  Functional magnetic resonance imaging techniques benefit from echo-planar
imaging's fast image acquisition but are susceptible to inhomogeneities in the
main magnetic field, resulting in geometric distortion and signal loss
artifacts in the images. Traditional methods leverage a field map or voxel
displacement map for distortion correction. However, voxel displacement map
estimation requires additional sequence acquisitions, and the accuracy of the
estimation influences correction performance. This work implements a novel
approach called GDCNet, which estimates a geometric distortion map by
non-linear registration to T1-weighted anatomical images and applies it for
distortion correction. GDCNet demonstrated fast distortion correction of
functional images in retrospectively and prospectively acquired datasets. Among
the compared models, the 2D self-supervised configuration resulted in a
statistically significant improvement to normalized mutual information between
distortion-corrected functional and T1-weighted images compared to the
benchmark methods FUGUE and TOPUP. Furthermore, GDCNet models achieved
processing speeds 14 times faster than TOPUP in the prospective dataset.
