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

Abdallah Zaid Alkilani1,2, Tolga Çukur1,2,3, Emine Ulku Saritas1,2,3

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Summary

This study introduces a novel deep learning method, FD-Net, for correcting susceptibility artifacts in echo planar imaging (EPI). FD-Net offers fast and accurate image correction by ensuring consistency with acquired data, significantly improving anatomical accuracy.

Keywords:
deep learningecho planar imagingreversed phase-encodingsusceptibility artifactsunsupervised learning

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Area of Science:

  • Medical Imaging
  • Deep Learning
  • Neuroimaging

Background:

  • Susceptibility artifacts in echo planar imaging (EPI) degrade image quality.
  • Existing correction methods often lack computational efficiency or consistency with acquired data.

Purpose of the Study:

  • To develop an unsupervised deep learning method for fast and effective correction of susceptibility artifacts in reversed phase-encode (PE) EPI image pairs.
  • Introduce the Forward-Distortion Network (FD-Net) for improved anatomical accuracy in EPI.

Main Methods:

  • FD-Net predicts susceptibility-induced displacement fields and anatomically correct images.
  • Enforces consistency between corrected image forward-distortions and acquired reversed-PE EPI data.
  • Utilizes a multiresolution architecture for enhanced performance.

Main Results:

  • FD-Net achieves competitive image quality compared to the gold-standard TOPUP method.
  • Demonstrates significant improvements in computational efficiency over existing methods.
  • Outperforms recent unsupervised methods in both image and field quality.

Conclusions:

  • The unsupervised FD-Net method provides fast, high-fidelity correction of EPI susceptibility artifacts.
  • Maintains consistency with measured data, holding promise for enhanced anatomical accuracy in EPI.
  • Represents a significant advancement in artifact correction for EPI.