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The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deepgenerative 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 poorlyon out-of-distribution samples. In this paper, we present the first successfulapplication of the CSGM framework on clinical MRI data. We train a generativeprior on brain scans from the fastMRI dataset, and show that posterior samplingvia Langevin dynamics achieves high quality reconstructions. Furthermore, ourexperiments and theory show that posterior sampling is robust to changes in theground-truth distribution and measurement process. Our code and models areavailable at: \url{https://github.com/utcsilab/csgm-mri-langevin}.

Robust Compressed Sensing MRI with Deep Generative Priors