, low aspect-ratio plasmas. An equilibrium reconstruction code was developed that used two dimensional X-ray images to constrain a flexible spline parameterization of the plasma profiles. Image constraint modeling was performed with this code, demonstrating that the profiles were well constrained, with less than 10% deviation of the reconstructed central safety factor, if the image measurement noise was held below 2% for emissivity constraints, and below 1% for intensity constraints. Two tangential soft X-ray pinhole camera imaging systems, a transmissive and reflective phosphor design, were built and operated on the PEGASUS toroidal experiment. Intensity image contours from these systems were used to constrain equilibrium reconstructions of the plasma discharge. The shapes and values of the q profiles determined by these reconstructions correspond well with the presence of coherent MHD activity observed in the plasmas. A comparison of the X-ray intensity-constrained equilibria with the external-magnetics-only reconstructions showed good agreement between most gross plasma parameters, but large variation between the reconstructed profiles. A next generation X-ray imaging system was designed to provide higher sensitivity, a more compact form factor, and multiple time point capability. The increased sensitivity will allow the variance of the experimental reconstructed profiles to achieve the level
Superiority of CT imaging reconstruction on Linux OS
Objective: To compare the speed of CT reconstruction using the Linux and Windows OS. Methods: Shepp-Logan head phantom in different pixel size was projected to obtain the sinogram by using the inverse Fourier transformation, filtered back projection and Radon transformation on both Linux and Windows OS. Results: CT image reconstruction using the Linux operating system was significantly better and more efficient than Windows. Conclusion: CT image reconstruction using the Linux operating system is more efficient. (authors)
Fully Automatic 3D Reconstruction of Histological Images
Bagci, Ulas; Bai, Li
2009-01-01
In this paper, we propose a computational framework for 3D volume reconstruction from 2D histological slices using registration algorithms in feature space. To improve the quality of reconstructed 3D volume, first, intensity variations in images are corrected by an intensity standardization process which maps image intensity scale to a standard scale where similar intensities correspond to similar tissues. Second, a subvolume approach is proposed for 3D reconstruction by dividing standardized...
Numerical modelling and image reconstruction in diffuse optical tomography
Dehghani, Hamid; Srinivasan, Subhadra; Pogue, Brian W.; Gibson, Adam
2009-01-01
The development of diffuse optical tomography as a functional imaging modality has relied largely on the use of model-based image reconstruction. The recovery of optical parameters from boundary measurements of light propagation within tissue is inherently a difficult one, because the problem is nonlinear, ill-posed and ill-conditioned. Additionally, although the measured near-infrared signals of light transmission through tissue provide high imaging contrast, the reconstructed images suffer ...
Reconstruction of Optical Thickness from Hoffman Modulation Contrast Images
Olsen, Niels Holm; Sporring, Jon; Nielsen, Mads; Hnida, Christina; Ziebe, Søren
Hoffman microscopy imaging systems are part of numerous fertility clinics world-wide. We discuss the physics of the Hoffman imaging system from optical thickness to image intensity, implement a simple, yet fast, reconstruction algorithm using Fast Fourier Transformation and discuss the usability ...... the method on a number of cells from a human embryo. Novelty is identifying the non-linearity of a typical Hoffman imaging system, and the application of Fourier Transformation to reconstruct the optical thickness....
Tan, Tien Jin, E-mail: tien_jin_tan@cgh.com.sg [Department of Radiology, Vancouver General Hospital, Vancouver, BC (Canada); Aljefri, Ahmad M. [Department of Radiology, Vancouver General Hospital, Vancouver, BC (Canada); Clarkson, Paul W.; Masri, Bassam A. [Department of Orthopaedics, University of British Columbia, Vancouver, BC (Canada); Ouellette, Hugue A.; Munk, Peter L.; Mallinson, Paul I. [Department of Radiology, Vancouver General Hospital, Vancouver, BC (Canada)
2015-09-15
Highlights: • Advances in reconstructive orthopaedic techniques now allow for limb salvage and prosthetic reconstruction procedures to be performed on patients who would otherwise be required to undergo debilitating limb amputations for malignant bone tumours. • The resulting post-operative imaging of such cases can be daunting for the radiologist to interpret, particularly in the presence of distorted anatomy and unfamiliar hardware. • This article reviews the indications for limb salvage surgery, prosthetic reconstruction devices involved, expected post-operative imaging findings, as well as the potential hardware related complications that may be encountered in the management of such cases. • By being aware of the various types of reconstructive techniques used in limb salvage surgery as well as the potential complications, the reporting radiologist should possess greater confidence in making an accurate assessment of the expected post-operative imaging findings in the management of such cases. - Abstract: Advances in reconstructive orthopaedic techniques now allow for limb salvage and prosthetic reconstruction procedures to be performed on patients who would otherwise be required to undergo debilitating limb amputations for malignant bone tumours. The resulting post-operative imaging of such cases can be daunting for the radiologist to interpret, particularly in the presence of distorted anatomy and unfamiliar hardware. This article reviews the indications for limb salvage surgery, prosthetic reconstruction devices involved, expected post-operative imaging findings, as well as the potential hardware related complications that may be encountered in the management of such cases.
Highlights: • Advances in reconstructive orthopaedic techniques now allow for limb salvage and prosthetic reconstruction procedures to be performed on patients who would otherwise be required to undergo debilitating limb amputations for malignant bone tumours. • The resulting post-operative imaging of such cases can be daunting for the radiologist to interpret, particularly in the presence of distorted anatomy and unfamiliar hardware. • This article reviews the indications for limb salvage surgery, prosthetic reconstruction devices involved, expected post-operative imaging findings, as well as the potential hardware related complications that may be encountered in the management of such cases. • By being aware of the various types of reconstructive techniques used in limb salvage surgery as well as the potential complications, the reporting radiologist should possess greater confidence in making an accurate assessment of the expected post-operative imaging findings in the management of such cases. - Abstract: Advances in reconstructive orthopaedic techniques now allow for limb salvage and prosthetic reconstruction procedures to be performed on patients who would otherwise be required to undergo debilitating limb amputations for malignant bone tumours. The resulting post-operative imaging of such cases can be daunting for the radiologist to interpret, particularly in the presence of distorted anatomy and unfamiliar hardware. This article reviews the indications for limb salvage surgery, prosthetic reconstruction devices involved, expected post-operative imaging findings, as well as the potential hardware related complications that may be encountered in the management of such cases
Lartizien, Carole; Kinahan, Paul E.; Comtat, Claude; Lin, Michael; Swensson, Richard G.; Trebossen, Regine; Bendriem, Bernard
2000-04-01
This work presents initial results from observer detection performance studies using the same volume visualization software tools that are used in clinical PET oncology imaging. Research into the FORE+OSEM and FORE+AWOSEM statistical image reconstruction methods tailored to whole- body 3D PET oncology imaging have indicated potential improvements in image SNR compared to currently used analytic reconstruction methods (FBP). To assess the resulting impact of these reconstruction methods on the performance of human observers in detecting and localizing tumors, we use a non- Monte Carlo technique to generate multiple statistically accurate realizations of 3D whole-body PET data, based on an extended MCAT phantom and with clinically realistic levels of statistical noise. For each realization, we add a fixed number of randomly located 1 cm diam. lesions whose contrast is varied among pre-calibrated values so that the range of true positive fractions is well sampled. The observer is told the number of tumors and, similar to the AFROC method, asked to localize all of them. The true positive fraction for the three algorithms (FBP, FORE+OSEM, FORE+AWOSEM) as a function of lesion contrast is calculated, although other protocols could be compared. A confidence level for each tumor is also recorded for incorporation into later AFROC analysis.
Terrain reconstruction from Chang'e-3 PCAM images
Wang, Wen-Rui; Ren, Xin; Wang, Fen-Fei; Liu, Jian-Jun; Li, Chun-Lai
2015-07-01
The existing terrain models that describe the local lunar surface have limited resolution and accuracy, which can hardly meet the needs of rover navigation, positioning and geological analysis. China launched the lunar probe Chang'e-3 in December, 2013. Chang'e-3 encompassed a lander and a lunar rover called “Yutu” (Jade Rabbit). A set of panoramic cameras were installed on the rover mast. After acquiring panoramic images of four sites that were explored, the terrain models of the local lunar surface with resolution of 0.02m were reconstructed. Compared with other data sources, the models derived from Chang'e-3 data were clear and accurate enough that they could be used to plan the route of Yutu. Supported by the National Natural Science Foundation of China.
A fast algorithm for image reconstruction based on sparse decomposition
YIN Zhongke; WANG Jianying; Pierre Vandergheynst
2007-01-01
It is very slow at present to reconstruct an image from its sparse decomposition results.To overcome this one of the main drawbacks in image sparse decomposition,the property of the energy distribution of atoms is studied in this paper.Based on the property that energy of most atoms is highly concentrated,an algorithm is proposed to fast reconstruct an image from atoms' parameters by limiting atom reconstruction calculating within the atom energy concentrating area.Moreover,methods for fast calculating atom energy and normalization are also put forward.The fast algorithm presented in this Paper improves the speed of the image reconstructing by approximately 32 times without degrading the reconstructed image quality.
Concurrent image and dose reconstruction for image guided radiation therapy
Sheng, Ke
The importance of knowing the patient actual position is essential for intensity modulated radiation therapy (IMRT). This procedure uses tightened margin and escalated tumor dose. In order to eliminate the uncertainty of the geometry in IMRT, daily imaging is prefered. The imaging dose, limited field of view and the imaging concurrency of the MVCT (mega-voltage computerized tomography) are investigated in this work. By applying partial volume imaging (PVI), imaging dose can be reduced for a region of interest (ROI) imaging. The imaging dose and the image quality are quantitatively balanced with inverse imaging dose planning. With PVI, 72% average imaging dose reduction was observed on a typical prostate patient case. The algebraic reconstruction technique (ART) based projection onto convex sets (POCS) shows higher robustness than filtered back projection when available imaging data is not complete and continuous. However, when the projection is continuous as in the actual delivery, a non-iterative wavelet based multiresolution local tomography (WMLT) is able to achieve 1% accuracy within the ROI. The reduction of imaging dose is dependent on the size of ROI. The improvement of concurrency is also discussed based on the combination of PVI and WMLT. Useful target images were acquired with treatment beams and the temporal resolution can be increased to 20 seconds in tomotherapy. The data truncation problem with the portal imager was also studied. Results show that the image quality is not adversely affected by truncation when WMLT is employed. When the online imaging is available, a perturbation dose calculation (PDC) that estimates the actual delivered dose is proposed. Corrected from the Fano's theorem, PDC counts the first order term in the density variation to calculate the internal and external anatomy change. Although change in the dose distribution that is caused by the internal organ motion is less than 1% for 6 MV beams, the external anatomy change has
Study of image reconstruction using dynamic grids in tomographic gamma scanning
In this paper, a new image reconstruction algorithm employing dynamic grids technique is proposed for tomographic gamma scanning. The key feature of the algorithm is the use of adaptive grid refinement in areas that indicate large values. Simulation results show that the application of dynamic grids has a good performance in emission reconstruction with a distinct advantage in the accurate positioning of the 'hot spots' and reducing the number of grids, but doesn't achieve a tangible improvement in transmission reconstruction. (authors)
Lu, Yujie; Zhu, Banghe; Darne, Chinmay; Tan, I.-Chih; Rasmussen, John C.; Sevick-Muraca, Eva M.
2011-12-01
The goal of preclinical fluorescence-enhanced optical tomography (FEOT) is to provide three-dimensional fluorophore distribution for a myriad of drug and disease discovery studies in small animals. Effective measurements, as well as fast and robust image reconstruction, are necessary for extensive applications. Compared to bioluminescence tomography (BLT), FEOT may result in improved image quality through higher detected photon count rates. However, background signals that arise from excitation illumination affect the reconstruction quality, especially when tissue fluorophore concentration is low and/or fluorescent target is located deeply in tissues. We show that near-infrared fluorescence (NIRF) imaging with an optimized filter configuration significantly reduces the background noise. Model-based reconstruction with a high-order approximation to the radiative transfer equation further improves the reconstruction quality compared to the diffusion approximation. Improvements in FEOT are demonstrated experimentally using a mouse-shaped phantom with targets of pico- and subpico-mole NIR fluorescent dye.
Depth-based selective image reconstruction using spatiotemporal image analysis
Haga, Tetsuji; Sumi, Kazuhiko; Hashimoto, Manabu; Seki, Akinobu
1999-03-01
In industrial plants, a remote monitoring system which removes physical tour inspection is often considered desirable. However the image sequence given from the mobile inspection robot is hard to see because interested objects are often partially occluded by obstacles such as pillars or fences. Our aim is to improve the image sequence that increases the efficiency and reliability of remote visual inspection. We propose a new depth-based image processing technique, which removes the needless objects from the foreground and recovers the occluded background electronically. Our algorithm is based on spatiotemporal analysis that enables fine and dense depth estimation, depth-based precise segmentation, and accurate interpolation. We apply this technique to a real time sequence given from the mobile inspection robot. The resulted image sequence is satisfactory in that the operator can make correct visual inspection with less fatigue.
Laminographic reconstruction from real-time radiographic images
We report the application of digital laminography reconstruction methods to real-time radiographic (RTR) images. Multiple digital images were acquired with the part at several orientations. Several acquisition and reconstruction methods have been investigated and their effects on the depth resolution and signal-to-noise ratio of the reconstructed images are discussed. The standard method yields the best signal-to-noise but the worst depth separation; the extreme value method yields the best depth separation with a slight decrease in signal-to-noise; and the iterative method is a compromise between the two. Both the extreme value and iterative methods require care in properly normalizing the projection images
Image Compression and Reconstruction using Cubic Spline Interpolation Technique
R. Muthaiah
2008-01-01
Full Text Available A new dimension of image compression using random pixels of irregular sampling and image reconstruction using cubic-spline interpolation techniques proposed in this paper. It also covers the wide field of multimedia communication concerned with multimedia messaging (MMS and image transfer through mobile phones and tried to find a mechanism to transfer images with minimum bandwidth requirement. This method would provide a better efficiency both in pixel reconstruction and color reproduction. The discussion covers theoretical techniques of random pixel selection, transfer and implementation of efficient reconstruction with cubic spline interpolation.
Accelerated gradient methods for total-variation-based CT image reconstruction
Total-variation (TV)-based CT image reconstruction has shown experimentally to be capable of producing accurate reconstructions from sparse-view data. In particular TV-based reconstruction is well suited for images with piecewise nearly constant regions. Computationally, however, TV-based reconstruction is demanding, especially for 3D imaging, and the reconstruction from clinical data sets is far from being close to real-time. This is undesirable from a clinical perspective, and thus there is an incentive to accelerate the solution of the underlying optimization problem. The TV reconstruction can in principle be found by any optimization method, but in practice the large scale of the systems arising in CT image reconstruction preclude the use of memory-intensive methods such as Newton's method. The simple gradient method has much lower memory requirements, but exhibits prohibitively slow convergence. In the present work we address the question of how to reduce the number of gradient method iterations needed to achieve a high-accuracy TV reconstruction. We consider the use of two accelerated gradient-based methods, GPBB and UPN, to solve the 3D-TV minimization problem in CT image reconstruction. The former incorporates several heuristics from the optimization literature such as Barzilai-Borwein (BB) step size selection and nonmonotone line search. The latter uses a cleverly chosen sequence of auxiliary points to achieve a better convergence rate. The methods are memory efficient and equipped with a stopping criterion to ensure that the TV reconstruction has indeed been found. An implementation of the methods (in C with interface to Matlab) is available for download from http://www2.imm.dtu.dk/~pch/TVReg/. We compare the proposed methods with the standard gradient method, applied to a 3D test problem with synthetic few-view data. We find experimentally that for realistic parameters the proposed methods significantly outperform the standard gradient method. (orig.)
Research on THz CT system and image reconstruction algorithm
Li, Ming-liang; Wang, Cong; Cheng, Hong
2009-07-01
Terahertz Computed Tomography takes the advantages of not only high resolution in space and density without image overlap but also the capability of being directly used in digital processing and spectral analysis, which determine it to be a good choice in parameter detection for process control. But Diffraction and scattering of THz wave will obfuscate or distort the reconstructed image. In order to find the most effective reconstruction method to build THz CT model. Because of the expensive cost, a fan-shaped THz CT industrial detection system scanning model, which consists of 8 emitters and 32 receivers, is established based on studying infrared CT technology. The model contains control and interface, data collecting and image reconstruction sub-system. It analyzes all the sub-function modules then reconstructs images with algebraic reconstruction algorithm. The experimental result proves it to be an effective, efficient algorithm with high resolution and even better than back-projection method.
Reconstruction of biofilm images: combining local and global structural parameters
Resat, Haluk; Renslow, Ryan S.; Beyenal, Haluk
2014-11-07
Digitized images can be used for quantitative comparison of biofilms grown under different conditions. Using biofilm image reconstruction, it was previously found that biofilms with a completely different look can have nearly identical structural parameters and that the most commonly utilized global structural parameters were not sufficient to uniquely define these biofilms. Here, additional local and global parameters are introduced to show that these parameters considerably increase the reliability of the image reconstruction process. Assessment using human evaluators indicated that the correct identification rate of the reconstructed images increased from 50% to 72% with the introduction of the new parameters into the reconstruction procedure. An expanded set of parameters especially improved the identification of biofilm structures with internal orientational features and of structures in which colony sizes and spatial locations varied. Hence, the newly introduced structural parameter sets helped to better classify the biofilms by incorporating finer local structural details into the reconstruction process.
Calibration and Image Reconstruction for the Hurricane Imaging Radiometer (HIRAD)
Ruf, Christopher; Roberts, J. Brent; Biswas, Sayak; James, Mark W.; Miller, Timothy
2012-01-01
The Hurricane Imaging Radiometer (HIRAD) is a new airborne passive microwave synthetic aperture radiometer designed to provide wide swath images of ocean surface wind speed under heavy precipitation and, in particular, in tropical cyclones. It operates at 4, 5, 6 and 6.6 GHz and uses interferometric signal processing to synthesize a pushbroom imager in software from a low profile planar antenna with no mechanical scanning. HIRAD participated in NASA s Genesis and Rapid Intensification Processes (GRIP) mission during Fall 2010 as its first science field campaign. HIRAD produced images of upwelling brightness temperature over a aprox 70 km swath width with approx 3 km spatial resolution. From this, ocean surface wind speed and column averaged atmospheric liquid water content can be retrieved across the swath. The calibration and image reconstruction algorithms that were used to verify HIRAD functional performance during and immediately after GRIP were only preliminary and used a number of simplifying assumptions and approximations about the instrument design and performance. The development and performance of a more detailed and complete set of algorithms are reported here.
Arne Vladimir Blackman; Stefan Grabuschnig; Robert Legenstein; Per Jesper Sjöström
2014-01-01
Accurate 3D reconstruction of neurons is vital for applications linking anatomy and physiology. Reconstructions are typically created using Neurolucida after biocytin histology (BH). An alternative inexpensive and fast method is to use freeware such as Neuromantic to reconstruct from fluorescence imaging (FI) stacks acquired using 2-photon laser-scanning microscopy during physiological recording. We compare these two methods with respect to morphometry, cell classification, and multicompartme...
Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity
Di Zhao; Huiqian Du; Yu Han; Wenbo Mei
2014-01-01
Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inac...
Tomographic images reconstruction and its applications in SPECT
The basis of the filtered back projection algorithms for image reconstruction from one-dimensional projections are described. This method is applied to SPECT studies and the effects due to photon attenuation, Compton scattering, detector response and statistical noise are analysed. The results show the importance of a) the filtering procedure to enhance the signal-to-noise ratio of the reconstructed images, and b) the correction of the image artifacts due to the above mentioned effects to improve resolution and contrast. (Author)
Sparse Reconstruction Schemes for Nonlinear Electromagnetic Imaging
Desmal, Abdulla
2016-03-01
synthetically generated or actually measured scattered fields, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.
Neural portraits of perception: reconstructing face images from evoked brain activity.
Cowen, Alan S; Chun, Marvin M; Kuhl, Brice A
2014-07-01
Recent neuroimaging advances have allowed visual experience to be reconstructed from patterns of brain activity. While neural reconstructions have ranged in complexity, they have relied almost exclusively on retinotopic mappings between visual input and activity in early visual cortex. However, subjective perceptual information is tied more closely to higher-level cortical regions that have not yet been used as the primary basis for neural reconstructions. Furthermore, no reconstruction studies to date have reported reconstructions of face images, which activate a highly distributed cortical network. Thus, we investigated (a) whether individual face images could be accurately reconstructed from distributed patterns of neural activity, and (b) whether this could be achieved even when excluding activity within occipital cortex. Our approach involved four steps. (1) Principal component analysis (PCA) was used to identify components that efficiently represented a set of training faces. (2) The identified components were then mapped, using a machine learning algorithm, to fMRI activity collected during viewing of the training faces. (3) Based on activity elicited by a new set of test faces, the algorithm predicted associated component scores. (4) Finally, these scores were transformed into reconstructed images. Using both objective and subjective validation measures, we show that our methods yield strikingly accurate neural reconstructions of faces even when excluding occipital cortex. This methodology not only represents a novel and promising approach for investigating face perception, but also suggests avenues for reconstructing 'offline' visual experiences-including dreams, memories, and imagination-which are chiefly represented in higher-level cortical areas. PMID:24650597
Rau, Urvashi; Owen, Frazer N
2016-01-01
Many deep wide-band wide-field radio interferometric surveys are being designed to accurately measure intensities, spectral indices and polarization properties of faint source populations. In this paper we compare various wideband imaging methods to evaluate the accuracy to which intensities and spectral indices of sources close to the confusion limit can be reconstructed. We simulated a wideband single-pointing (C-array, L-Band (1-2GHz)) and 46-pointing mosaic(D-array, C-Band (4-8GHz)) JVLA observation using realistic brightness distribution ranging from $1\\mu$Jy to $100m$Jy and time-,frequency-, polarization- and direction-dependent instrumental effects. The main results from these comparisons are (a) errors in the reconstructed intensities and spectral indices are larger for weaker sources even in the absence of simulated noise, (b) errors are systematically lower for joint reconstruction methods (such as MT-MFS) along with A-Projection for accurate primary beam correction, and (c) use of MT-MFS for image ...
Quantitative image quality evaluation for cardiac CT reconstructions
Tseng, Hsin-Wu; Fan, Jiahua; Kupinski, Matthew A.; Balhorn, William; Okerlund, Darin R.
2016-03-01
Maintaining image quality in the presence of motion is always desirable and challenging in clinical Cardiac CT imaging. Different image-reconstruction algorithms are available on current commercial CT systems that attempt to achieve this goal. It is widely accepted that image-quality assessment should be task-based and involve specific tasks, observers, and associated figures of merits. In this work, we developed an observer model that performed the task of estimating the percentage of plaque in a vessel from CT images. We compared task performance of Cardiac CT image data reconstructed using a conventional FBP reconstruction algorithm and the SnapShot Freeze (SSF) algorithm, each at default and optimal reconstruction cardiac phases. The purpose of this work is to design an approach for quantitative image-quality evaluation of temporal resolution for Cardiac CT systems. To simulate heart motion, a moving coronary type phantom synchronized with an ECG signal was used. Three different percentage plaques embedded in a 3 mm vessel phantom were imaged multiple times under motion free, 60 bpm, and 80 bpm heart rates. Static (motion free) images of this phantom were taken as reference images for image template generation. Independent ROIs from the 60 bpm and 80 bpm images were generated by vessel tracking. The observer performed estimation tasks using these ROIs. Ensemble mean square error (EMSE) was used as the figure of merit. Results suggest that the quality of SSF images is superior to the quality of FBP images in higher heart-rate scans.
Imaging biopsy composition at ACL reconstruction
Pedersen DR
2013-04-01
Full Text Available Douglas R Pedersen,1,2 James A Martin,1,2 Daniel R Thedens,3 Noelle F Klocke,1,2 Nathaniel H Roberts,1 Jessica E Goetz,1 Annunziato Amendola1 1Department of Orthopaedics and Rehabilitation, 2Department of Biomedical Engineering, 3Department of Radiology, University of Iowa, Iowa City, IA, USA Purpose: Early-stage osteoarthritis (OA includes glycosaminoglycan (GAG loss and collagen disruption that cannot be seen on morphological magnetic resonance imaging (MRI. T1ρ MRI is a measurement that probes the low-frequency rate of exchange between protons of free water and those from water associated with macromolecules in the cartilage's extracellular matrix. While it has been hypothesized that increased water mobility resulting from early osteoarthritic changes cause elevated T1ρ MRI values, there remain several unknown mechanisms influencing T1ρ measurements in cartilage. The purpose of this work was to relate histological and biochemical metrics directly measured from osteochondral biopsies and fluid specimens with quantitative MRI-detected changes of in vivo cartilage composition. Patients and methods: Six young patients were enrolled an average of 41 days after acute anterior cruciate ligament (ACL rupture. Femoral trochlear groove osteochondral biopsies, serum, and synovial fluid were harvested during ACL reconstruction to complement a presurgery quantitative MRI study (T1ρ, T2, delayed gadolinium-enhanced MRI of cartilage [dGEMRIC] relaxation times. A high-resolution MRI scan of the excised osteochondral biopsy was also collected. Analyses of in vivo T1ρ images were compared with ex vivo T1ρ imaging, GAG assays and histological GAG distribution in the osteochondral biopsies, and direct measures of bone and cartilage turnover markers and "OA marker" 3B3 in serum and synovial fluid samples. Conclusion: T1ρ relaxation times in patients with a torn ACL were elevated from normal, indicating changes consistent with general fluid effusion after
A 3D Model Reconstruction Method Using Slice Images
LI Hong-an; KANG Bao-sheng
2013-01-01
Aiming at achieving the high accuracy 3D model from slice images, a new model reconstruction method using slice im-ages is proposed. Wanting to extract the outermost contours from slice images, the method of the improved GVF-Snake model with optimized force field and ray method is employed. And then, the 3D model is reconstructed by contour connection using the im-proved shortest diagonal method and judgment function of contour fracture. The results show that the accuracy of reconstruction 3D model is improved.
Application of particle filtering algorithm in image reconstruction of EMT
To improve the image quality of electromagnetic tomography (EMT), a new image reconstruction method of EMT based on a particle filtering algorithm is presented. Firstly, the principle of image reconstruction of EMT is analyzed. Then the search process for the optimal solution for image reconstruction of EMT is described as a system state estimation process, and the state space model is established. Secondly, to obtain the minimum variance estimation of image reconstruction, the optimal weights of random samples obtained from the state space are calculated from the measured information. Finally, simulation experiments with five different flow regimes are performed. The experimental results have shown that the average image error of reconstruction results obtained by the method mentioned in this paper is 42.61%, and the average correlation coefficient with the original image is 0.8706, which are much better than corresponding indicators obtained by LBP, Landweber and Kalman Filter algorithms. So, this EMT image reconstruction method has high efficiency and accuracy, and provides a new method and means for EMT research. (paper)
Image Reconstruction Algorithm for Electrical Charge Tomography System
M. F. Rahmat
2010-01-01
Full Text Available Problem statement: Many problems in scientific computing can be formulated as inverse problem. A vast majority of these problems are ill-posed problems. In Electrical Charge Tomography (EChT, normally the sensitivity matrix generated from forward modeling is very ill-condition. This condition posts difficulties to the inverse problem solution especially in the accuracy and stability of the image being reconstructed. The objective of this study is to reconstruct the image cross-section of the material in pipeline gravity dropped mode conveyor as well to solve the ill-condition of matrix sensitivity. Approach: Least Square with Regularization (LSR method had been introduced to reconstruct the image and the electrodynamics sensor was used to capture the data that installed around the pipe. Results: The images were validated using digital imaging technique and Singular Value Decomposition (SVD method. The results showed that image reconstructed by this method produces a good promise in terms of accuracy and stability. Conclusion: This implied that LSR method provides good and promising result in terms of accuracy and stability of the image being reconstructed. As a result, an efficient method for electrical charge tomography image reconstruction has been introduced.
Takata, Tadanori; Ichikawa, Katsuhiro; Hayashi, Hiroyuki; Mitsui, Wataru; Sakuta, Keita; Koshida, Haruka; Yokoi, Tomohiro; Matsubara, Kousuke; Horii, Jyunsei; Iida, Hiroji
2012-01-01
The purpose of this study was to evaluate the image quality of an iterative reconstruction method, the iterative reconstruction in image space (IRIS), which was implemented in a 128-slices multi-detector computed tomography system (MDCT), Siemens Somatom Definition Flash (Definition). We evaluated image noise by standard deviation (SD) as many researchers did before, and in addition, we measured modulation transfer function (MTF), noise power spectrum (NPS), and perceptual low-contrast detectability using a water phantom including a low-contrast object with a 10 Hounsfield unit (HU) contrast, to evaluate whether the noise reduction of IRIS was effective. The SD and NPS were measured from the images of a water phantom. The MTF was measured from images of a thin metal wire and a bar pattern phantom with the bar contrast of 125 HU. The NPS of IRIS was lower than that of filtered back projection (FBP) at middle and high frequency regions. The SD values were reduced by 21%. The MTF of IRIS and FBP measured by the wire phantom coincided precisely. However, for the bar pattern phantom, the MTF values of IRIS at 0.625 and 0.833 cycle/mm were lower than those of FBP. Despite the reduction of the SD and the NPS, the low-contrast detectability study indicated no significant difference between IRIS and FBP. From these results, it was demonstrated that IRIS had the noise reduction performance with exact preservation for high contrast resolution and slight degradation of middle contrast resolution, and could slightly improve the low contrast detectability but with no significance. PMID:22516592
Artificial neural network Radon inversion for image reconstruction
Image reconstruction techniques are essential to computer tomography. Algorithms such as filtered backprojection (FBP) or algebraic techniques are most frequently used. This paper presents an attempt to apply a feed-forward back-propagation supervised artificial neural network (BPN) to tomographic image reconstruction, specifically to positron emission tomography (PET). The main result is that the network trained with Gaussian test images proved to be successful at reconstructing images from projection sets derived from arbitrary objects. Additional results relate to the design of the network and the full width at half maximum (FWHM) of the Gaussians in the training sets. First, the optimal number of nodes in the middle layer is about an order of magnitude less than the number of input or output nodes. Second, the number of iterations required to achieve a required training set tolerance appeared to decrease exponentially with the number of nodes in the middle layer. Finally, for training sets containing Gaussians of a single width, the optimal accuracy of reconstructing the control set is obtained with a FWHM of three pixels. Intended to explore feasibility, the BPN presented in the following does not provide reconstruction accuracy adequate for immediate application to PET. However, the trained network does reconstruct general images independent of the data with which it was trained. Proposed in the concluding section are several possible refinements that should permit the development of a network capable of fast reconstruction of three-dimensional images from the discrete, noisy projection data characteristic of PET
Fully Automatic 3D Reconstruction of Histological Images
Bagci, Ulas
2009-01-01
In this paper, we propose a computational framework for 3D volume reconstruction from 2D histological slices using registration algorithms in feature space. To improve the quality of reconstructed 3D volume, first, intensity variations in images are corrected by an intensity standardization process which maps image intensity scale to a standard scale where similar intensities correspond to similar tissues. Second, a subvolume approach is proposed for 3D reconstruction by dividing standardized slices into groups. Third, in order to improve the quality of the reconstruction process, an automatic best reference slice selection algorithm is developed based on an iterative assessment of image entropy and mean square error of the registration process. Finally, we demonstrate that the choice of the reference slice has a significant impact on registration quality and subsequent 3D reconstruction.
Rumple, C.; Richter, J.; Craven, B. A.; Krane, M.
2012-11-01
A summary of the research being carried out by our multidisciplinary team to better understand the form and function of the nose in different mammalian species that include humans, carnivores, ungulates, rodents, and marine animals will be presented. The mammalian nose houses a convoluted airway labyrinth, where two hallmark features of mammals occur, endothermy and olfaction. Because of the complexity of the nasal cavity, the anatomy and function of these upper airways remain poorly understood in most mammals. However, recent advances in high-resolution medical imaging, computational modeling, and experimental flow measurement techniques are now permitting the study of airflow and respiratory and olfactory transport phenomena in anatomically-accurate reconstructions of the nasal cavity. Here, we focus on efforts to manufacture transparent, anatomically-accurate models for stereo particle image velocimetry (SPIV) measurements of nasal airflow. Challenges in the design and manufacture of index-matched anatomical models are addressed and preliminary SPIV measurements are presented. Such measurements will constitute a validation database for concurrent computational fluid dynamics (CFD) simulations of mammalian respiration and olfaction. Supported by the National Science Foundation.
Sparsity-constrained PET image reconstruction with learned dictionaries
Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie
2016-09-01
PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.
MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods.
Johannes F M Schmidt
Full Text Available An approach to Magnetic Resonance (MR image reconstruction from undersampled data is proposed. Undersampling artifacts are removed using an iterative thresholding algorithm applied to nonlinearly transformed image block arrays. Each block array is transformed using kernel principal component analysis where the contribution of each image block to the transform depends in a nonlinear fashion on the distance to other image blocks. Elimination of undersampling artifacts is achieved by conventional principal component analysis in the nonlinear transform domain, projection onto the main components and back-mapping into the image domain. Iterative image reconstruction is performed by interleaving the proposed undersampling artifact removal step and gradient updates enforcing consistency with acquired k-space data. The algorithm is evaluated using retrospectively undersampled MR cardiac cine data and compared to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT reconstruction. Evaluation of image quality and root-mean-squared-error (RMSE reveal improved image reconstruction for up to 8-fold undersampled data with the proposed approach relative to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT. In conclusion, block matching and kernel methods can be used for effective removal of undersampling artifacts in MR image reconstruction and outperform methods using standard compressed sensing and ℓ1-regularized parallel imaging methods.
MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods.
Schmidt, Johannes F M; Santelli, Claudio; Kozerke, Sebastian
2016-01-01
An approach to Magnetic Resonance (MR) image reconstruction from undersampled data is proposed. Undersampling artifacts are removed using an iterative thresholding algorithm applied to nonlinearly transformed image block arrays. Each block array is transformed using kernel principal component analysis where the contribution of each image block to the transform depends in a nonlinear fashion on the distance to other image blocks. Elimination of undersampling artifacts is achieved by conventional principal component analysis in the nonlinear transform domain, projection onto the main components and back-mapping into the image domain. Iterative image reconstruction is performed by interleaving the proposed undersampling artifact removal step and gradient updates enforcing consistency with acquired k-space data. The algorithm is evaluated using retrospectively undersampled MR cardiac cine data and compared to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT reconstruction. Evaluation of image quality and root-mean-squared-error (RMSE) reveal improved image reconstruction for up to 8-fold undersampled data with the proposed approach relative to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT. In conclusion, block matching and kernel methods can be used for effective removal of undersampling artifacts in MR image reconstruction and outperform methods using standard compressed sensing and ℓ1-regularized parallel imaging methods. PMID:27116675
Surface Reconstruction and Image Enhancement via $L^1$-Minimization
Dobrev, Veselin
2010-01-01
A surface reconstruction technique based on minimization of the total variation of the gradient is introduced. Convergence of the method is established, and an interior-point algorithm solving the associated linear programming problem is introduced. The reconstruction algorithm is illustrated on various test cases including natural and urban terrain data, and enhancement oflow-resolution or aliased images. Copyright © by SIAM.
Face reconstruction from image sequences for forensic face comparison
Dam, van Chris; Veldhuis, Raymond; Spreeuwers, Luuk
2015-01-01
The authors explore the possibilities of a dense model-free three-dimensional (3D) face reconstruction method, based on image sequences from a single camera, to improve the current state of forensic face comparison. They propose a new model-free 3D reconstruction method for faces, based on the Lambe
The purpose of this study is to investigate whether the method of applicator reconstruction and/or the applicator orientation influence the dose calculation to points around the applicator for brachytherapy of cervical cancer with CT-based treatment planning. A phantom, containing a fixed ring applicator set and six lead pellets representing dose points, was used. The phantom was CT scanned with the ring applicator at four different angles related to the image plane. In each scan the applicator was reconstructed by three methods: (1) direct reconstruction in each image (DR) (2) reconstruction in multiplanar reconstructed images (MPR) and (3) library plans, using pre-defined applicator geometry (LIB). The doses to the lead pellets were calculated. The relative standard deviation (SD) for all reconstruction methods was less than 3.7% in the dose points. The relative SD for the LIB method was significantly lower (p < 0.05) than for the DR and MPR methods for all but two points. All applicator orientations had similar dose calculation reproducibility. Using library plans for applicator reconstruction gives the most reproducible dose calculation. However, with restrictive guidelines for applicator reconstruction the uncertainties for all methods are low compared to other factors influencing the accuracy of brachytherapy
Wahbeh, W.; Nebiker, S.; Fangi, G.
2016-06-01
This paper exploits the potential of dense multi-image 3d reconstruction of destroyed cultural heritage monuments by either using public domain touristic imagery only or by combining the public domain imagery with professional panoramic imagery. The focus of our work is placed on the reconstruction of the temple of Bel, one of the Syrian heritage monuments, which was destroyed in September 2015 by the so called "Islamic State". The great temple of Bel is considered as one of the most important religious buildings of the 1st century AD in the East with a unique design. The investigations and the reconstruction were carried out using two types of imagery. The first are freely available generic touristic photos collected from the web. The second are panoramic images captured in 2010 for documenting those monuments. In the paper we present a 3d reconstruction workflow for both types of imagery using state-of-the art dense image matching software, addressing the non-trivial challenges of combining uncalibrated public domain imagery with panoramic images with very wide base-lines. We subsequently investigate the aspects of accuracy and completeness obtainable from the public domain touristic images alone and from the combination with spherical panoramas. We furthermore discuss the challenges of co-registering the weakly connected 3d point cloud fragments resulting from the limited coverage of the touristic photos. We then describe an approach using spherical photogrammetry as a virtual topographic survey allowing the co-registration of a detailed and accurate single 3d model of the temple interior and exterior.
Accurate and robust reconstruction of the radioactivity concentration is of great importance in positron emission tomography (PET) imaging. Given the Poisson nature of photo-counting measurements, we present a reconstruction framework that integrates sparsity penalty on a dictionary into a maximum likelihood estimator. Patch-sparsity on a dictionary provides the regularization for our effort, and iterative procedures are used to solve the maximum likelihood function formulated on Poisson statistics. Specifically, in our formulation, a dictionary could be trained on CT images, to provide intrinsic anatomical structures for the reconstructed images, or adaptively learned from the noisy measurements of PET. Accuracy of the strategy with very promising application results from Monte-Carlo simulations, and real data are demonstrated. (paper)
Application of iterative image reconstruction to functional brain mapping
Full text: The advantage of the iterative image reconstruction algorithms, such as the maximum likelihood expectation maximisation (ML-EM) algorithm in providing improved image signal-to-noise ratio (SNR)in the low count positron emission tomography (PET) studies makes it a suitable image reconstruction algorithm for PET functional brain mapping. The ML-EM algorithm improves the sensitivity and specificity of functional brain imaging compared to images reconstructed using the filtered back projection (FBP) algorithm. We optimised the ML-EM algorithm for maximum sensitivity with no loss of specificity (compared to the FBP algorithm) as a function of iteration number and t-value probability threshold. A receiver operating characteristic (ROC) for analysing a simulated 3D activation study was determined for each ML-EM iteration up to the twenty first iteration. At four ML-EM iterations and using a 0.05 t-value probability threshold, the ML-EM images identified the signal regions with 41% increased sensitivity and 6% decreased specificity compared to FBP images. Results for a human auditory stimulus activation study are also presented and discussed. In conclusion, the images reconstructed at four ML-EM iterations demonstrate improved statistical properties compared to images reconstructed using FBP algorithm
Initial evaluation of discrete orthogonal basis reconstruction of ECT images
Discrete orthogonal basis restoration (DOBR) is a linear, non-iterative, and robust method for solving inverse problems for systems characterized by shift-variant transfer functions. This simulation study evaluates the feasibility of using DOBR for reconstructing emission computed tomographic (ECT) images. The imaging system model uses typical SPECT parameters and incorporates the effects of attenuation, spatially-variant PSF, and Poisson noise in the projection process. Sample reconstructions and statistical error analyses for a class of digital phantoms compare the DOBR performance for Hartley and Walsh basis functions. Test results confirm that DOBR with either basis set produces images with good statistical properties. No problems were encountered with reconstruction instability. The flexibility of the DOBR method and its consistent performance warrants further investigation of DOBR as a means of ECT image reconstruction
Generalized metrics induced anatomical prior for MAP PET image reconstruction
Information theoretic metrics, including mutual information (MI) and joint entropy (JE), have been investigated as priors to incorporate anatomical information in ill-posed positron emission tomography (PET) image reconstruction. These metrics are generally based on the Shannon entropy. Meanwhile, in this paper, we proposed a generalized metrics induced anatomical prior for maximum a posteriori (MAP) PET reconstruction based on the generalized Shannon entropy metrics or Tsallis entropy. For the presented prior computation, a non-parametric method was used to estimate the joint probability density of the PET and MR image. Furthermore, we also developed an one-step-advance (OSA) MAP algorithm for PET image reconstruction with the presented prior regularization. Simulation results show that the presented novel prior has significantly improved the reconstructed PET image quality. (orig.)
Online reconstruction of 3D magnetic particle imaging data
Knopp, T.; Hofmann, M.
2016-06-01
Magnetic particle imaging is a quantitative functional imaging technique that allows imaging of the spatial distribution of super-paramagnetic iron oxide particles at high temporal resolution. The raw data acquisition can be performed at frame rates of more than 40 volumes s‑1. However, to date image reconstruction is performed in an offline step and thus no direct feedback is available during the experiment. Considering potential interventional applications such direct feedback would be mandatory. In this work, an online reconstruction framework is implemented that allows direct visualization of the particle distribution on the screen of the acquisition computer with a latency of about 2 s. The reconstruction process is adaptive and performs block-averaging in order to optimize the signal quality for a given amount of reconstruction time.
Online reconstruction of 3D magnetic particle imaging data.
Knopp, T; Hofmann, M
2016-06-01
Magnetic particle imaging is a quantitative functional imaging technique that allows imaging of the spatial distribution of super-paramagnetic iron oxide particles at high temporal resolution. The raw data acquisition can be performed at frame rates of more than 40 volumes s(-1). However, to date image reconstruction is performed in an offline step and thus no direct feedback is available during the experiment. Considering potential interventional applications such direct feedback would be mandatory. In this work, an online reconstruction framework is implemented that allows direct visualization of the particle distribution on the screen of the acquisition computer with a latency of about 2 s. The reconstruction process is adaptive and performs block-averaging in order to optimize the signal quality for a given amount of reconstruction time. PMID:27182668
Homotopy Based Reconstruction from Acoustic Images
Sharma, Ojaswa
are reconstruction from an organised set of linear cross sections and reconstruction from an arbitrary set of linear cross sections. The first problem is looked upon in the context of acoustic signals wherein the cross sections show a definite geometric arrangement. A reconstruction in this case can...... take advantage of the inherent arrangement. The problem of reconstruction from arbitrary cross sections is a generic problem and is also shown to be solved here using the mathematical tool of continuous deformations. As part of a complete processing, segmentation using level set methods is explored for......This thesis presents work in the direction of generating smooth surfaces from linear cross sections embedded in R2 and R3 using homotopy continuation. The methods developed in this research are generic and can be applied to higher dimensions as well. Two types of problems addressed in this research...
Image reconstruction for x-ray holographic microscopy
The authors have demonstrated direct Fourier reconstruction of a simple three-dimensional object from multiple Fourier transform holograms recorded at different incidence angles. Following phase recovery of the scattered field based on the reference scatterer, the authors used standard diffraction tomography techniques to reconstruct the object scattering potential. For systems of low numerical aperture, algebraic techniques which assume straight rays were demonstrated to produce images with fewer reconstruction artifacts. These techniques allow the use of a priori knowledge of the object; this approach should be extended into diffraction-based reconstruction algorithms
Progress toward the development and testing of source reconstruction methods for NIF neutron imaging
Development of analysis techniques for neutron imaging at the National Ignition Facility is an important and difficult task for the detailed understanding of high-neutron yield inertial confinement fusion implosions. Once developed, these methods must provide accurate images of the hot and cold fuels so that information about the implosion, such as symmetry and areal density, can be extracted. One method under development involves the numerical inversion of the pinhole image using knowledge of neutron transport through the pinhole aperture from Monte Carlo simulations. In this article we present results of source reconstructions based on simulated images that test the methods effectiveness with regard to pinhole misalignment.
Program package for accurate 3D field reconstruction from boundary measurements
The problem of the magnetic field reconstruction inside a subregion in R3 from magnetic measurements on the closed boundary of this subregion is considered. The efficiency of the proposed method, algorithm and associated software for the precision magnet system is discussed. The results of the software verification, numerical experiments as well as the ones of the field reconstruction using boundary measurements in the magnet M1 of the separator COMBAS are given. Requirements to the position accuracy of sensors consistent with the required accuracy of the magnetic field reconstruction are defined. Recommendations on the magnetic scheme design for the field mapping are given. (author)
Total variation superiorization schemes in proton computed tomography image reconstruction
Penfold, S N; Censor, Y; Rosenfeld, A B
2010-01-01
Purpose: Iterative projection reconstruction algorithms are currently the preferred reconstruction method in proton computed tomography (pCT). However, due to inconsistencies in the measured data arising from proton energy straggling and multiple Coulomb scattering, noise in the reconstructed image increases with successive iterations. In the current work, we investigated the use of total variation superiorization (TVS) schemes that can be applied as an algorithmic add-on to perturbation-resilient iterative projection algorithms for pCT image reconstruction. Methods: The block-iterative diagonally relaxed orthogonal projections (DROP) algorithm was used for reconstructing Geant4 Monte Carlo simulated pCT data sets. Two TVS schemes added on to DROP were investigated; the first carried out the superiorization steps once per cycle and the second once per block. Simplifications of these schemes, involving the elimination of the computationally expensive feasibility proximity checking step of the TVS framework, we...
Fast MR Spectroscopic Imaging Technologies and Data Reconstruction Methods
HUANGMin; LUSong-tao; LINJia-rui; ZHANYing-jian
2004-01-01
MRSI plays a more and more important role in clinical application. In this paper, we compare several fast MRSI technologies and data reconstruction methods. For the conventional phase encoding MRSI, the data reconstruction using FFT is simple. But the data acquisition is very time consuming and thus prohibitive in clinical settings. Up to now, the MRSI technologies based on echo-planar, spiral trajectories and sensitivity encoding are the fastest in data acquisition, but their data reconstruction is complex. EPSI reconstruction uses shift of odd and even echoes. Spiral SI uses gridding FFT. SENSE-SI, a new approach to reducing the acquisition time, uses the distinct spatial sensitivities of the individual coil elements to recover the missing encoding information. These improvements in data acquisition and image reconstruction provide a potential value of metabolic imaging as a clinical tool.
On-line cone-beam computed tomography (CBCT) may be used to reconstruct the dose for geometric changes of patients and tumors during radiotherapy course. This study is to establish a practical method to modify the CBCT for accurate dose calculation in head and neck cancer. Fan-beam CT (FBCT) and Elekta's CBCT were used to acquire images. The CT numbers for different materials on CBCT were mathematically modified to match them with FBCT. Three phantoms were scanned by FBCT and CBCT for image uniformity, spatial resolution, and CT numbers, and to compare the dose distribution from orthogonal beams. A Rando phantom was scanned and planned with intensity-modulated radiation therapy (IMRT). Finally, two nasopharyngeal cancer patients treated with IMRT had their CBCT image sets calculated for dose comparison. With 360 acquisition of CBCT and high-resolution reconstruction, the uniformity of CT number distribution was improved and the otherwise large variations for background and high-density materials were reduced significantly. The dose difference between FBCT and CBCT was < 2% in phantoms. In the Rando phantom and the patients, the dose-volume histograms were similar. The corresponding isodose curves covering ≥ 90% of prescribed dose on FBCT and CBCT were close to each other (within 2 mm). Most dosimetric differences were from the setup errors related to the interval changes in body shape and tumor response. The specific CBCT acquisition, reconstruction, and CT number modification can generate accurate dose calculation for the potential use in adaptive radiotherapy.
Hu, Chih-Chung [National Taiwan Univ. Hospital and College of Medicine, Taipei (China). Division of Radiation Oncology; Yuanpei Univ., Hsinchu (China). Dept. of Radiological Technology; Huang, Wen-Tao [Yuanpei Univ., Hsinchu (China). Dept. of Radiological Technology; Tsai, Chiao-Ling; Chao, Hsiao-Ling; Huang, Guo-Ming; Wang, Chun-Wei [National Taiwan Univ. Hospital and College of Medicine, Taipei (China). Division of Radiation Oncology; Wu, Jian-Kuen [National Taiwan Univ. Hospital and College of Medicine, Taipei (China). Division of Radiation Oncology; National Taiwan Normal Univ., Taipei (China). Inst. of Electro-Optical Science and Technology; Wu, Chien-Jang [National Taiwan Normal Univ., Taipei (China). Inst. of Electro-Optical Science and Technology; Cheng, Jason Chia-Hsien [National Taiwan Univ. Hospital and College of Medicine, Taipei (China). Division of Radiation Oncology; National Taiwan Univ. Taipei (China). Graduate Inst. of Oncology; National Taiwan Univ. Taipei (China). Graduate Inst. of Clinical Medicine; National Taiwan Univ. Taipei (China). Graduate Inst. of Biomedical Electronics and Bioinformatics
2011-10-15
On-line cone-beam computed tomography (CBCT) may be used to reconstruct the dose for geometric changes of patients and tumors during radiotherapy course. This study is to establish a practical method to modify the CBCT for accurate dose calculation in head and neck cancer. Fan-beam CT (FBCT) and Elekta's CBCT were used to acquire images. The CT numbers for different materials on CBCT were mathematically modified to match them with FBCT. Three phantoms were scanned by FBCT and CBCT for image uniformity, spatial resolution, and CT numbers, and to compare the dose distribution from orthogonal beams. A Rando phantom was scanned and planned with intensity-modulated radiation therapy (IMRT). Finally, two nasopharyngeal cancer patients treated with IMRT had their CBCT image sets calculated for dose comparison. With 360 acquisition of CBCT and high-resolution reconstruction, the uniformity of CT number distribution was improved and the otherwise large variations for background and high-density materials were reduced significantly. The dose difference between FBCT and CBCT was < 2% in phantoms. In the Rando phantom and the patients, the dose-volume histograms were similar. The corresponding isodose curves covering {>=} 90% of prescribed dose on FBCT and CBCT were close to each other (within 2 mm). Most dosimetric differences were from the setup errors related to the interval changes in body shape and tumor response. The specific CBCT acquisition, reconstruction, and CT number modification can generate accurate dose calculation for the potential use in adaptive radiotherapy.
A novel building boundary reconstruction method based on lidar data and images
Chen, Yiming; Zhang, Wuming; Zhou, Guoqing; Yan, Guangjian
2013-09-01
Building boundary is important for the urban mapping and real estate industry applications. The reconstruction of building boundary is also a significant but difficult step in generating city building models. As Light detection and ranging system (Lidar) can acquire large and dense point cloud data fast and easily, it has great advantages for building reconstruction. In this paper, we combine Lidar data and images to develop a novel building boundary reconstruction method. We use only one scan of Lidar data and one image to do the reconstruction. The process consists of a sequence of three steps: project boundary Lidar points to image; extract accurate boundary from image; and reconstruct boundary in Lidar points. We define a relationship between 3D points and the pixel coordinates. Then we extract the boundary in the image and use the relationship to get boundary in the point cloud. The method presented here reduces the difficulty of data acquisition effectively. The theory is not complex so it has low computational complexity. It can also be widely used in the data acquired by other 3D scanning devices to improve the accuracy. Results of the experiment demonstrate that this method has a clear advantage and high efficiency over others, particularly in the data with large point spacing.
Accurate Image Retrieval Algorithm Based on Color and Texture Feature
Chunlai Yan
2013-06-01
Full Text Available Content-Based Image Retrieval (CBIR is one of the most active hot spots in the current research field of multimedia retrieval. According to the description and extraction of visual content (feature of the image, CBIR aims to find images that contain specified content (feature in the image database. In this paper, several key technologies of CBIR, e. g. the extraction of the color and texture features of the image, as well as the similarity measures are investigated. On the basis of the theoretical research, an image retrieval system based on color and texture features is designed. In this system, the Weighted Color Feature based on HSV space is adopted as a color feature vector, four features of the Co-occurrence Matrix, saying Energy, Entropy, Inertia Quadrature and Correlation, are used to construct texture vectors, and the Euclidean distance for similarity measure is employed as well. Experimental results show that this CBIR system is efficient in image retrieval.
Comparison of image reformation using personal computer with CT scan reconstruction
Radiographic planning is needed for implant placement in order to determine implant length, jaw bone volume, anatomical structure and so on. Radiographic examination includes conventional radiography, conventional tomography and CT scan. The most accurate measurement can be obtained from CT scan. For the cross-sectional view of mandible, CT scan reconstruction is generally needed. But the cross-sectional view of mandible can be reformed by personal computer. This study was performed to examine the clinical usefulness of reformed image using personal computer in comparison with CT scan reconstructed image. CT axial slices of 4 mandibles of 4 volunteers were used. Digital imaging system was composed of Macintosh II ci computer, high resolution Sony XC-77 CCD camera, Quick Capture frame grabber board and 'NIH Image' program. Seven reconstructed cross-sectional images within CT machine (CT group) were obtained. And seven reformed cross-sectional images (PC group) after digitization of CT axial slices into the personal computer were obtained. PC group was compared with CT group in the objective and subjective aspects. The results were as follow: 1. Measurement of mandibular height and width in both group showed insignificant difference (P>0.05). 2. Subjective assessment of the mandibular canal in both group showed insignificant difference (P>0.05). 3. Image reformation using personal computer could provide panoramic view, which could not be obtained in CT scan reconstruction.
Super Resolution Image Reconstruction using LWT
Padavala, Sivakrishna; Moghul, Arifullah Baig
2013-01-01
Since over three decades, computers have been widely used for processing and displaying images. The ability to process visual information from a super resolution image can enhance the information present in the image. The motivation is from a human eye which takes in raw images (noisy, blurred and translated) and constructs a super resolution image. An image with improved resolution is desired in almost all of the applications to enhance qualitative features and is reported to be achieved by ...
Intensity-modulated radiation therapy (IMRT) radiation treatment planning (RTP) requires accuracy. Metal artifacts are one of the factors that influence RTP. The metal artifacts from dental structures are problems at the level of the oropharynx, since these artifacts impair visualization of tumors or lymph nodes and change CT (computed tomography) values. We simulated RTP at the level of the oropharynx using CT images with and without artifacts from dental structures. Gantry tilt scanning was performed to avoid artifacts from dental structures and transverse images reconstructed from oblique images by gantry tilt scanning using a technique of multiplanar reconstruction (MPR). The reconstructed transverse images were used for the RTP. The reconstructed transverse images were useful to obtain accurate target volumes and the RTP of two opposed equally weighted beams by correct CT values. As dose distribution was changed slightly by the metal artifacts, the use of CT images without artifact is recommended in RTP. (author)
Application of Super-Resolution Image Reconstruction to Digital Holography
Zhang Shuqun
2006-01-01
Full Text Available We describe a new application of super-resolution image reconstruction to digital holography which is a technique for three-dimensional information recording and reconstruction. Digital holography has suffered from the low resolution of CCD sensors, which significantly limits the size of objects that can be recorded. The existing solution to this problem is to use optics to bandlimit the object to be recorded, which can cause the loss of details. Here super-resolution image reconstruction is proposed to be applied in enhancing the spatial resolution of digital holograms. By introducing a global camera translation before sampling, a high-resolution hologram can be reconstructed from a set of undersampled hologram images. This permits the recording of larger objects and reduces the distance between the object and the hologram. Practical results from real and simulated holograms are presented to demonstrate the feasibility of the proposed technique.
Matrix-based image reconstruction methods for tomography
Matrix methods of image reconstruction have not been used, in general, because of the large size of practical matrices, ill condition upon inversion and the success of Fourier-based techniques. An exception is the work that has been done at the Lawrence Berkeley Laboratory for imaging with accelerated radioactive ions. An extension of that work into more general imaging problems shows that, with a correct formulation of the problem, positron tomography with ring geometries results in well behaved matrices which can be used for image reconstruction with no distortion of the point response in the field of view and flexibility in the design of the instrument. Maximum Likelihood Estimator methods of reconstruction, which use the system matrices tailored to specific instruments and do not need matrix inversion, are shown to result in good preliminary images. A parallel processing computer structure based on multiple inexpensive microprocessors is proposed as a system to implement the matrix-MLE methods. 14 references, 7 figures
Compensation for air voids in photoacoustic computed tomography image reconstruction
Matthews, Thomas P.; Li, Lei; Wang, Lihong V.; Anastasio, Mark A.
2016-03-01
Most image reconstruction methods in photoacoustic computed tomography (PACT) assume that the acoustic properties of the object and the surrounding medium are homogeneous. This can lead to strong artifacts in the reconstructed images when there are significant variations in sound speed or density. Air voids represent a particular challenge due to the severity of the differences between the acoustic properties of air and water. In whole-body small animal imaging, the presence of air voids in the lungs, stomach, and gastrointestinal system can limit image quality over large regions of the object. Iterative reconstruction methods based on the photoacoustic wave equation can account for these acoustic variations, leading to improved resolution, improved contrast, and a reduction in the number of imaging artifacts. However, the strong acoustic heterogeneities can lead to instability or errors in the numerical wave solver. Here, the impact of air voids on PACT image reconstruction is investigated, and procedures for their compensation are proposed. The contributions of sound speed and density variations to the numerical stability of the wave solver are considered, and a novel approach for mitigating the impact of air voids while reducing the computational burden of image reconstruction is identified. These results are verified by application to an experimental phantom.
Electrophysiology Catheter Detection and Reconstruction From Two Views in Fluoroscopic Images.
Hoffmann, Matthias; Brost, Alexander; Koch, Martin; Bourier, Felix; Maier, Andreas; Kurzidim, Klaus; Strobel, Norbert; Hornegger, Joachim
2016-02-01
Electrophysiology (EP) studies and catheter ablation have become important treatment options for several types of cardiac arrhythmias. We present a novel image-based approach for automatic detection and 3-D reconstruction of EP catheters where the physician marks the catheter to be reconstructed by a single click in each image. The result can be used to provide 3-D information for enhanced navigation throughout EP procedures. Our approach involves two X-ray projections acquired from different angles, and it is based on two steps: First, we detect the catheter in each view after manual initialization using a graph-search method. Then, the detection results are used to reconstruct a full 3-D model of the catheter based on automatically determined point pairs for triangulation. An evaluation on 176 different clinical fluoroscopic images yielded a detection rate of 83.4%. For measuring the error, we used the coupling distance which is a more accurate quality measure than the average point-wise distance to a reference. For successful outcomes, the 2-D detection error was 1.7 mm ±1.2 mm. Using successfully detected catheters for reconstruction, we obtained a reconstruction error of 1.8 mm ±1.1 mm on phantom data. On clinical data, our method yielded a reconstruction error of 2.2 mm ±2.2 mm. PMID:26441411
Chris L. de Korte
2013-03-01
Full Text Available Atherosclerotic plaque rupture can initiate stroke or myocardial infarction. Lipid-rich plaques with thin fibrous caps have a higher risk to rupture than fibrotic plaques. Elastic moduli differ for lipid-rich and fibrous tissue and can be reconstructed using tissue displacements estimated from intravascular ultrasound radiofrequency (RF data acquisitions. This study investigated if modulus reconstruction is possible for noninvasive RF acquisitions of vessels in transverse imaging planes using an iterative 2D cross-correlation based displacement estimation algorithm. Furthermore, since it is known that displacements can be improved by compounding of displacements estimated at various beam steering angles, we compared the performance of the modulus reconstruction with and without compounding. For the comparison, simulated and experimental RF data were generated of various vessel-mimicking phantoms. Reconstruction errors were less than 10%, which seems adequate for distinguishing lipid-rich from fibrous tissue. Compounding outperformed single-angle reconstruction: the interquartile range of the reconstructed moduli for the various homogeneous phantom layers was approximately two times smaller. Additionally, the estimated lateral displacements were a factor of 2–3 better matched to the displacements corresponding to the reconstructed modulus distribution. Thus, noninvasive elastic modulus reconstruction is possible for transverse vessel cross sections using this cross-correlation method and is more accurate with compounding.
Bayesian image reconstruction for emission tomography based on median root prior
The aim of the present study was to investigate a new type of Bayesian one-step late reconstruction method which utilizes a median root prior (MRP). The method favours images which have locally monotonous radioactivity concentrations. The new reconstruction algorithm was applied to ideal simulated data, phantom data and some patient examinations with PET. The same projection data were reconstructed with filtered back-projection (FBP) and maximum likelihood-expectation maximization (ML-EM) methods for comparison. The MRP method provided good-quality images with a similar resolution to the FBP method with a ramp filter, and at the same time the noise properties were as good as with Hann-filtered FBP images. The typical artefacts seen in FBP reconstructed images outside of the object were completely removed, as was the grainy noise inside the object. Quantitativley, the resulting average regional radioactivity concentrations in a large region of interest in images produced by the MRP method corresponded to the FBP and ML-EM results but at the pixel by pixel level the MRP method proved to be the most accurate of the tested methods. In contrast to other iterative reconstruction methods, e.g. ML-EM, the MRP method was not sensitive to the number of iterations nor to the adjustment of reconstruction parameters. Only the Bayesian parameter β had to be set. The proposed MRP method is much more simple to calculate than the methods described previously, both with regard to the parameter settings and in terms of general use. The new MRP reconstruction method was shown to produce high-quality quantitative emission images with only one parameter setting in addition to the number of iterations. (orig.)
MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods
Schmidt, Johannes F. M.; Claudio Santelli; Sebastian Kozerke
2016-01-01
An approach to Magnetic Resonance (MR) image reconstruction from undersampled data is proposed. Undersampling artifacts are removed using an iterative thresholding algorithm applied to nonlinearly transformed image block arrays. Each block array is transformed using kernel principal component analysis where the contribution of each image block to the transform depends in a nonlinear fashion on the distance to other image blocks. Elimination of undersampling artifacts is achieved by convention...
The main objective of this article is to improve the stability of reconstruction algorithms for estimation of radiobiological parameters using serial tumor imaging data acquired during radiation therapy. Serial images of tumor response to radiation therapy represent a complex summation of several exponential processes as treatment induced cell inactivation, tumor growth rates, and the rate of cell loss. Accurate assessment of treatment response would require separation of these processes because they define radiobiological determinants of treatment response and, correspondingly, tumor control probability. However, the estimation of radiobiological parameters using imaging data can be considered an inverse ill-posed problem because a sum of several exponentials would produce the Fredholm integral equation of the first kind which is ill posed. Therefore, the stability of reconstruction of radiobiological parameters presents a problem even for the simplest models of tumor response. To study stability of the parameter reconstruction problem, we used a set of serial CT imaging data for head and neck cancer and a simplest case of a two-level cell population model of tumor response. Inverse reconstruction was performed using a simulated annealing algorithm to minimize a least squared objective function. Results show that the reconstructed values of cell surviving fractions and cell doubling time exhibit significant nonphysical fluctuations if no stabilization algorithms are applied. However, after applying a stabilization algorithm based on variational regularization, the reconstruction produces statistical distributions for survival fractions and doubling time that are comparable to published in vitro data. This algorithm is an advance over our previous work where only cell surviving fractions were reconstructed. We conclude that variational regularization allows for an increase in the number of free parameters in our model which enables development of more
Chvetsov, Alevei V.; Sandison, George A.; Schwartz, Jeffrey L.; Rengan, Ramesh
2015-11-01
The main objective of this article is to improve the stability of reconstruction algorithms for estimation of radiobiological parameters using serial tumor imaging data acquired during radiation therapy. Serial images of tumor response to radiation therapy represent a complex summation of several exponential processes as treatment induced cell inactivation, tumor growth rates, and the rate of cell loss. Accurate assessment of treatment response would require separation of these processes because they define radiobiological determinants of treatment response and, correspondingly, tumor control probability. However, the estimation of radiobiological parameters using imaging data can be considered an inverse ill-posed problem because a sum of several exponentials would produce the Fredholm integral equation of the first kind which is ill posed. Therefore, the stability of reconstruction of radiobiological parameters presents a problem even for the simplest models of tumor response. To study stability of the parameter reconstruction problem, we used a set of serial CT imaging data for head and neck cancer and a simplest case of a two-level cell population model of tumor response. Inverse reconstruction was performed using a simulated annealing algorithm to minimize a least squared objective function. Results show that the reconstructed values of cell surviving fractions and cell doubling time exhibit significant nonphysical fluctuations if no stabilization algorithms are applied. However, after applying a stabilization algorithm based on variational regularization, the reconstruction produces statistical distributions for survival fractions and doubling time that are comparable to published in vitro data. This algorithm is an advance over our previous work where only cell surviving fractions were reconstructed. We conclude that variational regularization allows for an increase in the number of free parameters in our model which enables development of more
Compressed sensing MR image reconstruction exploiting TGV and wavelet sparsity.
Zhao, Di; Du, Huiqian; Han, Yu; Mei, Wenbo
2014-01-01
Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate. In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains. The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations. In addition, we apply total generalized variation (TGV) regularization to eliminate the staircasing artifacts caused by conventional total variation (TV). Fast composite splitting algorithm (FCSA) is used to solve the proposed reconstruction problem in order to improve computational efficiency. Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively. PMID:25371704
Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity
Di Zhao
2014-01-01
Full Text Available Compressed sensing (CS based methods make it possible to reconstruct magnetic resonance (MR images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate. In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains. The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations. In addition, we apply total generalized variation (TGV regularization to eliminate the staircasing artifacts caused by conventional total variation (TV. Fast composite splitting algorithm (FCSA is used to solve the proposed reconstruction problem in order to improve computational efficiency. Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively.
Influence of Iterative Reconstruction Algorithms on PET Image Resolution
Karpetas, G. E.; Michail, C. M.; Fountos, G. P.; Valais, I. G.; Nikolopoulos, D.; Kandarakis, I. S.; Panayiotakis, G. S.
2015-09-01
The aim of the present study was to assess image quality of PET scanners through a thin layer chromatography (TLC) plane source. The source was simulated using a previously validated Monte Carlo model. The model was developed by using the GATE MC package and reconstructed images obtained with the STIR software for tomographic image reconstruction. The simulated PET scanner was the GE DiscoveryST. A plane source consisted of a TLC plate, was simulated by a layer of silica gel on aluminum (Al) foil substrates, immersed in 18F-FDG bath solution (1MBq). Image quality was assessed in terms of the modulation transfer function (MTF). MTF curves were estimated from transverse reconstructed images of the plane source. Images were reconstructed by the maximum likelihood estimation (MLE)-OSMAPOSL, the ordered subsets separable paraboloidal surrogate (OSSPS), the median root prior (MRP) and OSMAPOSL with quadratic prior, algorithms. OSMAPOSL reconstruction was assessed by using fixed subsets and various iterations, as well as by using various beta (hyper) parameter values. MTF values were found to increase with increasing iterations. MTF also improves by using lower beta values. The simulated PET evaluation method, based on the TLC plane source, can be useful in the resolution assessment of PET scanners.
SPECT data acquisition and image reconstruction in a stationary small animal SPECT/MRI system
Xu, Jingyan; Chen, Si; Yu, Jianhua; Meier, Dirk; Wagenaar, Douglas J.; Patt, Bradley E.; Tsui, Benjamin M. W.
2010-04-01
The goal of the study was to investigate data acquisition strategies and image reconstruction methods for a stationary SPECT insert that can operate inside an MRI scanner with a 12 cm bore diameter for simultaneous SPECT/MRI imaging of small animals. The SPECT insert consists of 3 octagonal rings of 8 MR-compatible CZT detectors per ring surrounding a multi-pinhole (MPH) collimator sleeve. Each pinhole is constructed to project the field-of-view (FOV) to one CZT detector. All 24 pinholes are focused to a cylindrical FOV of 25 mm in diameter and 34 mm in length. The data acquisition strategies we evaluated were optional collimator rotations to improve tomographic sampling; and the image reconstruction methods were iterative ML-EM with and without compensation for the geometric response function (GRF) of the MPH collimator. For this purpose, we developed an analytic simulator that calculates the system matrix with the GRF models of the MPH collimator. The simulator was used to generate projection data of a digital rod phantom with pinhole aperture sizes of 1 mm and 2 mm and with different collimator rotation patterns. Iterative ML-EM reconstruction with and without GRF compensation were used to reconstruct the projection data from the central ring of 8 detectors only, and from all 24 detectors. Our results indicated that without GRF compensation and at the default design of 24 projection views, the reconstructed images had significant artifacts. Accurate GRF compensation substantially improved the reconstructed image resolution and reduced image artifacts. With accurate GRF compensation, useful reconstructed images can be obtained using 24 projection views only. This last finding potentially enables dynamic SPECT (and/or MRI) studies in small animals, one of many possible application areas of the SPECT/MRI system. Further research efforts are warranted including experimentally measuring the system matrix for improved geometrical accuracy, incorporating the co
3D image reconstruction of fiber systems using electron tomography
Over the past several years, electron microscopists and materials researchers have shown increased interest in electron tomography (reconstruction of three-dimensional information from a tilt series of bright field images obtained in a transmission electron microscope (TEM)). In this research, electron tomography has been used to reconstruct a three-dimensional image for fiber structures from secondary electron images in a scanning electron microscope (SEM). The implementation of this technique is used to examine the structure of fiber system before and after deformation. A test sample of steel wool was tilted around a single axis from −10° to 60° by one-degree steps with images taken at every degree; three-dimensional images were reconstructed for the specimen of fine steel fibers. This method is capable of reconstructing the three-dimensional morphology of this type of lineal structure, and to obtain features such as tortuosity, contact points, and linear density that are of importance in defining the mechanical properties of these materials. - Highlights: • The electron tomography technique has been adapted to the SEM for analysis of linear structures. • Images are obtained by secondary electron imaging through a given depth of field, making them analogous to projected images. • Quantitative descriptions of the microstructure can be obtained including tortuosity and contact points per volume
Three-dimensional image reconstruction for scattering light tomography
For optical imaging of testes we have developed three-dimensional image reconstruction algorithms for scattering light tomography in the near-infrared range. The mathematical problem is thereby decomposed into a pixel conversion step and an inverse problem solver. Different voxal bases and solution methods have been selected and evaluated. (author)
Distributed image reconstruction for very large arrays in radio astronomy
Ferrari, André; Flamary, Rémi; Richard, Cédric
2015-01-01
Current and future radio interferometric arrays such as LOFAR and SKA are characterized by a paradox. Their large number of receptors (up to millions) allow theoretically unprecedented high imaging resolution. In the same time, the ultra massive amounts of samples makes the data transfer and computational loads (correlation and calibration) order of magnitudes too high to allow any currently existing image reconstruction algorithm to achieve, or even approach, the theoretical resolution. We investigate here decentralized and distributed image reconstruction strategies which select, transfer and process only a fraction of the total data. The loss in MSE incurred by the proposed approach is evaluated theoretically and numerically on simple test cases.
Analytic 3D image reconstruction using all detected events
We present the results of testing a previously presented algorithm for three-dimensional image reconstruction that uses all gamma-ray coincidence events detected by a PET volume-imaging scanner. By using two iterations of an analytic filter-backprojection method, the algorithm is not constrained by the requirement of a spatially invariant detector point spread function, which limits normal analytic techniques. Removing this constraint allows the incorporation of all detected events, regardless of orientation, which improves the statistical quality of the final reconstructed image
An image correlation procedure for digitally reconstructed radiographs and electronic portal images
Purpose: To study a procedure that uses megavoltage digitally reconstructed radiographs (DRRs) calculated from patient's three-dimensional (3D) computed tomography (CT) data as a reference image for correlation with on-line electronic portal images (EPIs) to detect patient setup errors. Methods and Materials: Megavoltage DRRs were generated by ray tracing through a modified volumetric CT data set in which CT numbers were converted into linear attenuation coefficients for the therapeutic beam energy. The DRR transmission image was transformed to the grayscale window of the EPI by a histogram-matching technique. An alternative approach was to calibrate the transmission DRR using a measured response curve of the electronic portal imaging device (EPID). This forces the calculated transmission fluence values to be distributed in the same range as that of the EPID image. A cross-correlation technique was used to determine the degree of alignment of the patient anatomy found in the EPID image relative to the reference DRR. Results: Phantom studies demonstrated that the correlation procedure had a standard deviation of 0.5 mm and 0.5 deg. in aligning translational shifts and in-plane rotations. Systematic errors were found between a reference DRR and a reference EPID image. The automated grayscale image-correlation process was completed within 3 s on a workstation computer or 12 s on a PC. Conclusion: The alignment procedure allows the direct comparison of a patient's treatment portal designed with a 3D planning computer with a patient's on-line portal image acquired at the treatment unit. The image registration process is automated to the extent that it requires minimal user intervention, and it is fast and accurate enough for on-line clinical applications
Cervigram image segmentation based on reconstructive sparse representations
Zhang, Shaoting; Huang, Junzhou; Wang, Wei; Huang, Xiaolei; Metaxas, Dimitris
2010-03-01
We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination and specular reflection, the color and texture features in optical images often overlap with each other and are not linearly separable. By leveraging sparse representations the data can be transformed to higher dimensions with sparse constraints and become more separated. K-SVD algorithm is employed to find sparse representations and corresponding dictionaries. The data can be reconstructed from its sparse representations and positive and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In the experiments we applied our method to automatically segment the biomarker AcetoWhite (AW) regions in an archive of 60,000 images of the uterine cervix. Compared with other general methods, our approach showed lower space and time complexity and higher sensitivity.
Large Scale 3D Image Reconstruction in Optical Interferometry
Schutz, Antony; Mary, David; Thiébaut, Eric; Soulez, Ferréol
2015-01-01
Astronomical optical interferometers (OI) sample the Fourier transform of the intensity distribution of a source at the observation wavelength. Because of rapid atmospheric perturbations, the phases of the complex Fourier samples (visibilities) cannot be directly exploited , and instead linear relationships between the phases are used (phase closures and differential phases). Consequently, specific image reconstruction methods have been devised in the last few decades. Modern polychromatic OI instruments are now paving the way to multiwavelength imaging. This paper presents the derivation of a spatio-spectral ("3D") image reconstruction algorithm called PAINTER (Polychromatic opticAl INTErferometric Reconstruction software). The algorithm is able to solve large scale problems. It relies on an iterative process, which alternates estimation of polychromatic images and of complex visibilities. The complex visibilities are not only estimated from squared moduli and closure phases, but also from differential phase...
Analysis Operator Learning and Its Application to Image Reconstruction
Hawe, Simon; Diepold, Klaus
2012-01-01
Exploiting a priori known structural information lies at the core of many image reconstruction methods that can be stated as inverse problems. The synthesis model, which assumes that images can be decomposed into a linear combination of very few atoms of some dictionary, is now a well established tool for the design of image reconstruction algorithms. An interesting alternative is the analysis model, where the signal is multiplied by an analysis operator and the outcome is assumed to be the sparse. This approach has only recently gained increasing interest. The quality of reconstruction methods based on an analysis model severely depends on the right choice of the suitable operator. In this work, we present an algorithm for learning an analysis operator from training images. Our method is based on an $\\ell_p$-norm minimization on the set of full rank matrices with normalized columns. We carefully introduce the employed conjugate gradient method on manifolds, and explain the underlying geometry of the constrai...
A novel automated image analysis method for accurate adipocyte quantification
Osman, Osman S.; Selway, Joanne L; Kępczyńska, Małgorzata A; Stocker, Claire J.; O’Dowd, Jacqueline F; Cawthorne, Michael A.; Arch, Jonathan RS; Jassim, Sabah; Langlands, Kenneth
2013-01-01
Increased adipocyte size and number are associated with many of the adverse effects observed in metabolic disease states. While methods to quantify such changes in the adipocyte are of scientific and clinical interest, manual methods to determine adipocyte size are both laborious and intractable to large scale investigations. Moreover, existing computational methods are not fully automated. We, therefore, developed a novel automatic method to provide accurate measurements of the cross-section...
Propagation Phasor Approach in Holographic Image Reconstruction
Luo, Wei
2016-01-01
High-resolution and wide-field imaging is of great value for various scientific disciplines; such tasks demand the imaging modalities to possess large space-bandwidth products. The rapid evolutions of modern image sensor technologies and computing power have provided tremendous opportunities for development of new imaging systems with significantly larger space-bandwidth products than conventional lens-based systems. This dissertation introduces the latest advance in lensfree holographic micr...
Improving JWST Coronagraphic Performance with Accurate Image Registration
Van Gorkom, Kyle; Pueyo, Laurent; Lajoie, Charles-Philippe; JWST Coronagraphs Working Group
2016-06-01
The coronagraphs on the James Webb Space Telescope (JWST) will enable high-contrast observations of faint objects at small separations from bright hosts, such as circumstellar disks, exoplanets, and quasar disks. Despite attenuation by the coronagraphic mask, bright speckles in the host’s point spread function (PSF) remain, effectively washing out the signal from the faint companion. Suppression of these bright speckles is typically accomplished by repeating the observation with a star that lacks a faint companion, creating a reference PSF that can be subtracted from the science image to reveal any faint objects. Before this reference PSF can be subtracted, however, the science and reference images must be aligned precisely, typically to 1/20 of a pixel. Here, we present several such algorithms for performing image registration on JWST coronagraphic images. Using both simulated and pre-flight test data (taken in cryovacuum), we assess (1) the accuracy of each algorithm at recovering misaligned scenes and (2) the impact of image registration on achievable contrast. Proper image registration, combined with post-processing techniques such as KLIP or LOCI, will greatly improve the performance of the JWST coronagraphs.
Alexandre, E.; Cuadra, L.; Nieto-Borge, J. C.; Candil-García, G.; del Pino, M.; Salcedo-Sanz, S.
2015-08-01
Wave parameters computed from time series measured by buoys (significant wave height Hs, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset FnSP with nSP parameters that minimizes the Hs reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset FnSP are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct Hs. The results show that all the ML method explored achieve a good Hs reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).
Reconstruction of Undersampled Atomic Force Microscopy Images
Jensen, Tobias Lindstrøm; Arildsen, Thomas; Østergaard, Jan;
2013-01-01
Atomic force microscopy (AFM) is one of the most advanced tools for high-resolution imaging and manipulation of nanoscale matter. Unfortunately, standard AFM imaging requires a timescale on the order of seconds to minutes to acquire an image which makes it complicated to observe dynamic processes...
Distributed multi-frequency image reconstruction for radio-interferometry
Deguignet, Jérémy; Mary, David; Ferrari, Chiara
2016-01-01
The advent of enhanced technologies in radio interferometry and the perspective of the SKA telescope bring new challenges in image reconstruction. One of these challenges is the spatio-spectral reconstruction of large (Terabytes) data cubes with high fidelity. This contribution proposes an alternative implementation of one such 3D prototype algorithm, MUFFIN (MUlti-Frequency image reconstruction For radio INterferometry), which combines spatial and spectral analysis priors. Using a recently proposed primal dual algorithm, this new version of MUFFIN allows a parallel implementation where computationally intensive steps are split by spectral channels. This parallelization allows to implement computationally demanding translation invariant wavelet transforms (IUWT), as opposed to the union of bases used previously. This alternative implementation is important as it opens the possibility of comparing these efficient dictionaries, and others, in spatio-spectral reconstruction. Numerical results show that the IUWT-...
Image interface in Java for tomographic reconstruction in nuclear medicine
The aim of this study is to implement a software for tomographic reconstruction of SPECT data from Nuclear Medicine with a flexible interface design, cross-platform, written in Java. Validation tests were performed based on SPECT simulated data. The results showed that the implemented algorithms and filters agree with the theoretical context. We intend to extend the system by implementing additional tomographic reconstruction techniques and Java threads, in order to provide simultaneously image processing. (author)
High resolution image reconstruction with constrained, total-variation minimization
Sidky, Emil Y; Duchin, Yuval; Ullberg, Christer; Pan, Xiaochuan
2011-01-01
This work is concerned with applying iterative image reconstruction, based on constrained total-variation minimization, to low-intensity X-ray CT systems that have a high sampling rate. Such systems pose a challenge for iterative image reconstruction, because a very fine image grid is needed to realize the resolution inherent in such scanners. These image arrays lead to under-determined imaging models whose inversion is unstable and can result in undesirable artifacts and noise patterns. There are many possibilities to stabilize the imaging model, and this work proposes a method which may have an advantage in terms of algorithm efficiency. The proposed method introduces additional constraints in the optimization problem; these constraints set to zero high spatial frequency components which are beyond the sensing capability of the detector. The method is demonstrated with an actual CT data set and compared with another method based on projection up-sampling.
An adaptive filtered back-projection for photoacoustic image reconstruction
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
Pino, Francisco [Unitat de Biofísica, Facultat de Medicina, Universitat de Barcelona, Barcelona 08036, Spain and Servei de Física Mèdica i Protecció Radiològica, Institut Català d’Oncologia, L’Hospitalet de Llobregat 08907 (Spain); Roé, Nuria [Unitat de Biofísica, Facultat de Medicina, Universitat de Barcelona, Barcelona 08036 (Spain); Aguiar, Pablo, E-mail: pablo.aguiar.fernandez@sergas.es [Fundación Ramón Domínguez, Complexo Hospitalario Universitario de Santiago de Compostela 15706, Spain and Grupo de Imagen Molecular, Instituto de Investigacións Sanitarias de Santiago de Compostela (IDIS), Galicia 15782 (Spain); Falcon, Carles; Ros, Domènec [Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain and CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona 08036 (Spain); Pavía, Javier [Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 080836 (Spain); CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona 08036 (Spain); and Servei de Medicina Nuclear, Hospital Clínic, Barcelona 08036 (Spain)
2015-02-15
Purpose: Single photon emission computed tomography (SPECT) has become an important noninvasive imaging technique in small-animal research. Due to the high resolution required in small-animal SPECT systems, the spatially variant system response needs to be included in the reconstruction algorithm. Accurate modeling of the system response should result in a major improvement in the quality of reconstructed images. The aim of this study was to quantitatively assess the impact that an accurate modeling of spatially variant collimator/detector response has on image-quality parameters, using a low magnification SPECT system equipped with a pinhole collimator and a small gamma camera. Methods: Three methods were used to model the point spread function (PSF). For the first, only the geometrical pinhole aperture was included in the PSF. For the second, the septal penetration through the pinhole collimator was added. In the third method, the measured intrinsic detector response was incorporated. Tomographic spatial resolution was evaluated and contrast, recovery coefficients, contrast-to-noise ratio, and noise were quantified using a custom-built NEMA NU 4–2008 image-quality phantom. Results: A high correlation was found between the experimental data corresponding to intrinsic detector response and the fitted values obtained by means of an asymmetric Gaussian distribution. For all PSF models, resolution improved as the distance from the point source to the center of the field of view increased and when the acquisition radius diminished. An improvement of resolution was observed after a minimum of five iterations when the PSF modeling included more corrections. Contrast, recovery coefficients, and contrast-to-noise ratio were better for the same level of noise in the image when more accurate models were included. Ring-type artifacts were observed when the number of iterations exceeded 12. Conclusions: Accurate modeling of the PSF improves resolution, contrast, and recovery
Motion compensation for PET image reconstruction using deformable tetrahedral meshes
Respiratory-induced organ motion is a technical challenge to PET imaging. This motion induces displacements and deformation of the organs tissues, which need to be taken into account when reconstructing the spatial radiation activity. Classical image-based methods that describe motion using deformable image registration (DIR) algorithms cannot fully take into account the non-reproducibility of the respiratory internal organ motion nor the tissue volume variations that occur during breathing. In order to overcome these limitations, various biomechanical models of the respiratory system have been developed in the past decade as an alternative to DIR approaches. In this paper, we describe a new method of correcting motion artefacts in PET image reconstruction adapted to motion estimation models such as those based on the finite element method. In contrast with the DIR-based approaches, the radiation activity was reconstructed on deforming tetrahedral meshes. For this, we have re-formulated the tomographic reconstruction problem by introducing a time-dependent system matrix based calculated using tetrahedral meshes instead of voxelized images. The MLEM algorithm was chosen as the reconstruction method. The simulations performed in this study show that the motion compensated reconstruction based on tetrahedral deformable meshes has the capability to correct motion artefacts. Results demonstrate that, in the case of complex deformations, when large volume variations occur, the developed tetrahedral based method is more appropriate than the classical DIR-based one. This method can be used, together with biomechanical models controlled by external surrogates, to correct motion artefacts in PET images and thus reducing the need for additional internal imaging during the acquisition. (paper)
Wang, Jianhua; Xiao, Zexin
2011-11-01
Due to the limited depth of focus of microscope objective, a series of images taken from different sections and directions are needed to reconstruct 3D microscopy image. In this paper, we present a novel method which utilizes piezoelectric actuator, high magnification microscopy system without mirror and single CCD to observe micro-objects and reconstruct its three-dimensional image. Inverse piezoelectric effect of piezoelectric ceramics have some superior characteristics, such as high positioning resolution, high positioning accuracy, etc. And piezoelectric actuator possess the advantage of small-size, strong-power and easy- to-integrated as well. Based on these points, we designed a 360° rotation and tilt positioning platform. In this platform, Piezoelectric actuator is employed to ensure the positioning accuracy at axis-Z direction. At the same time, Motion of 360° rotation and tilt can be controlled precisely using stepping motor controlling technology. Furthermore, finite element methods (FEM) analyze software--ANSYS is used to analyze the rigidity, stress and structure optimization of the platform. This rotation and tilt mechanical positioning platform can help the single CCD to get clear, complete-view two dimensional images. This method paves the way for three-dimensional reconstruction of micro objects. Experiments demonstrate that this 360° rotation and tilt positioning stage is structure-simple and high-accurate. It can be widely used in micro-structure observing and three-dimensional image reconstruction among mechanics, materials and biology, etc.
Direct reconstruction of the Vienna applicator on MR images
Purpose: To introduce and test a direct reconstruction concept for intracavitary tandem ring applicators in MR image-based brachytherapy treatment planning. Materials and methods: Optical measurements of transparent ring-phantoms provided the geometric relation between source path and the Vienna ring applicator as visible on MRI. For the manual direct reconstruction method (PLATO), the geometry plotted on a transparency was placed on the screen and rotated to fit with visible ring holes. With the software-integrated reconstruction method (OncentraGYN), the applicator geometry was directly used when placing the visible parts of the applicator in the 3D dataset. Clinical feasibility was tested in 10 clinical insertions. Reconstruction and dose calculation were performed independently on two treatment planning systems (PLATO and OncentraGYN) using MRI alone. DVH parameters for targets and organs at risk were analysed and compared to the clinically used radiograph/MRI registration-based method. Results: The direct reconstruction concept for both methods was feasible and reduced treatment planning time. Evaluated DVH parameters for plans using direct reconstruction methods differed from clinically used plans (traditional reconstruction) in mean differences ≤0.2 Gy for plans with 7 Gy prescribed dose. Conclusion: If the relation between applicator shape visible on MRI and the source path is defined once, the reconstruction process can be performed by directly placing the applicator in the MRI dataset.
Worby, Colin J.; Marc Lipsitch; Hanage, William P
2014-01-01
The prospect of using whole genome sequence data to investigate bacterial disease outbreaks has been keenly anticipated in many quarters, and the large-scale collection and sequencing of isolates from cases is becoming increasingly feasible. While sequence data can provide many important insights into disease spread and pathogen adaptation, it remains unclear how successfully they may be used to estimate individual routes of transmission. Several studies have attempted to reconstruct transmis...
Worby, Colin J.; Marc Lipsitch; William P Hanage
2014-01-01
The prospect of using whole genome sequence data to investigate bacterial disease outbreaks has been keenly anticipated in many quarters, and the large-scale collection and sequencing of isolates from cases is becoming increasingly feasible. While sequence data can provide many important insights into disease spread and pathogen adaptation, it remains unclear how successfully they may be used to estimate individual routes of transmission. Several studies have attempted to reconstruct transmis...
Improved proton computed tomography by dual modality image reconstruction
Hansen, David Christoffer; Bassler, Niels; Petersen, Jørgen B.B.;
2014-01-01
Purpose: Proton computed tomography (CT) is a promising image modality for improving the stopping power estimates and dose calculations for particle therapy. However, the finite range of about 33 cm of water of most commercial proton therapy systems limits the sites that can be scanned from a full...... 360◦ rotation. In this paper the authors propose a method to overcome the problem using a dual modality reconstruction (DMR) combining the proton data with a cone-beam x-ray prior. Methods: A Catphan 600 phantom was scanned using a cone beam x-ray CT scanner. A digital replica of the phantom was...... created in the Monte Carlo code Geant4 and a 360◦ proton CT scan was simulated, storing the entrance and exit position and momentum vector of every proton. Proton CT images were reconstructed using a varying number of angles from the scan. The proton CT images were reconstructed using a constrained...
Reconstruction of CT images by the Bayes- back projection method
Haruyama, M; Takase, M; Tobita, H
2002-01-01
In the course of research on quantitative assay of non-destructive measurement of radioactive waste, the have developed a unique program based on the Bayesian theory for reconstruction of transmission computed tomography (TCT) image. The reconstruction of cross-section images in the CT technology usually employs the Filtered Back Projection method. The new imaging reconstruction program reported here is based on the Bayesian Back Projection method, and it has a function of iterative improvement images by every step of measurement. Namely, this method has the capability of prompt display of a cross-section image corresponding to each angled projection data from every measurement. Hence, it is possible to observe an improved cross-section view by reflecting each projection data in almost real time. From the basic theory of Baysian Back Projection method, it can be not only applied to CT types of 1st, 2nd, and 3rd generation. This reported deals with a reconstruction program of cross-section images in the CT of ...
Sparse representation for the ISAR image reconstruction
Hu, Mengqi; Montalbo, John; Li, Shuxia; Sun, Ligang; Qiao, Zhijun G.
2016-05-01
In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.
Sykes, L.R.; Deng, J. (Lamont-Doherty Earth Observatory, Palisades, NY (United States) Columbia Univ., New York, NY (United States)); Lyubomirskiy, P. (Lamont-Doherty Earth Observatory, Palisades, NY (United States))
1993-09-15
This paper reports on the accurate location of ten large tamped nuclear explosions near Azgir, Kazakhstan, conducted by the former Soviet Union in salt domes. The events are located from shot points on a SPOT satellite image, and from reconstructed seismic events recorded on seismographs scattered around the world, including recently released data from the Soviet Union. A concern behind the location of these events, is the possibility that the caverns created by these shots might be used for seismically decoupled testing of nuclear explosions in the future.
Relevance of accurate Monte Carlo modeling in nuclear medical imaging
Zaidi, H
1999-01-01
Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), and multiple emission tomography (MET) are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. This paper presents derivation and methodological basis for this approach and critically reviews their areas of application in nuclear imaging. An ...
Flame Reconstruction Using Synthetic Aperture Imaging
Murray, Preston; Tree, Dale; Truscott, Tadd
2011-01-01
Flames can be formed by burning methane (CH4). When oxygen is scarce, carbon particles nucleate into solid particles called soot. These particles emit photons, making the flame yellow. Later, methane is pre-mixed with air forming a blue flame; burning more efficiently, providing less soot and light. Imaging flames and knowing their temperature are vital to maximizing efficiency and validating numerical models. Most temperature probes disrupt the flame and create differences leading to an inaccurate measurement of the flame temperature. We seek to image the flame in three dimensions using synthetic aperture imaging. This technique has already successfully measured velocity fields of a vortex ring [1]. Synthetic aperture imaging is a technique that views one scene from multiple cameras set at different angles, allowing some cameras to view objects that are obscured by others. As the resulting images are overlapped different depths of the scene come into and out of focus, known as focal planes, similar to tomogr...
Purpose: Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise (and thereby patient dose), to reduce artifacts, or to improve spatial resolution. Among vendors and researchers, however, there is no consensus of how to best achieve these aims. The general approach is to incorporatea priori knowledge into iterative image reconstruction, for example, by adding additional constraints to the cost function, which penalize variations between neighboring voxels. However, this approach to regularization in general poses a resolution noise trade-off because the stronger the regularization, and thus the noise reduction, the stronger the loss of spatial resolution and thus loss of anatomical detail. The authors propose a method which tries to improve this trade-off. The proposed reconstruction algorithm is called alpha image reconstruction (AIR). One starts with generating basis images, which emphasize certain desired image properties, like high resolution or low noise. The AIR algorithm reconstructs voxel-specific weighting coefficients that are applied to combine the basis images. By combining the desired properties of each basis image, one can generate an image with lower noise and maintained high contrast resolution thus improving the resolution noise trade-off. Methods: All simulations and reconstructions are performed in native fan-beam geometry. A water phantom with resolution bar patterns and low contrast disks is simulated. A filtered backprojection (FBP) reconstruction with a Ram-Lak kernel is used as a reference reconstruction. The results of AIR are compared against the FBP results and against a penalized weighted least squares reconstruction which uses total variation as regularization. The simulations are based on the geometry of the Siemens Somatom Definition Flash scanner. To quantitatively assess image quality, the authors analyze line profiles through resolution patterns to define a contrast
A rapid reconstruction algorithm for three-dimensional scanning images
Xiang, Jiying; Wu, Zhen; Zhang, Ping; Huang, Dexiu
1998-04-01
A `simulated fluorescence' three-dimensional reconstruction algorithm, which is especially suitable for confocal images of partial transparent biological samples, is proposed in this paper. To make the retina projection of the object reappear and to avoid excessive memory consumption, the original image is rotated and compressed before the processing. A left image and a right image are mixed by different colors to increase the sense of stereo. The details originally hidden in deep layers are well exhibited with the aid of an `auxiliary directional source'. In addition, the time consumption is greatly reduced compared with conventional methods such as `ray tracing'. The realization of the algorithm is interpreted by a group of reconstructed images.
Gadgetron: An Open Source Framework for Medical Image Reconstruction
Hansen, Michael Schacht; Sørensen, Thomas Sangild
2013-01-01
recompiling the basic framework infrastructure. Gadgets are typically implemented in C/C++, but the framework includes wrapper Gadgets that allow the user to implement new modules in the Python scripting language for rapid prototyping. In addition to the streaming framework infrastructure, the Gadgetron comes......This work presents a new open source framework for medical image reconstruction called the “Gadgetron.” The framework implements a flexible system for creating streaming data processing pipelines where data pass through a series of modules or “Gadgets” from raw data to reconstructed images. The...
Image reconstruction in PET using time of flight information
Recent progresses in fast time coincidence technique have permitted the use of time of flight (TOF) information in positron Emission Tomography. We describe the basic concept of positron time of flight imaging and introduce new concepts in order to incorporate the TOF data in the reconstruction process. An algorithm to recover positron activity is then proposed. We describe the image reconstruction in the TTVO1 time of flight camera, the system architecture and the special purpose operators. The time of flight tomography offers large development possibilities and we look forward the new high resolution, high signal-to-noise TOF camera
Murase, Kenya
2016-01-01
The purpose of this study was to present image reconstruction methods for magnetic particle imaging (MPI) with a field-free-line (FFL) encoding scheme and to propose the use of the maximum likelihood-expectation maximization (ML-EM) algorithm for improving the image quality of MPI. The feasibility of these methods was investigated by computer simulations, in which the projection data were generated by summing up the Fourier harmonics obtained from the MPI signals based on the Langevin function. Images were reconstructed from the generated projection data using the filtered backprojection (FBP) method and the ML-EM algorithm. The effects of the gradient of selection magnetic field (SMF), the strength of drive magnetic field (DMF), the diameter of magnetic nanoparticles (MNPs), and the number of projection data on the image quality of the reconstructed images were investigated. The spatial resolution of the reconstructed images became better with increasing gradient of SMF and with increasing diameter of MNPs u...
Blockwise conjugate gradient methods for image reconstruction in volumetric CT.
Qiu, W; Titley-Peloquin, D; Soleimani, M
2012-11-01
Cone beam computed tomography (CBCT) enables volumetric image reconstruction from 2D projection data and plays an important role in image guided radiation therapy (IGRT). Filtered back projection is still the most frequently used algorithm in applications. The algorithm discretizes the scanning process (forward projection) into a system of linear equations, which must then be solved to recover images from measured projection data. The conjugate gradients (CG) algorithm and its variants can be used to solve (possibly regularized) linear systems of equations Ax=b and linear least squares problems minx∥b-Ax∥2, especially when the matrix A is very large and sparse. Their applications can be found in a general CT context, but in tomography problems (e.g. CBCT reconstruction) they have not widely been used. Hence, CBCT reconstruction using the CG-type algorithm LSQR was implemented and studied in this paper. In CBCT reconstruction, the main computational challenge is that the matrix A usually is very large, and storing it in full requires an amount of memory well beyond the reach of commodity computers. Because of these memory capacity constraints, only a small fraction of the weighting matrix A is typically used, leading to a poor reconstruction. In this paper, to overcome this difficulty, the matrix A is partitioned and stored blockwise, and blockwise matrix-vector multiplications are implemented within LSQR. This implementation allows us to use the full weighting matrix A for CBCT reconstruction without further enhancing computer standards. Tikhonov regularization can also be implemented in this fashion, and can produce significant improvement in the reconstructed images. PMID:22325240
Alpuche Aviles, Jorge E; Pistorius, Stephen; Gordon, Richard; Elbakri, Idris A
2011-01-01
This work presents a first generation incoherent scatter CT (ISCT) hybrid (analytic-iterative) reconstruction algorithm for accurate ρ{e}imaging of objects with clinically relevant sizes. The algorithm reconstructs quantitative images of ρ{e} within a few iterations, avoiding the challenges of optimization based reconstruction algorithms while addressing the limitations of current analytical algorithms. A 4π detector is conceptualized in order to address the issue of directional dependency and is then replaced with a ring of detectors which detect a constant fraction of the scattered photons. The ISCT algorithm corrects for the attenuation of photons using a limited number of iterations and filtered back projection (FBP) for image reconstruction. This results in a hybrid reconstruction algorithm that was tested with sinograms generated by Monte Carlo (MC) and analytical (AN) simulations. Results show that the ISCT algorithm is weakly dependent on the ρ{e} initial estimate. Simulation results show that the proposed algorithm reconstruct ρ{e} images with a mean error of -1% ± 3% for the AN model and from -6% to -8% for the MC model. Finally, the algorithm is capable of reconstructing qualitatively good images even in the presence of multiple scatter. The proposed algorithm would be suitable for in-vivo medical imaging as long as practical limitations can be addressed. PMID:21422588
Bastarrika, Gorka; Arraiza, Maria; Pueyo, Jesus C. [Clinica Universitaria, Universidad de Navarra, Department of Radiology, Pamplona (Spain); Cecco, Carlo N. de [Universita' di Roma ' ' Sapienza' ' -Ospedale Sant' Andrea, Department of Radiology, Rome (Italy); Ubilla, Matias; Mastrobuoni, Stefano; Rabago, Gregorio [Clinica Universitaria, Universidad de Navarra, Department of Cardiovascular Surgery, Pamplona (Spain)
2008-09-15
The image quality and optimal reconstruction interval for coronary arteries in heart transplant recipients undergoing non-invasive dual-source computed tomography (DSCT) coronary angiography was evaluated. Twenty consecutive heart transplant recipients who underwent DSCT coronary angiography were included (19 male, one female; mean age 63.1{+-}10.7 years). Data sets were reconstructed in 5% steps from 30% to 80% of the R-R interval. Two blinded independent observers assessed the image quality of each coronary segments using a five-point scale (from 0 = not evaluative to 4=excellent quality). A total of 289 coronary segments in 20 heart transplant recipients were evaluated. Mean heart rate during the scan was 89.1{+-}10.4 bpm. At the best reconstruction interval, diagnostic image quality (score {>=}2) was obtained in 93.4% of the coronary segments (270/289) with a mean image quality score of 3.04{+-} 0.63. Systolic reconstruction intervals provided better image quality scores than diastolic reconstruction intervals (overall mean quality scores obtained with the systolic and diastolic reconstructions 3.03{+-}1.06 and 2.73{+-}1.11, respectively; P<0.001). Different systolic reconstruction intervals (35%, 40%, 45% of RR interval) did not yield to significant differences in image quality scores for the coronary segments (P=0.74). Reconstructions obtained at the systolic phase of the cardiac cycle allowed excellent diagnostic image quality coronary angiograms in heart transplant recipients undergoing DSCT coronary angiography. (orig.)