Robust Compressed Sensing MRI with Deep Generative Priors

Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alex Dimakis, Jonathan Tamir

Keywords: Compressed sensing, generative priors, MRI reconstruction, Langevin dynamics, Inverse problems

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}.

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TL;DR: Posterior sampling of score-based models is robust for compressed sensing multi-coil MRI

Code: https://github.com/utcsilab/csgm-mri-langevin

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/arxiv:2108.01368/code)