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.
