Referenceless reduction of spin-echo echo-planar imaging distortion with generative displacement mapping.

Purpose: We aimed to develop a fully automatic, referenceless method for correcting distortions in echo-planar imaging (EPI) data sets, specifically designed for applications in retrospective studies lacking reference field maps or reversed-gradient scans. This work primarily targets data sets acquired with anterior-posterior or posterior-anterior phase-encoding protocols.

Methods: Our approach used a generative adversarial network to generate a displacement map. The network model took a three-dimensional raw b0 volume from a diffusion-tensor data set as input and reproduced a displacement map, similar to that originally derived using a reversed-gradient correction method. This generative displacement map was used to correct echo-planar images across an entire diffusion data set.

Results: The performance of our method was evaluated across multiple institutions using large-scale databases. We found that it effectively reduced geometric distortions in EPI data sets and improved the accuracy of diffusion indices. Moreover, it significantly enhanced the coregistration between EPI and high-resolution T₁-weighted images (p < 0.01).

Conclusions: Our referenceless EPI distortion correction method has been publicly shared as a standalone application and offers a practical solution for enhancing the quality of EPI data sets in retrospective studies. It effectively reduces distortions and increases the accuracy of diffusion measures, making it a valuable tool for studies where EPI data contain no distortion calibration scan.