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Poster
Robust Compressed Sensing MRI with Deep Generative Priors
Ajil Jalal · Marius Arvinte · Giannis Daras · Eric Price · Alexandros Dimakis · Jonathan Tamir

Wed Dec 08 04:30 PM -- 06:00 PM (PST) @ None #None

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 oncertain datasets (for example, human faces and MNIST digits), and itis known to perform poorly on out-of-distribution samples. In thispaper, we present the first successful application of the CSGMframework on clinical MRI data. We train a generative prior on brainscans from the fastMRI dataset, and show that posterior sampling viaLangevin dynamics achieves high quality reconstructions. Furthermore,our experiments and theory show that posterior sampling is robust tochanges in the ground-truth distribution and measurement process.Our code and models are available at: \url{https://github.com/utcsilab/csgm-mri-langevin}.

Author Information

Ajil Jalal (University of Texas at Austin)
Marius Arvinte (University of Texas, Austin)
Giannis Daras (The University of Texas at Austin)
Eric Price (University of Texas at Austin)
Alexandros Dimakis (University of Texas, Austin)
Jonathan Tamir (The University of Texas at Austin)

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