  Magnetic Resonance Imaging allows high resolution data acquisition with the
downside of motion sensitivity due to relatively long acquisition times. Even
during the acquisition of a single 2D slice, motion can severely corrupt the
image. Retrospective motion correction strategies do not interfere during
acquisition time but operate on the motion affected data. Known methods suited
to this scenario are compressed sensing (CS), generative adversarial networks
(GANs), and explicit motion estimation. In this paper we propose an iterative
approach which uses GAN predictions for motion estimation. The motion estimates
allow to provide data consistent reconstructions and can improve reconstruction
quality and reliability. With this approach, a clinical application of motion
estimation is feasible without any further requirements on the acquisition
trajectory i.e. no temporal redundancy is needed. We evaluate our proposed
supervised network on motion corrupted HASTE acquisitions of brain and abdomen.
