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BL
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| Record Number
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891302
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| Main Entry
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Deka, Bhabesh
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| Title & Author
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Compressed sensing magnetic resonance image reconstruction algorithms : : a convex optimization approach /\ Bhabesh Deka, Sumit Datta.
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| Publication Statement
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Singapore :: Springer,, [2019]
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| Series Statement
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Springer Series on Bio- and Neurosystems ;; v. 9
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| Page. NO
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1 online resource (133 pages)
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| ISBN
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9789811335976
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: 9789811335983
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: 9811335974
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: 9811335982
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9789811335969
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9811335966
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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Intro; Preface; Acknowledgements; Contents; About the Authors; 1 Introduction to Compressed Sensing Magnetic Resonance Imaging; 1.1 Introduction to MRI; 1.2 MRI Data Acquisition; 1.2.1 Single-Channel MRI; 1.2.2 Multichannel (or Parallel) MRI; 1.3 MR Image Contrast; 1.3.1 Relaxation Time; 1.3.2 Repetition Time; 1.3.3 Echo Time; 1.4 Types of MR Images; 1.4.1 T1-Weighted Image; 1.4.2 T2-Weighted Image; 1.4.3 PD-Weighted Image; 1.5 Compressed Sensing in MRI; 1.6 Essentials of Sparse MRI; 1.6.1 Sparsity of MR Images; 1.6.2 Mutual Coherence; 1.7 Design of CS-MRI Sampling Pattern.
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1.7.1 Variable Density Undersampling Pattern1.7.2 Undersampling Pattern for Clinical MRI; 1.8 Some Implementations of CS-MRI for Clinical Applications; 1.9 Conclusions; References; 2 CS-MRI Reconstruction Problem; 2.1 Introduction; 2.2 CS-MRI Problem Formulation; 2.3 Conclusions; References; 3 Fast Algorithms for Compressed Sensing MRI Reconstruction; 3.1 Introduction; 3.2 Operator Splitting Method; 3.2.1 Iterative Shrinkage-Thresholding Algorithm; 3.2.2 Two-Step Iterative Shrinkage-Thresholding Algorithm; 3.2.3 Sparse Reconstruction by Separable Approximation.
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3.2.4 Fast Iterative Shrinkage-Thresholding Algorithm3.2.5 Total Variation ell1 Compressed MR Imaging; 3.3 Variable Splitting Method; 3.3.1 Augmented Lagrange Multiplier Method; 3.3.2 Alternating Direction Method of Multipliers; 3.3.3 Algorithm Based on Bregman Iteration; 3.4 Composite Splitting; 3.4.1 Composite Splitting Denoising; 3.4.2 Composite Splitting Algorithm (CSA); 3.4.3 Fast Composite Splitting Algorithm (FCSA); 3.5 Non-splitting Method; 3.5.1 Nonlinear Conjugate Gradient Method; 3.5.2 Gradient Projection for Sparse Reconstruction; 3.5.3 Truncated Newton Interior-Point Method.
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3.6 ConclusionsReferences; 4 Performance Evaluation of CS-MRI Reconstruction Algorithms; 4.1 Introduction; 4.2 Simulation Setup; 4.2.1 MRI Database Selection; 4.2.2 Selection of Parameters; 4.3 Performance Evaluation; 4.4 Experiments on Convergence; 4.5 Performance Evaluation of Iteratively Weighted Algorithms; 4.6 Conclusions; References; 5 CS-MRI Benchmarks and Current Trends; 5.1 Introduction; 5.2 Compressed Sensing for Clinical MRI; 5.3 CS-MRI Reconstruction; 5.3.1 k-Space Undersampling in Practice and Sparsifying Transform; 5.3.2 Implementations; 5.4 Image Quality Assessment.
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5.5 Computational Complexity5.6 Current Trends; 5.6.1 Interpolated CS-MRI (iCS-MRI) Reconstruction; 5.6.2 Fast CS-MRI Hardware Implementation; 5.7 Future Research Directions; 5.8 Conclusions; References; 6 Applications of CS-MRI in Bioinformatics and Neuroinformatics; 6.1 Introduction; 6.2 MRI in Bioinformatics; 6.2.1 Whole-Body MRI; 6.2.2 Magnetic Resonance Spectroscopy Imaging; 6.2.3 Diffusion-Weighted MRI; 6.2.4 Magnetic Resonance Angiography for Body Imaging; 6.3 MRI in Neuroinformatics; 6.3.1 Brain MRI; 6.3.2 Functional MRI; 6.3.3 Diffusion Weighted Brain MRI.
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| Abstract
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This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI) is able to reduce the scan time of MRI considerably as it is possible to reconstruct MR images from only a few measurements in the k-space; far below the requirements of the Nyquist sampling rate. L1-norm-based regularization problems can be solved efficiently using the state-of-the-art convex optimization techniques, which in general outperform the greedy techniques in terms of quality of reconstructions. Recently, fast convex optimization based reconstruction algorithms have been developed which are also able to achieve the benchmarks for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of medical image processing, particularly in MRI to understand the need for the CS in MRI, and thereby how it could revolutionize the soft tissue imaging to benefit healthcare technology without making major changes in the existing scanner hardware. It would be particularly useful for researchers who have just entered into the exciting field of CS-MRI and would like to quickly go through the developments to date without diving into the detailed mathematical analysis. Finally, it also discusses recent trends and future research directions for implementation of CS-MRI in clinical practice, particularly in Bio- and Neuro-informatics applications.
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| Subject
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Compressed sensing (Telecommunication)
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Magnetic resonance imaging.
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Compressed sensing (Telecommunication)
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| Subject
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Magnetic resonance imaging.
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| Dewey Classification
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616.07/548
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| LC Classification
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QC762.6.M34
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| Added Entry
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Datta, Sumit
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