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Abstract #1946

A Plug-and-play Low-rank Network ModuleĀ in Dynamic MR Imaging

Ziwen Ke1, Wenqi Huang1, Jing Cheng1, Leslie Ying2, Xin Liu1, Hairong Zheng1, Yanjie Zhu1, and Dong Liang1
1Shenzhen Institutes of Advanced Technology, Shenzhen, China, 2University at Buffalo, The State University of New York, Buffalo, NY, United States

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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