Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms
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
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Bhabesh Deka Sumit Datta
Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms A Convex Optimization Approach
Springer Series on Bio- and Neurosystems Volume 9
Series editor Nikola Kasabov, Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Penrose, New Zealand
The Springer Series on Bio- and Neurosystems publishes fundamental principles and state-of-the-art research at the intersection of biology, neuroscience, information processing and the engineering sciences. The series covers general informatics methods and techniques, together with their use to answer biological or medical questions. Of interest are both basics and new developments on traditional methods such as machine learning, artificial neural networks, statistical methods, nonlinear dynamics, information processing methods, and image and signal processing. New findings in biology and neuroscience obtained through informatics and engineering methods, topics in systems biology, medicine, neuroscience and ecology, as well as engineering applications such as robotic rehabilitation, health information technologies, and many more, are also examined. The main target group includes informaticians and engineers interested in biology, neuroscience and medicine, as well as biologists and neuroscientists using computational and engineering tools. Volumes published in the series include monographs, edited volumes, and selected conference proceedings. Books purposely devoted to supporting education at the graduate and post-graduate levels in bio- and neuroinformatics, computational biology and neuroscience, systems biology, systems neuroscience and other related areas are of particular interest.
More information about this series at http://www.springer.com/series/15821
Bhabesh Deka Sumit Datta •
Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms A Convex Optimization Approach
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Bhabesh Deka Department of Electronics and Communication Engineering Tezpur University Tezpur, Assam, India
Sumit Datta Department of Electronics and Communication Engineering Tezpur University Tezpur, Assam, India
ISSN 2520-8535 ISSN 2520-8543 (electronic) Springer Series on Bio- and Neurosystems ISBN 978-981-13-3596-9 ISBN 978-981-13-3597-6 (eBook) https://doi.org/10.1007/978-981-13-3597-6 Library of Congress Control Number: 2018963034 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such
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