  MRI images reconstructed from sub-sampled Cartesian data using deep learning
techniques often show a characteristic banding (sometimes described as
streaking), which is particularly strong in low signal-to-noise regions of the
reconstructed image. In this work, we propose the use of an adversarial loss
that penalizes banding structures without requiring any human annotation. Our
technique greatly reduces the appearance of banding, without requiring any
additional computation or post-processing at reconstruction time. We report the
results of a blind comparison against a strong baseline by a group of expert
evaluators (board-certified radiologists), where our approach is ranked
superior at banding removal with no statistically significant loss of detail.
