Wasserstein GANs for MR Imaging: from Paired to Unpaired Training

Lack of ground-truth MR images (labels) impedes the common supervised training of deep networks for image reconstruction. To cope with this challenge, this paper leverages WGANs for unpaired training of reconstruction networks, where the inputs are the undersampled naively reconstructed images from one dataset, and the outputs are high-quality images from another dataset. The generator network is an unrolled neural network with a cascade of residual blocks and data consistency modules. The discriminator is also a multilayer CNN that plays the role of a critic scoring the quality of reconstructed images. Our extensive experiments with knee MRI datasets demonstrate 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. Also, supervised paired training with additional WGAN loss achieves better and faster reconstruction compared to wavelet-based compressed sensing.