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), introduced in 2007, is able to reduce the scan time of MRI considerably as it makes it possible to reconstruct MR images from only a few measurements in the k-space, which is 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 standards for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of MR imaging to understand the need for CS in MRI and how it could revolutionize the soft tissue imaging technology to benefit healthcare without making major changes in the existing scanner hardware. It is particularly useful for researchers who have just entered this exciting field and would like to quickly grasp the developments to date without having to dive into the detailed mathematical analysis. Lastly, it discusses recent trends and suggests future research directions that will help consolidate this research for implementation in clinical practice.