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Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms Book
Author : Bhabesh Deka,Sumit Datta
Publisher : Springer
Release : 2018-12-29
ISBN : 9811335974
Language : En, Es, Fr & De

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Book Description :

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.

Magnetic Resonance Image Reconstruction

Magnetic Resonance Image Reconstruction Book
Author : Mariya Ivanova Doneva,Mehmet Akcakaya,Claudia Prieto
Publisher : Academic Press
Release : 2021-11-15
ISBN : 9780128227268
Language : En, Es, Fr & De

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Book Description :

Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. It discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. Magnetic Resonance Image Reconstruction: Theory, Methods and Applications is a unique resource suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. Explains the underlying principles of MRI reconstruction as well as the latest research Gives example code of some of the methods presented Includes the latest developments such as compressed sensing, tensor-based reconstruction and machine learning based reconstruction

Magnetic Resonance Imaging with Nonlinear Gradient Fields

Magnetic Resonance Imaging with Nonlinear Gradient Fields Book
Author : Gerrit Schultz
Publisher : Springer Science & Business Media
Release : 2013-04-04
ISBN : 3658011343
Language : En, Es, Fr & De

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Book Description :

​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.

MRI

MRI Book
Author : Angshul Majumdar,Rabab Kreidieh Ward
Publisher : CRC Press
Release : 2018-09-03
ISBN : 1482298899
Language : En, Es, Fr & De

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Book Description :

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.

Regularized Image Reconstruction in Parallel MRI with MATLAB

Regularized Image Reconstruction in Parallel MRI with MATLAB Book
Author : Joseph Suresh Paul,Raji Susan Mathew
Publisher : CRC Press
Release : 2019-11-11
ISBN : 135102924X
Language : En, Es, Fr & De

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Book Description :

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.

Principles of Magnetic Resonance Imaging

Principles of Magnetic Resonance Imaging Book
Author : Zhi-Pei Liang,Paul C. Lauterbur
Publisher : Wiley-IEEE Press
Release : 1999-11-01
ISBN : 9780780347236
Language : En, Es, Fr & De

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Book Description :

In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, "father of the MRI", and Dr. Zhi-Pei Liang have co-authored the first engineering textbook on magnetic resonance imaging. This long-awaited, definitive text will help undergraduate and graduate students of biomedical engineering, biomedical imaging scientists, radiologists, and electrical engineers gain an in-depth understanding of MRI principles. The authors use a signal processing approach to describe the fundamentals of magnetic resonance imaging. You will find a clear and rigorous discussion of these carefully selected essential topics: Mathematical fundamentals Signal generation and detection principles Signal characteristics Signal localization principles Image reconstruction techniques Image contrast mechanisms Image resolution, noise, and artifacts Fast-scan imaging Constrained reconstruction Complete with a comprehensive set of examples and homework problems, Principles of Magnetic Resonance Imaging is the must-read book to improve your knowledge of this revolutionary technique.

Magnetic Resonance Imaging

Magnetic Resonance Imaging Book
Author : Robert W. Brown,E. Mark Haacke,Y.-C. Norman Cheng,Michael R. Thompson,Ramesh Venkatesan
Publisher : John Wiley & Sons
Release : 2014-06-23
ISBN : 0471720852
Language : En, Es, Fr & De

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Book Description :

Preceded by Magnetic resonance imaging: physical principles and sequence design / E. Mark Haacke ... [et al.]. c1999.

Medical Image Reconstruction

Medical Image Reconstruction Book
Author : Gengsheng Zeng
Publisher : Springer Science & Business Media
Release : 2010-12-28
ISBN : 3642053688
Language : En, Es, Fr & De

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Book Description :

"Medical Image Reconstruction: A Conceptual Tutorial" introduces the classical and modern image reconstruction technologies, such as two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. This book presents both analytical and iterative methods of these technologies and their applications in X-ray CT (computed tomography), SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging). Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich's cone-beam filtered backprojection algorithm, and reconstruction with highly undersampled data with l0-minimization are also included. This book is written for engineers and researchers in the field of biomedical engineering specializing in medical imaging and image processing with image reconstruction. Gengsheng Lawrence Zeng is an expert in the development of medical image reconstruction algorithms and is a professor at the Department of Radiology, University of Utah, Salt Lake City, Utah, USA.

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction Book
Author : Farah Deeba,Patricia Johnson,Tobias Würfl,Jong Chul Ye
Publisher : Springer
Release : 2020-10-20
ISBN : 9783030615970
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms Book
Author : Sumit Datta
Publisher : Unknown
Release : 2019
ISBN : 9789811335983
Language : En, Es, Fr & De

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Book Description :

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.

Improved Image Reconstruction Methods for Parallel Magnetic Resonance Imaging

Improved Image Reconstruction Methods for Parallel Magnetic Resonance Imaging Book
Author : Kaiyu Zheng
Publisher : Unknown
Release : 2010
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Improved Image Reconstruction Methods for Parallel Magnetic Resonance Imaging book written by Kaiyu Zheng, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Fundamentals of Magnetic Resonance Imaging

Fundamentals of Magnetic Resonance Imaging Book
Author : Jintong Mao
Publisher : Independently Published
Release : 2019-10-25
ISBN : 9781701655348
Language : En, Es, Fr & De

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Book Description :

This book is in black and white printing. It was revised on 05/30/2020. Starting from complex free induction decay (FID), this book establishes a logical framework for the discussion of the principles of MRI. Based on the framework, traditional topics and some new topics are described in detail. Every formula is derived step by step at length. Essence of MRI is thoroughly discussed. It is emphasized that Fourier transform (FT) in MRI is a natural result from data acquisition if with a linear field gradient. Each concept, particularly the concept of echo, is explained in detail. For example, it is indicated that the popular drawing of an echo following a single FID (note this "single") in time axis is misleading in MRI (but may not in NMR). An echo cannot be considered as two back to back FID, etc. If you cannot accept these statements immediately, you may need to refresh your basic knowledge of MRI. The procedure from FID to MR image is accomplished by a pair of FT. The first FT is established naturally and automatically from echo acquisition. Analog digital converter leads to discrete FID. Using Nyquist sampling and quadrature phase sensitive detection (PSD), formula FOV*dk = 2pi is derived. From FOV*dk=2pi, discrete FT is derived by the summation of discrete FID directly, without relying on continuous FT. Thus, discrete FID leads to discrete FT. On other side, a discrete echo is the summation of acquired discrete FID, if re-phasing linear gradient field follows de-phasing gradient field. Thus, discrete FID also leads to discrete echo. We have the result that the discrete echo is a discrete FT (one dimensional). A series of echoes is obtained by phase encoding (raw data in two-dimensional k-space). The k-space, therefore, is a two-dimensional discrete FT (first FT). The reconstructed image is obtained by applying inverse FT (second FT) to the series of discrete echoes (k-space). Continuous FT is used as a heuristic step. But it is not necessary for the discussion of MRI. As example from FID to MR image, simulated images are obtained for graphical phantoms by using MATLAB. In appendix, MATLAB codes for image reconstruction and for some frequency selective pulses are included. Based on the framework, the topics include basic pulse sequences; pulse train; image contrasts; signal to noise ratio; ringing artifacts; aliasing artifacts; improvement of slice profile of selective pulses (Bloch equation is solved numerically using Runge-Kutta method); fat suppression; magnetization transfer; diffusion; flow image; functional MRI (fMRI for a perceptual alternation is presented), etc. Inside of the framework, emphasized topics include pulsatile ghost artifact for flow that is simulated by MATLAB and explained by interleaved zero data in k-space; experiments show that traditional explanation of flow mis-registration is not correct; the experiment also shows that the profile of laminar flow looks like a long needle, instead of ellipsoid; Stejskal-Tanner formula for b-value can be obtained by a wrong derivation, thus, the correctness of the formula may be in question; the strength of refocusing gradient for 90d selective pulse is-0.515, instead of commonly used -0.5 (small difference in refocusing strength leads to a large difference in refocusing effects due to non-linearity of Bloch equation); etc. In addition to above topics, Bloch equation with the terms T1, T2, diffusion, flow, etc. is derived by adding independent contributions to dM/dt with the assumption that T2 functions only in x-y plane. It is the hope this book is readable. It is the hope that the journey through the book might be a joy. This book will be of value to beginners. Perhaps it is valuable to a more extensive readership as well.

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction Book
Author : Nandinee Haq,Patricia Johnson,Andreas Maier,Tobias Würfl,Jaejun Yoo
Publisher : Springer Nature
Release : 2021-09-29
ISBN : 3030885526
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Synthetic Magnetic Resonance Image Reconstruction Based on Parametric Weighted Images for Contrast Ehnacement at High Field MRI

Synthetic Magnetic Resonance Image Reconstruction Based on Parametric Weighted Images for Contrast Ehnacement at High Field MRI Book
Author : David Milford
Publisher : Unknown
Release : 2013
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Synthetic Magnetic Resonance Image Reconstruction Based on Parametric Weighted Images for Contrast Ehnacement at High Field MRI book written by David Milford, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Regularized Image Reconstruction in Parallel MRI with MATLAB

Regularized Image Reconstruction in Parallel MRI with MATLAB Book
Author : Joseph Suresh Paul,Raji Susan Mathew
Publisher : CRC Press
Release : 2019
ISBN : 9781351029261
Language : En, Es, Fr & De

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Book Description :

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.

Advanced Image Processing in Magnetic Resonance Imaging

Advanced Image Processing in Magnetic Resonance Imaging Book
Author : Luigi Landini,Vincenzo Positano,Maria Santarelli
Publisher : CRC Press
Release : 2018-10-03
ISBN : 1420028669
Language : En, Es, Fr & De

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Book Description :

The popularity of magnetic resonance (MR) imaging in medicine is no mystery: it is non-invasive, it produces high quality structural and functional image data, and it is very versatile and flexible. Research into MR technology is advancing at a blistering pace, and modern engineers must keep up with the latest developments. This is only possible with a firm grounding in the basic principles of MR, and Advanced Image Processing in Magnetic Resonance Imaging solidly integrates this foundational knowledge with the latest advances in the field. Beginning with the basics of signal and image generation and reconstruction, the book covers in detail the signal processing techniques and algorithms, filtering techniques for MR images, quantitative analysis including image registration and integration of EEG and MEG techniques with MR, and MR spectroscopy techniques. The final section of the book explores functional MRI (fMRI) in detail, discussing fundamentals and advanced exploratory data analysis, Bayesian inference, and nonlinear analysis. Many of the results presented in the book are derived from the contributors' own work, imparting highly practical experience through experimental and numerical methods. Contributed by international experts at the forefront of the field, Advanced Image Processing in Magnetic Resonance Imaging is an indispensable guide for anyone interested in further advancing the technology and capabilities of MR imaging.

Image Reconstruction

Image Reconstruction Book
Author : Gengsheng Lawrence Zeng
Publisher : Walter de Gruyter GmbH & Co KG
Release : 2017-03-20
ISBN : 3110500590
Language : En, Es, Fr & De

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Book Description :

This book introduces the classical and modern image reconstruction technologies. It covers topics in two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. Both analytical and iterative methods are presented. The applications in X-ray CT, SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging) are discussed. Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich’s cone-beam filtered backprojection algorithm, and reconstruction with highly under-sampled data are included. The last chapter of the book is devoted to the techniques of using a fast analytical algorithm to reconstruct an image that is equivalent to an iterative reconstruction. These techniques are the author’s most recent research results. This book is intended for students, engineers, and researchers who are interested in medical image reconstruction. Written in a non-mathematical way, this book provides an easy access to modern mathematical methods in medical imaging. Table of Content: Chapter 1 Basic Principles of Tomography 1.1 Tomography 1.2 Projection 1.3 Image Reconstruction 1.4 Backprojection 1.5 Mathematical Expressions Problems References Chapter 2 Parallel-Beam Image Reconstruction 2.1 Fourier Transform 2.2 Central Slice Theorem 2.3 Reconstruction Algorithms 2.4 A Computer Simulation 2.5 ROI Reconstruction with Truncated Projections 2.6 Mathematical Expressions (The Fourier Transform and Convolution , The Hilbert Transform and the Finite Hilbert Transform , Proof of the Central Slice Theorem, Derivation of the Filtered Backprojection Algorithm , Expression of the Convolution Backprojection Algorithm, Expression of the Radon Inversion Formula ,Derivation of the Backprojection-then-Filtering Algorithm Problems References Chapter 3 Fan-Beam Image Reconstruction 3.1 Fan-Beam Geometry and Point Spread Function 3.2 Parallel-Beam to Fan-Beam Algorithm Conversion 3.3 Short Scan 3.4 Mathematical Expressions (Derivation of a Filtered Backprojection Fan-Beam Algorithm, A Fan-Beam Algorithm Using the Derivative and the Hilbert Transform) Problems References Chapter 4 Transmission and Emission Tomography 4.1 X-Ray Computed Tomography 4.2 Positron Emission Tomography and Single Photon Emission Computed Tomography 4.3 Attenuation Correction for Emission Tomography 4.4 Mathematical Expressions Problems References Chapter 5 3D Image Reconstruction 5.1 Parallel Line-Integral Data 5.2 Parallel Plane-Integral Data 5.3 Cone-Beam Data (Feldkamp's Algorithm, Grangeat's Algorithm, Katsevich's Algorithm) 5.4 Mathematical Expressions (Backprojection-then-Filtering for Parallel Line-Integral Data, Filtered Backprojection Algorithm for Parallel Line-Integral Data, 3D Radon Inversion Formula, 3D Backprojection-then-Filtering Algorithm for Radon Data, Feldkamp's Algorithm, Tuy's Relationship, Grangeat's Relationship, Katsevich’s Algorithm) Problems References Chapter 6 Iterative Reconstruction 6.1 Solving a System of Linear Equations 6.2 Algebraic Reconstruction Technique 6.3 Gradient Descent Algorithms 6.4 Maximum-Likelihood Expectation-Maximization Algorithms 6.5 Ordered-Subset Expectation-Maximization Algorithm 6.6 Noise Handling (Analytical Methods, Iterative Methods, Iterative Methods) 6.7 Noise Modeling as a Likelihood Function 6.8 Including Prior Knowledge 6.9 Mathematical Expressions (ART, Conjugate Gradient Algorithm, ML-EM, OS-EM, Green’s One-Step Late Algorithm, Matched and Unmatched Projector/Backprojector Pairs ) 6.10 Reconstruction Using Highly Undersampled Data with l0 Minimization Problems References Chapter 7 MRI Reconstruction 7.1 The 'M' 7.2 The 'R' 7.3 The 'I'; (To Obtain z-Information, x-Information, y-Information) 7.4 Mathematical Expressions Problems References Indexing

Quantitative Magnetic Resonance Imaging

Quantitative Magnetic Resonance Imaging Book
Author : Nicole Seiberlich,Vikas Gulani,Adrienne Campbell-Washburn,Steven Sourbron,Mariya Ivanova Doneva,Fernando Calamante,Houchun Harry Hu
Publisher : Academic Press
Release : 2020-11-27
ISBN : 0128170581
Language : En, Es, Fr & De

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Book Description :

Quantitative Magnetic Resonance Imaging is a ‘go-to’ reference for methods and applications of quantitative magnetic resonance imaging, with specific sections on Relaxometry, Perfusion, and Diffusion. Each section will start with an explanation of the basic techniques for mapping the tissue property in question, including a description of the challenges that arise when using these basic approaches. For properties which can be measured in multiple ways, each of these basic methods will be described in separate chapters. Following the basics, a chapter in each section presents more advanced and recently proposed techniques for quantitative tissue property mapping, with a concluding chapter on clinical applications. The reader will learn: The basic physics behind tissue property mapping How to implement basic pulse sequences for the quantitative measurement of tissue properties The strengths and limitations to the basic and more rapid methods for mapping the magnetic relaxation properties T1, T2, and T2* The pros and cons for different approaches to mapping perfusion The methods of Diffusion-weighted imaging and how this approach can be used to generate diffusion tensor maps and more complex representations of diffusion How flow, magneto-electric tissue property, fat fraction, exchange, elastography, and temperature mapping are performed How fast imaging approaches including parallel imaging, compressed sensing, and Magnetic Resonance Fingerprinting can be used to accelerate or improve tissue property mapping schemes How tissue property mapping is used clinically in different organs Structured to cater for MRI researchers and graduate students with a wide variety of backgrounds Explains basic methods for quantitatively measuring tissue properties with MRI - including T1, T2, perfusion, diffusion, fat and iron fraction, elastography, flow, susceptibility - enabling the implementation of pulse sequences to perform measurements Shows the limitations of the techniques and explains the challenges to the clinical adoption of these traditional methods, presenting the latest research in rapid quantitative imaging which has the possibility to tackle these challenges Each section contains a chapter explaining the basics of novel ideas for quantitative mapping, such as compressed sensing and Magnetic Resonance Fingerprinting-based approaches

Machine Learning for Medical Image Reconstruction

Machine Learning for Medical Image Reconstruction Book
Author : Florian Knoll,Andreas Maier,Daniel Rueckert,Jong Chul Ye
Publisher : Springer
Release : 2019-10-24
ISBN : 9783030338428
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.