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Abstract #4648

Unpaired Super-Resolution GANs for MR Image Reconstruction

Ke Lei1, Morteza Mardani1,2, Shreyas Vasawanala2, and John Pauly1

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

While undersampled MRI data is easy to obtain, lack of high-quality labels for dynamic organs impedes the common supervised training of deep neural nets for MRI reconstruction. We propose an unpaired training super-resolution model with pure GAN loss to use a minimal amount of labels but all available low-quality data for training. Leveraging Wasserstein-GANs with gradient penalty followed by a data-consistency refinement high-quality Knee MR images are recovered from 3-fold undersampled single coil measurements using 20% of the labels compared with a paired training model.

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