Open AccessMultidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform
Author(s) -
Yeyang Yu,
Jin Jin,
Liu F,
Stuart Crozier
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0098441
Subject(s) - singular value decomposition , compressed sensing , computer science , pattern recognition (psychology) , tensor (intrinsic definition) , temporal resolution , artificial intelligence , dynamic contrast enhanced mri , rank (graph theory) , matrix (chemical analysis) , matrix decomposition , basis (linear algebra) , sparse matrix , algorithm , magnetic resonance imaging , data mining , mathematics , eigenvalues and eigenvectors , physics , medicine , materials science , geometry , quantum mechanics , combinatorics , gaussian , pure mathematics , composite material , radiology
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.