Deep Low-rank Prior in Dynamic MR Imaging

The deep learning methods have achieved attractive results in dynamic MR imaging. However, all of these methods only utilize the sparse prior of MR images, while the important low-rank (LR) prior of dynamic MR images is not explored, which limits further improvements of dynamic MR reconstruction. In this paper, a learned singular value thresholding (Learned-SVT) operation is proposed to explore deep low-rank prior in dynamic MR imaging to obtain improved reconstruction results. In particular, we propose two novel and distinct schemes to introduce the learnable low-rank prior into deep network architectures in an unrolling manner and a plug-and-play manner respectively. In the unrolling manner, we propose a model-based unrolling sparse and low-rank network for dynamic MR imaging, dubbed SLR-Net. The SLR-Net is defined over a deep network flow graphs, which is unrolled from the iterative procedures in Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a sparse and low-rank based dynamic MRI model. In the plug-and-play manner, we propose a plug-and-play LR network module that can be easily embedded into any other dynamic MR neural networks without changing the network paradigm. To the best of our knowlegde, this is the first time that a deep low-rank prior has been applied in dynamic MR imaging. Experimental results show that both of the two schemes can further improve the reconstruction results, no matter qualitatively and quantitatively.