FD-Net: An Unsupervised Deep Forward-Distortion Model for Susceptibility Artifact Correction in EPI
This work addresses the problem of improving anatomical accuracy in EPI imaging for medical applications, representing an incremental advance over existing unsupervised correction methods.
The paper tackles susceptibility artifact correction in EPI imaging by proposing FD-Net, an unsupervised deep-learning method that predicts both the displacement field and the corrected image, enforcing forward-distortion consistency with acquired data. It performs competitively with the gold-standard TOPUP in image quality while achieving a leap in computational efficiency and outperforms recent unwarping-based methods in image and field quality.
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.