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

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.