Accelerating MRI Using Vision Transformer with Unpaired Unsupervised Training

In this abstract, we propose a novel deep-learning reconstruction method that enables training with only unpaired undersampled k-space data without the ground truth. The network utilizes a statistical model for the undersampling artifacts to enable unsupervised learning, and the generative adversarial network to enable unpaired training. In addition, the physics model is incorporated into the transformer network by unrolling the underlying optimization problem. Experiment results based on the fastMRI knee dataset exhibit marked improvements over the existing state-of-the-art reconstructions.