  We propose a new plug-and-play priors (PnP) based MR image reconstruction
method that systematically enforces data consistency while also exploiting
deep-learning priors. Our prior is specified through a convolutional neural
network (CNN) trained without any artifact-free ground truth to remove
undersampling artifacts from MR images. The results on reconstructing
free-breathing MRI data into ten respiratory phases show that the method can
form high-quality 4D images from severely undersampled measurements
corresponding to acquisitions of about 1 and 2 minutes in length. The results
also highlight the competitive performance of the method compared to several
popular alternatives, including the TGV regularization and traditional UNet3D.
