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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