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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4047014/ https://www.ncbi.nlm.nih.gov/pubmed/24901331 http://dx.doi.org/10.1371/journal.pone.0098441 |
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author | Yu, Yeyang Jin, Jin Liu, Feng Crozier, Stuart |
author_facet | Yu, Yeyang Jin, Jin Liu, Feng Crozier, Stuart |
author_sort | Yu, Yeyang |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-4047014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-40470142014-06-09 Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform Yu, Yeyang Jin, Jin Liu, Feng Crozier, Stuart PLoS One 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. |
spellingShingle | Research Article Yu, Yeyang Jin, Jin Liu, Feng Crozier, Stuart Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform |
title | 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_short | Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform |
title_sort | multidimensional compressed sensing mri using tensor decomposition-based sparsifying transform |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4047014/ https://www.ncbi.nlm.nih.gov/pubmed/24901331 http://dx.doi.org/10.1371/journal.pone.0098441 |
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