Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms A Convex Optimization Approach /

• Main Authors: Deka, Bhabesh (Author, http://id.loc.gov/vocabulary/relators/aut), Datta, Sumit (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Singapore : Springer Singapore : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Springer Series on Bio- and Neurosystems, 9
• Subjects: Signal processing.; Image processing.; Speech processing systems.; Biomedical engineering.; Radiology.; Signal, Image and Speech Processing.; Biomedical Engineering and Bioengineering.; Imaging / Radiology.

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 po...

Table of Contents:

• 1. Introduction to Compressed Sensing Magnetic Resonance Imaging
• 2. Compressed Sensing MRI Reconstruction Problem
• 3. Fast Algorithms for Compressed Sensing MRI Reconstruction
• 4. Simulation Results
• 5. Performance Evaluation and Benchmark Setting
• 6. Conclusions and Future Directions.