The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Dimitris N Metaxas - One of the best experts on this subject based on the ideXlab platform.
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efficient mr Image Reconstruction for compressed mr imaging
Medical Image Analysis, 2011Co-Authors: Junzhou Huang, Shaoting Zhang, Dimitris N MetaxasAbstract:Abstract 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.
Jinyi Qi - One of the best experts on this subject based on the ideXlab platform.
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PET Image Reconstruction Using Kernel Method
IEEE Transactions on Medical Imaging, 2015Co-Authors: Guobao Wang, Jinyi QiAbstract:Image Reconstruction from low-count positron emission tomography (PET) projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve Image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET Image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based Image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood Image Reconstruction. A kernelized expectation-maximization algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET Image Reconstruction than the conventional maximum likelihood method with and without post-Reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better Image quality for low-count data. Application of the proposed kernel method to a 4-D dynamic PET patient dataset showed promising results.
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PET Image Reconstruction using kernel method
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Guobao Wang, Jinyi QiAbstract:Image Reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Inspired by the kernel methods for machine learning, this paper proposes a kernel based method that models PET Image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based Image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by maximum likelihood or penalized likelihood Image Reconstruction. Computer simulation shows that the proposed approach can achieve a higher signal-to-noise ratio for dynamic PET Image Reconstruction than the conventional maximum likelihood method with and without post-Reconstruction denoising.
Andrew J Reader - One of the best experts on this subject based on the ideXlab platform.
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Advances in PET Image Reconstruction
Pet Clinics, 2020Co-Authors: Andrew J Reader, Habib ZaidiAbstract:Until recently, the most widely used methods for Image Reconstruction were direct analytic techniques. Iterative techniques, although computationally much more intensive, produce improved Images (principally arising from more accurate modeling of the acquired projection data), enabling these techniques to replace analytic techniques not only in research settings but also in the clinic. This article offers an overview of Image Reconstruction theory and algorithms for PET, with a particular emphasis on statistical iterative Reconstruction techniques. Future directions for Image Reconstruction in PET are considered, which concern mainly improving the modeling of the data acquisition process and task-specific specification of the parameters to be estimated in Image Reconstruction.
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4D Image Reconstruction for emission tomography
Physics in Medicine and Biology, 2014Co-Authors: Andrew J Reader, Jeroen VerhaegheAbstract: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 approaches.
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The promise of new PET Image Reconstruction
Physica Medica, 2008Co-Authors: Andrew J ReaderAbstract:Abstract Image Reconstruction in positron emission tomography (PET) is conventionally regarded as the algorithm applied to the acquired data to produce Images used for estimation of physiological parameters, or to determine the presence of disease. There are numerous approaches to Image Reconstruction, and the method chosen has a significant impact on the utility of PET. The use of iterative Image Reconstruction algorithms and the use of resolution modelling (“resolution recovery”) are two specific advances from recent years which have demonstrated marked improvements in Image quality. This paper considers three main aspects of PET Image Reconstruction in which there are still promising possibilities for further advance: (i) full consideration of the raw acquired PET data (with minimal pre-processing), (ii) careful selection of the parameters to estimate and (iii) accurate definition of the system matrix, which maps the parameters to the measurement space. Specific examples for these three areas include (i) the full use of timing, position and energy information, (ii) selecting physiological parameters as the unknowns to estimate and (iii) using Monte Carlo simulation to model the PET scanner and patient, in conjunction with time-dependent MRI or CT anatomical information to render the model more accurate. Present computational constraints mean that limited but practical methods have to be used, which to some extent compromise the full capabilities of Image Reconstruction in all of the aforementioned areas of promise. Nonetheless, this paper comments on possible ways forward without being overly concerned about the current limitations.
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Penalised least squares Image Reconstruction from backprojection space for 3D PET
Nuclear Science, 2000Co-Authors: Andrew J ReaderAbstract:... Penalised Least Squares Image Reconstruction Backprojection Space 3D PET ... Image Reconstruction 3D PET 3D ...
Lihui Peng - One of the best experts on this subject based on the ideXlab platform.
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An Image Reconstruction method for improving resolution of capacitive wire mesh tomography
2017 IEEE International Conference on Imaging Systems and Techniques (IST), 2017Co-Authors: Lihui Peng, Yi LiAbstract:As an instantaneous tomography method, wire-mesh tomography has advantage in speed but has less Image resolution because classic wire-mesh tomography Image Reconstruction methods only provide same amount of pixels as measurement number. In order to increase Image resolution, a new Image Reconstruction method based on sensitivity map is proposed, which is of providing more pixels (sub pixels) by solving inverse problem with capacitive wire-mesh tomography Image Reconstruction. The Image Reconstruction algorithms, including the traditional wire-mesh direct Image Reconstruction algorithm, the linear back projection, the projected Landweber iteration, and the total variation based iteration, are conducted and the results are compared each other. Experimental results show that the proposed sensitivity map based Image Reconstruction method can provide better resolution and helpful to improve the Reconstruction quality of capacitive wire-mesh tomography.
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Image Reconstruction algorithms for electrical capacitance tomography
Measurement Science and Technology, 2003Co-Authors: Wuqiang Yang, Lihui PengAbstract:Electrical capacitance tomography (ECT) is used to Image cross-sections of industrial processes containing dielectric material. This technique has been under development for more than a decade. The task of Image Reconstruction for ECT is to determine the permittivity distribution and hence material distribution over the cross-section from capacitance measurements. There are three principal difficulties with Image Reconstruction for ECT: (1) the relationship between the permittivity distribution and capacitance is non-linear and the electric field is distorted by the material present, the so-called 'soft-field' effect; (2) the number of independent measurements is limited, leading to an under-determined problem and (3) the inverse problem is ill posed and ill conditioned, making the solution sensitive to measurement errors and noise. Regularization methods are needed to treat this ill-posedness. This paper reviews existing Image Reconstruction algorithms for ECT, including linear back-projection, singular value decomposition, Tikhonov regularization, Newton–Raphson, iterative Tikhonov, the steepest descent method, Landweber iteration, the conjugate gradient method, algebraic Reconstruction techniques, simultaneous iterative Reconstruction techniques and model-based Reconstruction. Some of these algorithms are examined by simulation and experiment for typical permittivity distributions. Future developments in Image Reconstruction for ECT are discussed.
Junzhou Huang - One of the best experts on this subject based on the ideXlab platform.
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efficient mr Image Reconstruction for compressed mr imaging
Medical Image Analysis, 2011Co-Authors: Junzhou Huang, Shaoting Zhang, Dimitris N MetaxasAbstract:Abstract 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.
