  Modern diffusion MRI sequences commonly acquire a large number of volumes
with diffusion sensitization gradients of differing strengths or directions.
Such sequences rely on echo-planar imaging (EPI) to achieve reasonable scan
duration. However, EPI is vulnerable to off-resonance effects, leading to
tissue susceptibility and eddy-current induced distortions. The latter is
particularly problematic because it causes misalignment between volumes,
disrupting downstream modelling and analysis. The essential correction of eddy
distortions is typically done post-acquisition, with image registration.
However, this is non-trivial because correspondence between volumes can be
severely disrupted due to volume-specific signal attenuations induced by
varying directions and strengths of the applied gradients. This challenge has
been successfully addressed by the popular FSL~Eddy tool but at considerable
computational cost. We propose an alternative approach, leveraging recent
advances in image processing enabled by deep learning (DL). It consists of two
convolutional neural networks: 1) An image translator to restore correspondence
between images; 2) A registration model to align the translated images. Results
demonstrate comparable distortion estimates to FSL~Eddy, while requiring only
modest training sample sizes. This work, to the best of our knowledge, is the
first to tackle this problem with deep learning. Together with recently
developed DL-based susceptibility correction techniques, they pave the way for
real-time preprocessing of diffusion MRI, facilitating its wider uptake in the
clinic.
