Deep Low-rank Prior in Dynamic MR Imaging
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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, we 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 deep 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 neural network paradigm. Experimental results show that both of the two schemes can significantly improve the reconstruction results, no matter qualitatively and quantitatively.
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