Attention-guided network for image registration of accelerated cardiac CINE

Motion-resolved reconstruction methods permit for considerable acceleration for cardiac CINE acquisition. Solving for the non-rigid cardiac motion is computationally demanding, and even more challenging in highly accelerated acquisitions, due to the undersampling artifacts in image domain. Here, we introduce a novel deep learning-based image registration network, GMA-RAFT, for estimating cardiac motion from accelerated imaging. A transformer-based module enhances the iterative recurrent refinement of the estimated motion by introducing structural self-similarities into the decoded features. Experiments on Cartesian and radial trajectories demonstrate superior results compared to other deep learning and state-of-the-art baselines in terms of motion estimation and motion-compensated reconstruction.