Compressed sensing trends in magnetic resonance imaging

Abstract

The theory of Compressive Sensing (CS) has experienced a tremendous growth through continuous works of researchers from different cross platform domains of study. The strict realm of Shannon-Nyquist sampling theorem is compromised and an image can be reconstructed from fewer measurements than it was shown necessary to be, but with a trade-off in the efficiency. In biomedical signal processing, especially Magnetic Resonance Imaging (MRI), the potential applicability of CS is long observed. Since then quite a large number of research work in this field has been proposed, a few with experimental analysis, which establish its applicability in the domain of MRI. Since the topic is too broad, this review paper presents a discussion and summary of important works on different fields of CS-MRI. The challenges, limitations and advantages of different techniques of CS-MRI are studied and future trend/ direction is predicted.

Keywords

• Biomedical imaging
• Compressed sensing
• Magnetic resonance imaging
• Sparse reconstruction
• Adaptive transform
• l1-minimization
• Parallel imaging