Multidimensional compressed sensing MRI using tensor decomposition-based sparsifying transform

Yu, Yeyang, Jin, Jin, Liu, Feng and Crozier, Stuart (2014) Multidimensional compressed sensing MRI using tensor decomposition-based sparsifying transform. PLoS One, 9 6: e98441.1-e98441.13. doi:10.1371/journal.pone.0098441

Author Yu, Yeyang
Jin, Jin
Liu, Feng
Crozier, Stuart
Title Multidimensional compressed sensing MRI using tensor decomposition-based sparsifying transform
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2014-06-05
Sub-type Article (original research)
DOI 10.1371/journal.pone.0098441
Open Access Status DOI
Volume 9
Issue 6
Start page e98441.1
End page e98441.13
Total pages 13
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Language eng
Subject 1300 Biochemistry, Genetics and Molecular Biology
1100 Agricultural and Biological Sciences
Abstract 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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 24 Jun 2014, 19:34:07 EST by Mr Jin Jin on behalf of School of Information Technol and Elec Engineering