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We propose a new plug-and-play priors (PnP) based MR image reconstructionmethod that systematically enforces data consistency while also exploitingdeep-learning priors. Our prior is specified through a convolutional neuralnetwork (CNN) trained without any artifact-free ground truth to removeundersampling artifacts from MR images. The results on reconstructingfree-breathing MRI data into ten respiratory phases show that the method canform high-quality 4D images from severely undersampled measurementscorresponding to acquisitions of about 1 and 2 minutes in length. The resultsalso highlight the competitive performance of the method compared to severalpopular alternatives, including the TGV regularization and traditional UNet3D.

Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth