Low-rank plus sparse joint smoothing model based on tensor singular value decomposition for dynamic MRI reconstruction

Xiaotong Liu; Jingfei He; Chenghu Mi; Xiaoyue Zhang

doi:10.1016/j.mri.2023.09.003

Low-rank plus sparse joint smoothing model based on tensor singular value decomposition for dynamic MRI reconstruction

Magn Reson Imaging. 2023 Sep 21:104:52-60. doi: 10.1016/j.mri.2023.09.003. Online ahead of print.

Authors

Xiaotong Liu¹, Jingfei He², Chenghu Mi¹, Xiaoyue Zhang¹

Affiliations

¹ Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, 340 Xiping Road, Beichen District, Tianjin 300401, PR China.
² Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, 340 Xiping Road, Beichen District, Tianjin 300401, PR China. Electronic address: hejingfei@hebut.edu.cn.

PMID: 37741515
DOI: 10.1016/j.mri.2023.09.003

Abstract

Dynamic magnetic resonance imaging (DMRI) is an important medical imaging modality, but the long imaging time limits its practical applications. This paper proposes a low-rank plus sparse joint smoothing model based on tensor singular value decomposition (T-SVD) to reconstruct DMR images from highly under-sampled k-t space data. The low-rank plus sparse tensor (ℒ+S) model decomposes the DMR data into a low-rank and sparse tensor, which naturally fits the dynamic MR images characteristics and exploits the spatiotemporal correlation of DMRI data to improve reconstruction effect. T-SVD is utilized in the ℒ+S model to maintain the intrinsic structure of the low-rank tensor and further enhance the low-rank property. In addition, considering the global multi-dimensional smoothness of the DMR images, the proposed method joint tensor total variation (TTV) constraints to utilize the smoothness of DMR images to obtain more reconstruction details while protecting the global structure. We conducted experiments on the dynamic cardiac datasets, and the experiment results show that the proposed method has superior performance to several state-of-the-art imaging methods.

Keywords: Dynamic magnetic resonance imaging; Low rank plus sparsity model; Smoothness; Tensor singular value decomposition; Tensor total variation constraint.