Compressed Sensing For Magnetic Resonance Image Reconstruction

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Compressed Sensing for Magnetic Resonance Image Reconstruction

Author : Angshul Majumdar
Publisher : Cambridge University Press
ISBN 13 : 1316673928
Total Pages : 228 pages
Book Rating : 4.3/5 (166 download)

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Book Synopsis Compressed Sensing for Magnetic Resonance Image Reconstruction by : Angshul Majumdar

Download or read book Compressed Sensing for Magnetic Resonance Image Reconstruction written by Angshul Majumdar and published by Cambridge University Press. This book was released on 2015-02-26 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expecting the reader to have some basic training in liner algebra and optimization, the book begins with a general discussion on CS techniques and algorithms. It moves on to discussing single channel static MRI, the most common modality in clinical studies. It then takes up multi-channel MRI and the interesting challenges consequently thrown up in signal reconstruction. Off-line and on-line techniques in dynamic MRI reconstruction are visited. Towards the end the book broadens the subject by discussing how CS is being applied to other areas of biomedical signal processing like X-ray, CT and EEG acquisition. The emphasis throughout is on qualitative understanding of the subject rather than on quantitative aspects of mathematical forms. The book is intended for MRI engineers interested in the brass tacks of image formation; medical physicists interested in advanced techniques in image reconstruction; and mathematicians or signal processing engineers.

Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

Author : Bhabesh Deka
Publisher : Springer
ISBN 13 : 9811335974
Total Pages : 122 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms by : Bhabesh Deka

Download or read book Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms written by Bhabesh Deka and published by Springer. This book was released on 2018-12-29 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Mehmet Akcakaya
Publisher : Academic Press
ISBN 13 : 012822746X
Total Pages : 518 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Magnetic Resonance Image Reconstruction by : Mehmet Akcakaya

Download or read book Magnetic Resonance Image Reconstruction written by Mehmet Akcakaya and published by Academic Press. This book was released on 2022-11-04 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. Explains the underlying principles of MRI reconstruction, along with the latest research“/li> Gives example codes for some of the methods presented Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction

Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

Author : Sumit Datta
Publisher :
ISBN 13 : 9789811335983
Total Pages : 133 pages
Book Rating : 4.3/5 (359 download)

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Book Synopsis Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms by : Sumit Datta

Download or read book Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms written by Sumit Datta and published by . This book was released on 2019 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

MRI

Author : Angshul Majumdar
Publisher : CRC Press
ISBN 13 : 1482298899
Total Pages : 222 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis MRI by : Angshul Majumdar

Download or read book MRI written by Angshul Majumdar and published by CRC Press. This book was released on 2018-09-03 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Nano-Optics: Principles Enabling Basic Research and Applications

Author : Baldassare Di Bartolo
Publisher : Springer
ISBN 13 : 9402408509
Total Pages : 564 pages
Book Rating : 4.4/5 (24 download)

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Book Synopsis Nano-Optics: Principles Enabling Basic Research and Applications by : Baldassare Di Bartolo

Download or read book Nano-Optics: Principles Enabling Basic Research and Applications written by Baldassare Di Bartolo and published by Springer. This book was released on 2017-02-15 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of nano-optics, including basic theory, experiment and applications, particularly in nanofabrication and optical characterization. The contributions clearly demonstrate how advances in nano-optics and photonics have stimulated progress in nanoscience and -fabrication, and vice versa. Their expert authors address topics such as three-dimensional optical lithography and microscopy beyond the Abbe diffraction limit, optical diagnostics and sensing, optical data- and telecommunications, energy-efficient lighting, and efficient solar energy conversion. Nano-optics emerges as a key enabling technology of the 21st century. This work will appeal to a wide readership, from physics through chemistry, to biology and engineering. The contributions that appear in this volume were presented at a NATO Advanced Study Institute held in Erice, 4-19 July, 2015. Re Ch. 73 - Structure and Luminescence Properties of Nanofluorapatite Activated with Eu3+ Ions Synthesized by Hydrothermal Method, pp 567-569: The authors would like to acknowledge the National Science Centre (NSC) for financial support within the Project ‘Preparation and characterization of nanoapatites doped with rare earth ions and their biocomposites’ UMO-2012/05/E/ST5/03904

Advances in Electronics, Communication and Computing

Author : Akhtar Kalam
Publisher : Springer
ISBN 13 : 9811047650
Total Pages : 808 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Advances in Electronics, Communication and Computing by : Akhtar Kalam

Download or read book Advances in Electronics, Communication and Computing written by Akhtar Kalam and published by Springer. This book was released on 2017-10-27 with total page 808 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a compilation of research work in the interdisciplinary areas of electronics, communication, and computing. This book is specifically targeted at students, research scholars and academicians. The book covers the different approaches and techniques for specific applications, such as particle-swarm optimization, Otsu’s function and harmony search optimization algorithm, triple gate silicon on insulator (SOI)MOSFET, micro-Raman and Fourier Transform Infrared Spectroscopy (FTIR) analysis, high-k dielectric gate oxide, spectrum sensing in cognitive radio, microstrip antenna, Ground-penetrating radar (GPR) with conducting surfaces, and digital image forgery detection. The contents of the book will be useful to academic and professional researchers alike.

Compressed Sensing for Magnetic Resonance Image Reconstruction

Author : Angshul Majumdar
Publisher : Cambridge University Press
ISBN 13 : 1107103762
Total Pages : 227 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis Compressed Sensing for Magnetic Resonance Image Reconstruction by : Angshul Majumdar

Regularized Image Reconstruction in Parallel MRI with MATLAB

Author : Joseph Suresh Paul
Publisher : CRC Press
ISBN 13 : 1351029258
Total Pages : 306 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Regularized Image Reconstruction in Parallel MRI with MATLAB by : Joseph Suresh Paul

Download or read book Regularized Image Reconstruction in Parallel MRI with MATLAB written by Joseph Suresh Paul and published by CRC Press. This book was released on 2019-11-05 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning for Medical Image Reconstruction

Author : Farah Deeba
Publisher : Springer Nature
ISBN 13 : 3030615987
Total Pages : 170 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Machine Learning for Medical Image Reconstruction by : Farah Deeba

Download or read book Machine Learning for Medical Image Reconstruction written by Farah Deeba and published by Springer Nature. This book was released on 2020-10-21 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Accelerating Cardiac MRI Compressed Sensing Image Reconstruction Using Graphics Processing Units

Author : Majid Sabbagh
Publisher :
ISBN 13 :
Total Pages : 32 pages
Book Rating : 4.:/5 (99 download)

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Book Synopsis Accelerating Cardiac MRI Compressed Sensing Image Reconstruction Using Graphics Processing Units by : Majid Sabbagh

Download or read book Accelerating Cardiac MRI Compressed Sensing Image Reconstruction Using Graphics Processing Units written by Majid Sabbagh and published by . This book was released on 2016 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cardiac magnetic resonance imaging (MRI) has become a crucial part of monitoring patients with congenital heart diseases. An important limitation of cardiac MRI using the prominent 3D steady-state free precession (3D-SSFP) sequence is its long scan time. Compressed sensing (CS) algorithm reduces the scan time by undersampling the data but increases the image reconstruction time because a non-linear optimization problem must be iteratively solved to estimate the missing data and reconstruct the images. The growing demand for reducing the examination time in cardiac MRI led us to investigate opportunities to accelerate this non-linear optimization problem to facilitate the migration of CS into the clinical environment. Using undersampled 3D-SSFP datasets acquired from five patients, we compared the speed and output quality of CS image reconstruction algorithm using a Central Processing Unit (CPU), a CPU with OpenMP parallelization, and two different Graphics Processing Unit (GPU) platforms. Reconstruction time had a mean of 13.1 minutes with a standard deviation of 3.8 minutes for the CPU, a mean of 11.5 minutes with a standard deviation of 3.6 minutes for the CPU with OpenMP parallelization, a mean of 2.2 minutes with a standard deviation of 0.3 minutes for the CPU with OpenMP plus NVIDIA k20m GPU, and a mean of 1.7 minutes with a standard deviation of 0.3 minutes for the CPU with OpenMP plus NVIDIA k40m GPU. The quality of images reconstructed on GPU and on CPU, as assessed by image subtraction, was comparable. Furthermore, necessary steps for implementation of rapid CS image reconstruction in the clinical environment are discussed.

Compressed Sensing for MRI

Author : Mariya Doneva
Publisher : Sudwestdeutscher Verlag Fur Hochschulschriften AG
ISBN 13 : 9783838111018
Total Pages : 132 pages
Book Rating : 4.1/5 (11 download)

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Book Synopsis Compressed Sensing for MRI by : Mariya Doneva

Download or read book Compressed Sensing for MRI written by Mariya Doneva and published by Sudwestdeutscher Verlag Fur Hochschulschriften AG. This book was released on 2011 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work explores and extends the concept of applying compressed sensing to MRI. Asuccessful CS reconstruction requires incoherent measurements,signal sparsity, and a nonlinearsparsity promoting reconstruction. To optimize the performance of CS, the acquisition, thesparsifying transform and the reconstruction have to be adapted to the application of interest.This work presents new approaches for sampling, signal sparsity and reconstruction, which areapplied to three important applications: dynamic MR imaging, MR parameter mapping andchemical-shift based water-fat separation.The methods presented in this work allow to more fully exploit the potential of compressedsensing to improve imaging speed. Future development of these methods, and combination withexisting techniques for fast imaging, holds the potential to improve the diagnostic quality ofexisting clinical MR imaging techniques and to open up opportunities for entirely new clinicalapplications of MRI.

Quantitative Magnetic Resonance Imaging

Author : Nicole Seiberlich
Publisher : Academic Press
ISBN 13 : 0128170581
Total Pages : 1094 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Quantitative Magnetic Resonance Imaging by : Nicole Seiberlich

Download or read book Quantitative Magnetic Resonance Imaging written by Nicole Seiberlich and published by Academic Press. This book was released on 2020-11-18 with total page 1094 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Reconstruction-Free Compressive Vision for Surveillance Applications

Author : Henry Braun
Publisher : Springer Nature
ISBN 13 : 3031025415
Total Pages : 86 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Reconstruction-Free Compressive Vision for Surveillance Applications by : Henry Braun

Download or read book Reconstruction-Free Compressive Vision for Surveillance Applications written by Henry Braun and published by Springer Nature. This book was released on 2022-05-31 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.

Handbook of Mathematical Methods in Imaging

Author : Otmar Scherzer
Publisher : Springer Science & Business Media
ISBN 13 : 0387929193
Total Pages : 1626 pages
Book Rating : 4.3/5 (879 download)

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Book Synopsis Handbook of Mathematical Methods in Imaging by : Otmar Scherzer

Download or read book Handbook of Mathematical Methods in Imaging written by Otmar Scherzer and published by Springer Science & Business Media. This book was released on 2010-11-23 with total page 1626 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.

Regularized Image Reconstruction in Parallel MRI with MATLAB

Author : Joseph Suresh Paul
Publisher : CRC Press
ISBN 13 : 135102924X
Total Pages : 271 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Regularized Image Reconstruction in Parallel MRI with MATLAB by : Joseph Suresh Paul

Download or read book Regularized Image Reconstruction in Parallel MRI with MATLAB written by Joseph Suresh Paul and published by CRC Press. This book was released on 2019-11-05 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning for Medical Image Reconstruction

Author : Nandinee Haq
Publisher : Springer Nature
ISBN 13 : 3030885526
Total Pages : 142 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Machine Learning for Medical Image Reconstruction by : Nandinee Haq

Download or read book Machine Learning for Medical Image Reconstruction written by Nandinee Haq and published by Springer Nature. This book was released on 2021-09-29 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.