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Abstract #0470

Learning Perceptual Neural Proximals for Robust MRI Recovery

Morteza Mardani1, David Donoho2, John Pauly1, and Shreyas Vasanawala3

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Statistics, Stanford University, Stanford, CA, United States, 3Radiology, Stanford University, Stanford, CA, United States

This abstract proposes a framework for robust reconstruction of MR images from highly undersampled measurements. Inspired by proximal gradient descent (PGD) iterations a recurrent neural network is trained to learn a high diagnostic proximal using Wasserstein GANs. For a Knee dataset, the proximal modeled with only two residual blocks shared across 5 iterations not only trains fast but also reveals fine diagnostic details with limited training data. All in all, this study suggests that sharing the proximal weights across iterations regularizes the reconstruction and improves the generalization.

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