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

Chung-Chin Kuo; Teng-Yi Huang; Yi-Ru Lin; Tzu-Chao Chuang; Shang-Yueh Tsai; Ming-Long Wu; Hsiao-Wen Chung

doi:10.1002/mrm.30577

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

Magn Reson Med. 2025 May 23. doi: 10.1002/mrm.30577. Online ahead of print.

Authors

Chung-Chin Kuo¹, Teng-Yi Huang¹, Yi-Ru Lin², Tzu-Chao Chuang³, Shang-Yueh Tsai⁴, Ming-Long Wu^{5

6}, Hsiao-Wen Chung⁷

Affiliations

¹ Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
² Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
³ Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.
⁴ Graduate Institute of Applied Physics, National Chengchi University, Taipei, Taiwan.
⁵ Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.
⁶ Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan.
⁷ Graduate Institute of Biomedical Electronics and Bioinformatics, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.

PMID: 40407812
DOI: 10.1002/mrm.30577

Abstract

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.

Keywords: EPI distortion; deep learning; reversed gradient.

Abstract

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