Compressed sensing trends in magnetic resonance imaging

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...

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Published inEngineering science and technology, an international journal Vol. 20; no. 4; pp. 1342 - 1352
Main Authors Sandilya, Mrinmoy, Nirmala, S.R.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.08.2017
Elsevier
Subjects
[formula omitted]-minimization
Adaptive transform
Biomedical imaging
Compressed sensing
l1-minimization
Magnetic resonance imaging
Parallel imaging
Sparse reconstruction
Magnetic resonance imaging
Sparse reconstruction
Adaptive transform
Parallel imaging
Compressed sensing
l1-minimization
Biomedical imaging
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Summary: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.
ISSN:2215-0986
2215-0986
DOI:10.1016/j.jestch.2017.07.001