  Recent learning-based correction approaches in EPI estimate a displacement
field, unwarp the reversed-PE image pair with the estimated field, and average
the unwarped pair to yield a corrected image. Unsupervised learning in these
unwarping-based methods is commonly attained via a similarity constraint
between the unwarped images in reversed-PE directions, neglecting consistency
to the acquired EPI images. This work introduces an unsupervised deep-learning
method for fast and effective correction of susceptibility artifacts in
reversed phase-encode (PE) image pairs acquired with EPI. FD-Net predicts both
the susceptibility-induced displacement field and the underlying
anatomically-correct image. Unlike previous methods, FD-Net enforces the
forward-distortions of the correct image in both PE directions to be consistent
with the acquired reversed-PE image pair. FD-Net further leverages a
multiresolution architecture to maintain high local and global performance.
FD-Net performs competitively with a gold-standard reference method (TOPUP) in
image quality, while enabling a leap in computational efficiency. Furthermore,
FD-Net outperforms recent unwarping-based methods for unsupervised correction
in terms of both image and field quality. The unsupervised FD-Net method
introduces a deep forward-distortion approach to enable fast, high-fidelity
correction of susceptibility artifacts in EPI by maintaining consistency to
measured data. Therefore, it holds great promise for improving the anatomical
accuracy of EPI imaging.
