Accelerated dynamic MR Imaging is a significant but challenging task. Our previous work demonstrated that deep low-rank priors could achieve improved reconstruction performance by unrolling a sparse and low-rank-based optimization algorithm. However, the optimization algorithm is highly customized, and currently, no deep learning methods exist to apply low-rankness as prior to general inverse problems. In this paper, we propose a plug-and-play low-rank network module in dynamic MR imaging. The low-rank network module can be easily embedded into other deep learning models. The embedding of the LR module can effectively improve the reconstruction results.
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