title: Robust Compressed Sensing MRI with Deep Generative Priors
abstract: 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}.
authors: Ajil Jalal Marius Arvinte Giannis Daras Eric Price Alex Dimakis Jonathan Tamir
hIndex: Alex Dimakis 55