GDCNet: Calibrationless geometric distortion correction of echo planar
  imaging data using deep learning
Marina Manso Jimeno Keren Bachi George Gardner Yasmin L. Hurd John Thomas Vaughan Jr. Sairam Geethanath
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.