title: Multidimensional compressed sensing MRI using tensor decomposition-based sparsifying transform.
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.
authors.affiliation: School of Information Technology and Electrical Engineering, the University of Queensland, St Lucia, Queensland, Australia.
authors.forename: Yeyang
authors.initials: Y
authors.lastname: Yu
authors.affiliation: School of Information Technology and Electrical Engineering, the University of Queensland, St Lucia, Queensland, Australia.
authors.forename: Jin
authors.initials: J
authors.lastname: Jin
authors.affiliation: School of Information Technology and Electrical Engineering, the University of Queensland, St Lucia, Queensland, Australia.
authors.forename: Feng
authors.initials: F
authors.lastname: Liu
authors.affiliation: School of Information Technology and Electrical Engineering, the University of Queensland, St Lucia, Queensland, Australia.
authors.forename: Stuart
authors.initials: S
authors.lastname: Crozier
mesh_terms: D000465:Algorithms; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical