Magnetic Resonance Image Reconstruction Books

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Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms


Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms
  • Author : Bhabesh Deka
  • Publisher : Springer
  • Release : 2018-12-29
  • ISBN : 9789811335976
  • Language : En, Es, Fr & De
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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.

Compressed Sensing for Magnetic Resonance Image Reconstruction


Compressed Sensing for Magnetic Resonance Image Reconstruction
  • Author : Angshul Majumdar
  • Publisher : Cambridge University Press
  • Release : 2015-02-26
  • ISBN : 9781107103764
  • Language : En, Es, Fr & De
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"Discusses different ways to use existing mathematical techniques to solve compressed sensing problems"--Provided by publisher.

MRI


MRI
  • Author : Angshul Majumdar
  • Publisher : CRC Press
  • Release : 2018-09-03
  • ISBN : 9781482298895
  • Language : En, Es, Fr & De
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The field of magnetic resonance imaging (MRI) has developed rapidly over the past decade, benefiting greatly from the newly developed framework of compressed sensing and its ability to drastically reduce MRI scan times. MRI: Physics, Image Reconstruction, and Analysis presents the latest research in MRI technology, emphasizing compressed sensing-based image reconstruction techniques. The book begins with a succinct introduction to the principles of MRI and then: Discusses the technology and applications of T1rho MRI Details the recovery of highly sampled functional MRIs Explains sparsity-based techniques for quantitative MRIs Describes multi-coil parallel MRI reconstruction techniques Examines off-line techniques in dynamic MRI reconstruction Explores advances in brain connectivity analysis using diffusion and functional MRIs Featuring chapters authored by field experts, MRI: Physics, Image Reconstruction, and Analysis delivers an authoritative and cutting-edge treatment of MRI reconstruction techniques. The book provides engineers, physicists, and graduate students with a comprehensive look at the state of the art of MRI.

Magnetic Resonance Imaging with Nonlinear Gradient Fields


Magnetic Resonance Imaging with Nonlinear Gradient Fields
  • Author : Gerrit Schultz
  • Publisher : Springer Science & Business Media
  • Release : 2013-04-04
  • ISBN : 9783658011345
  • Language : En, Es, Fr & De
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​Within the past few decades MRI has become one of the most important imaging modalities in medicine. For a reliable diagnosis of pathologies further technological improvements are of primary importance. This study deals with a radically new approach of image encoding. Gradient linearity has ever since been an unquestioned technological design criterion. With the advent of parallel imaging, this approach may be questioned, making way of much a more flexible gradient hardware that uses encoding fields with an arbitrary geometry. The theoretical basis of this new imaging modality – PatLoc imaging – are comprehensively presented, suitable image reconstruction algorithms are developed for a variety of imaging sequences and imaging results – including in vivo data – are explored based on novel hardware designs.

Regularized Image Reconstruction in Parallel MRI with MATLAB


Regularized Image Reconstruction in Parallel MRI with MATLAB
  • Author : Joseph Suresh Paul
  • Publisher : CRC Press
  • Release : 2019-11-05
  • ISBN : 9781351029254
  • Language : En, Es, Fr & De
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Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features: Provides details for optimizing regularization parameters in each type of reconstruction. Presents comparison of regularization approaches for each type of pMRI reconstruction. Includes discussion of case studies using clinically acquired data. MATLAB codes are provided for each reconstruction type. Contains method-wise description of adapting regularization to optimize speed and accuracy. This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.