  Accurate motion estimation at high acceleration factors enables rapid
motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without
compromising the diagnostic image quality. In this work, we introduce an
attention-aware deep learning-based framework that can perform non-rigid
pairwise registration for fully sampled and accelerated MRI. We extract local
visual representations to build similarity maps between the registered image
pairs at multiple resolution levels and additionally leverage long-range
contextual information using a transformer-based module to alleviate
ambiguities in the presence of artifacts caused by undersampling. We combine
local and global dependencies to perform simultaneous coarse and fine motion
estimation. The proposed method was evaluated on in-house acquired fully
sampled and accelerated data of 101 patients and 62 healthy subjects undergoing
cardiac and thoracic MRI. The impact of motion estimation accuracy on the
downstream task of motion-compensated reconstruction was analyzed. We
demonstrate that our model derives reliable and consistent motion fields across
different sampling trajectories (Cartesian and radial) and acceleration factors
of up to 16x for cardiac motion and 30x for respiratory motion and achieves
superior image quality in motion-compensated reconstruction qualitatively and
quantitatively compared to conventional and recent deep learning-based
approaches. The code is publicly available at
https://github.com/lab-midas/GMARAFT.
