Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform

Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimension...

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Main Authors:	Yu, Yeyang, Jin, Jin, Liu, Feng, Crozier, Stuart
Format:	Online
Language:	English
Published:	Public Library of Science 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4047014/

id	pubmed-4047014
recordtype	oai_dc
spelling	pubmed-40470142014-06-09 Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform Yu, Yeyang Jin, Jin Liu, Feng Crozier, Stuart Research Article Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the image sparsity. Traditional methods typically sparsify the spatial and temporal information independently. In this work, we propose a novel concept of tensor sparsity for the application of CS in dynamic MRI, and present the Higher-order Singular Value Decomposition (HOSVD) as a practical example. Applications presented in the three- and four-dimensional MRI data demonstrate that HOSVD simultaneously exploited the correlations within spatial and temporal dimensions. Validations based on cardiac datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods and, outperformed the conventional sparse recovery methods. Public Library of Science 2014-06-05 /pmc/articles/PMC4047014/ /pubmed/24901331 http://dx.doi.org/10.1371/journal.pone.0098441 Text en © 2014 Yu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Yu, Yeyang Jin, Jin Liu, Feng Crozier, Stuart
spellingShingle	Yu, Yeyang Jin, Jin Liu, Feng Crozier, Stuart Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform
author_facet	Yu, Yeyang Jin, Jin Liu, Feng Crozier, Stuart
author_sort	Yu, Yeyang
title	Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform
title_short	Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform
title_full	Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform
title_fullStr	Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform
title_full_unstemmed	Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform
title_sort	multidimensional compressed sensing mri using tensor decomposition-based sparsifying transform
description	Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the image sparsity. Traditional methods typically sparsify the spatial and temporal information independently. In this work, we propose a novel concept of tensor sparsity for the application of CS in dynamic MRI, and present the Higher-order Singular Value Decomposition (HOSVD) as a practical example. Applications presented in the three- and four-dimensional MRI data demonstrate that HOSVD simultaneously exploited the correlations within spatial and temporal dimensions. Validations based on cardiac datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods and, outperformed the conventional sparse recovery methods.
publisher	Public Library of Science
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4047014/
_version_	1612097510237536256

Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform

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