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Compressed Sensing for Magnetic Resonance Image Reconstruction

£93.00

  • Date Published: February 2015
  • availability: Available
  • format: Hardback
  • isbn: 9781107103764

£ 93.00
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About the Authors
  • 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.

    • Discusses different ways to use mathematical techniques to solve compressed sensing problems
    • Bridges the gap between theoretical aspects of compressed sensing and practical problems in MRI
    • Discusses several problems in biomedical imaging (static and dynamic reconstruction for X-ray and CT) and biomedical signal processing (EEG/ECG reconstruction)
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    Product details

    • Date Published: February 2015
    • format: Hardback
    • isbn: 9781107103764
    • length: 224 pages
    • dimensions: 247 x 189 x 17 mm
    • weight: 0.6kg
    • availability: Available
  • Table of Contents

    List of figures
    List of tables
    Foreword
    Preface
    Acknowledgements
    Color plates
    1. Mathematical techniques
    2. Single channel static MR image reconstruction
    3. Multi-coil parallel MRI reconstruction
    4. Dynamic MRI reconstruction
    5. Applications in other areas
    6. Some open problems
    Index
    About the author.

  • Author

    Angshul Majumdar
    Angshul Majumdar completed his Master's and PhD at the University of British Columbia in 2009 and 2012 respectively. He is currently Assistant Professor at Indraprastha Institute of Information Technology, New Delhi. His primary research interests are optimization algorithms for sparse vector recovery and low-rank matrix completion. The application areas of his research spans across medical imaging, biomedical signal processing, radar signal processing and collaborative filtering recommender systems.

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