FD‐Net: An unsupervised deep forward‐distortion model for susceptibility artifact correction in EPI

Abstract

AbstractPurposeTo introduce an unsupervised deep‐learning method for fast and effective correction of susceptibility artifacts in reversed phase‐encode (PE) image pairs acquired with echo planar imaging (EPI).MethodsRecent 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 a novel unsupervised deep Forward‐Distortion Network (FD‐Net) that 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.ResultsFD‐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.ConclusionThe 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.