Advertisement

Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

A Convex Optimization Approach

  • Bhabesh Deka
  • Sumit Datta

Part of the Springer Series on Bio- and Neurosystems book series (SSBN, volume 9)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Bhabesh Deka, Sumit Datta
    Pages 23-29
  3. Bhabesh Deka, Sumit Datta
    Pages 31-74
  4. Bhabesh Deka, Sumit Datta
    Pages 75-98
  5. Bhabesh Deka, Sumit Datta
    Pages 99-110
  6. Bhabesh Deka, Sumit Datta
    Pages 111-122

About this book

Introduction

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.

Keywords

Rapid magnetic resonance image reconstruction k-space undersampling Compressed sensing MRI Fast L1-norm regularization Composite splitting based CS-MRI Clinical CS-MRI CS-MRI reconstruction algorithm

Authors and affiliations

  • Bhabesh Deka
    • 1
  • Sumit Datta
    • 2
  1. 1.Department of Electronics and Communication EngineeringTezpur UniversityTezpurIndia
  2. 2.Department of Electronics and communication EngineeringTezpur UniversityTezpurIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-13-3597-6
  • Copyright Information Springer Nature Singapore Pte Ltd. 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-13-3596-9
  • Online ISBN 978-981-13-3597-6
  • Series Print ISSN 2520-8535
  • Series Online ISSN 2520-8543
  • Buy this book on publisher's site
Industry Sectors
Pharma
Automotive
Biotechnology
Finance, Business & Banking
Electronics
IT & Software
Telecommunications
Consumer Packaged Goods
Aerospace
Oil, Gas & Geosciences
Engineering