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Abstract #0992

Wasserstein GANs for MR Imaging: from Paired to Unpaired Training

Ke Lei1, Morteza Mardani1,2, Shreyas S. Vasanawala2, and John M. Pauly1
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Lack of ground-truth MR images impedes the common supervised training of deep networks for image reconstruction. This work leverages WGANs for unpaired training of reconstruction networks. The reconstruction network is an unrolled neural network with a cascade of residual blocks and data consistency modules. The discriminator network is a multilayer CNN that acts like a critic, scoring the generated and label images. Our experiments demonstrate that unpaired WGAN training with minimal supervision is a viable option when there exists insufficient or no fully-sampled training label images that match the input images. Adding WGANs to paired training is also shown effective.

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