Robust Compressed Sensing MR Imaging 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.
Cite
Text
Jalal et al. "Robust Compressed Sensing MR Imaging with Deep Generative Priors." NeurIPS 2021 Workshops: Deep_Inverse, 2021.Markdown
[Jalal et al. "Robust Compressed Sensing MR Imaging with Deep Generative Priors." NeurIPS 2021 Workshops: Deep_Inverse, 2021.](https://mlanthology.org/neuripsw/2021/jalal2021neuripsw-robust/)BibTeX
@inproceedings{jalal2021neuripsw-robust,
title = {{Robust Compressed Sensing MR Imaging with Deep Generative Priors}},
author = {Jalal, Ajil and Arvinte, Marius and Daras, Giannis and Price, Eric and Dimakis, Alex and Tamir, Jonathan},
booktitle = {NeurIPS 2021 Workshops: Deep_Inverse},
year = {2021},
url = {https://mlanthology.org/neuripsw/2021/jalal2021neuripsw-robust/}
}