Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms
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Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms
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Author : Bhabesh Deka
language : en
Publisher: Springer
Release Date : 2018-12-29
Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms written by Bhabesh Deka and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-29 with Technology & Engineering categories.
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
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Author : Angshul Majumdar
language : en
Publisher: Cambridge University Press
Release Date : 2015-02-26
Compressed Sensing For Magnetic Resonance Image Reconstruction written by Angshul Majumdar and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-26 with Computers categories.
"Discusses different ways to use existing mathematical techniques to solve compressed sensing problems"--Provided by publisher.
Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms
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Author : Sumit Datta
language : en
Publisher:
Release Date : 2019
Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms written by Sumit Datta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Compressed sensing (Telecommunication) categories.
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
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Author : Mehmet Akcakaya
language : en
Publisher: Academic Press
Release Date : 2022-11-04
Magnetic Resonance Image Reconstruction written by Mehmet Akcakaya and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-04 with Science categories.
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 - 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 For Engineers
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Author : Angshul Majumdar
language : en
Publisher: CRC Press
Release Date : 2018-12-07
Compressed Sensing For Engineers written by Angshul Majumdar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-07 with Technology & Engineering categories.
Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations. The topic is of immense practical significance since all naturally occurring signals can be sparsely represented in some domain. In recent years, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) and has also helped reduce the health hazard in X-Ray Computed CT. This book is a valuable resource suitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra. Covers fundamental concepts of compressed sensing Makes subject matter accessible for engineers of various levels Focuses on algorithms including group-sparsity and row-sparsity, as well as applications to computational imaging, medical imaging, biomedical signal processing, and machine learning Includes MATLAB examples for further development
Ai And Data Science Applications During Covid 19
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Author : Gopee Mukhopadhyay
language : en
Publisher: Educohack Press
Release Date : 2025-01-03
Ai And Data Science Applications During Covid 19 written by Gopee Mukhopadhyay and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
The illustrations in this book are created by “Team Educohack”. AI and Data Science Applications During COVID-19 explores cutting-edge research on how artificial intelligence (AI) and data science have been used to detect, mitigate, treat, and eliminate COVID-19. This comprehensive guide introduces AI and data science techniques used in COVID-19 research, considering both past and future pandemics, including related Coronavirus variations. We cover a wide range of AI applications in COVID-19 research, such as robotics, machine learning, neural networks, computer vision, expert systems, speech recognition, evolutionary computation, and natural language processing. The book also delves into data science applications, including image analysis, data processing, data privacy, deep learning, medical image processing, data protection, cybersecurity, sorting applications, COVID-19 diagnosis, geoprocessing and tracking, predictive systems, design cognition, mobile technology, and telemedicine solutions. Additionally, we discuss AI-based solutions, innovative treatment methods, and public safety measures. Finally, readers will learn about the applications of Big Data and new data models for mitigating the effects of pandemics.
Regularized Image Reconstruction In Parallel Mri With Matlab
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Author : Joseph Suresh Paul
language : en
Publisher: CRC Press
Release Date : 2019-11-05
Regularized Image Reconstruction In Parallel Mri With Matlab written by Joseph Suresh Paul and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-05 with Medical categories.
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.
Medical Image Computing And Computer Assisted Intervention Miccai 2010
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Author : Tianzi Jiang
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09
Medical Image Computing And Computer Assisted Intervention Miccai 2010 written by Tianzi Jiang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09 with Computers categories.
The three-volume set LNCS 6361, 6362 and 6363 constitutes the refereed proceedings of the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, held in Beijing, China, in September 2010. Based on rigorous peer reviews, the program committee carefully selected 251 revised papers from 786 submissions for presentation in three volumes. The first volume includes 84 papers organized in topical sections on computer-aided diagnosis, planning and guidance of interventions, image segmentation, image reconstruction and restoration, functional and diffusion-weighted MRI, modeling and simulation, instrument and patient localization and tracking, quantitative image analysis, image registration, computational and interventional cardiology, and diffusion tensor MR imaging and analysis.
Neural Information Processing
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Author : Akira Hirose
language : en
Publisher: Springer
Release Date : 2016-09-30
Neural Information Processing written by Akira Hirose and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-30 with Computers categories.
The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitutes the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms.
Magnetic Resonance Imaging
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Author : Robert W. Brown
language : en
Publisher: John Wiley & Sons
Release Date : 2014-06-23
Magnetic Resonance Imaging written by Robert W. Brown and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-23 with Medical categories.
New edition explores contemporary MRI principles and practices Thoroughly revised, updated and expanded, the second edition of Magnetic Resonance Imaging: Physical Principles and Sequence Design remains the preeminent text in its field. Using consistent nomenclature and mathematical notations throughout all the chapters, this new edition carefully explains the physical principles of magnetic resonance imaging design and implementation. In addition, detailed figures and MR images enable readers to better grasp core concepts, methods, and applications. Magnetic Resonance Imaging, Second Edition begins with an introduction to fundamental principles, with coverage of magnetization, relaxation, quantum mechanics, signal detection and acquisition, Fourier imaging, image reconstruction, contrast, signal, and noise. The second part of the text explores MRI methods and applications, including fast imaging, water-fat separation, steady state gradient echo imaging, echo planar imaging, diffusion-weighted imaging, and induced magnetism. Lastly, the text discusses important hardware issues and parallel imaging. Readers familiar with the first edition will find much new material, including: New chapter dedicated to parallel imaging New sections examining off-resonance excitation principles, contrast optimization in fast steady-state incoherent imaging, and efficient lower-dimension analogues for discrete Fourier transforms in echo planar imaging applications Enhanced sections pertaining to Fourier transforms, filter effects on image resolution, and Bloch equation solutions when both rf pulse and slice select gradient fields are present Valuable improvements throughout with respect to equations, formulas, and text New and updated problems to test further the readers' grasp of core concepts Three appendices at the end of the text offer review material for basic electromagnetism and statistics as well as a list of acquisition parameters for the images in the book. Acclaimed by both students and instructors, the second edition of Magnetic Resonance Imaging offers the most comprehensive and approachable introduction to the physics and the applications of magnetic resonance imaging.