  The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep
generative priors can be powerful tools for solving inverse problems. However,
to date this framework has been empirically successful only on certain datasets
(for example, human faces and MNIST digits), and it is known to perform poorly
on out-of-distribution samples. In this paper, we present the first successful
application of the CSGM framework on clinical MRI data. We train a generative
prior on brain scans from the fastMRI dataset, and show that posterior sampling
via Langevin dynamics achieves high quality reconstructions. Furthermore, our
experiments and theory show that posterior sampling is robust to changes in the
ground-truth distribution and measurement process. Our code and models are
available at: \url{https://github.com/utcsilab/csgm-mri-langevin}.
