Zaid Alkilani, AbdallahÇukur, TolgaSarıtaş, Emine Ülkü2024-03-172024-03-172023-08-150740-3194https://hdl.handle.net/11693/114830PurposeTo 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.en-USCC BY-NC-ND 4.0 DEED (Attribution-NonCommercial-NoDerivs 4.0 International)https://creativecommons.org/licenses/by-nc-nd/4.0/Deep learningEcho planar imagingReversed phase-encodingSusceptibility artifactsUnsupervised learningArticle10.1002/mrm.298511522-2594