Unsupervised Deep Unrolled Reconstruction Powered by Regularization by Denoising
Peizhou Huang1, Chaoyi Zhang2, Hongyu Li2, Ruiying Liu2, Xiaoliang Zhang1, Xiaojuan Li3, Dong Liang4, and Leslie Ying1,2
1Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, 2Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, 3Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, 4Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS, Shenzhen, China
In this abstract, we propose a novel reconstruction method, named DURED-Net, that enables interpretable unsupervised learning for MR image reconstruction by combining an unsupervised denoising network and a plug-and-play method. Specifically, we incorporate the physics model into the denoising network using Regularization by Denoising (RED), and unroll the underlying optimization into a deep neural network. In addition, a sampling pattern was specially designed to facilitate unsupervised learning. Experiment results based on the knee fastMRI dataset exhibit marked improvements over the existing unsupervised reconstruction methods.
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