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Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps.

Kurt G Schilling1,2, Justin Blaber3, Colin Hansen4

  • 1Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America.

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|August 1, 2020
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Summary

This study introduces a deep learning method for correcting geometric distortions in diffusion MRI scans without needing extra data. The approach successfully corrects distortions, improving image quality for microstructure analysis.

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Machine Learning

Background:

  • Diffusion magnetic resonance images (dMRI) are prone to geometric distortions from susceptibility-induced off-resonance fields.
  • These distortions cause misalignment with anatomical images, impacting microstructural and connectivity analyses.
  • Current correction methods often require specific acquisitions (e.g., reverse phase encoding) or additional field map scans, which are not always available.

Purpose of the Study:

  • To develop a susceptibility distortion correction method for dMRI that works with historical or limited datasets lacking specialized correction sequences.
  • To eliminate the need for computationally intensive registration procedures when using structural MRI for distortion correction.
  • To synthesize undistorted b0 images that align geometrically with structural T1w images and match diffusion image intensities using deep learning.

Main Methods:

  • Utilized deep learning, specifically 3D U-nets, to synthesize undistorted b0 images.
  • Trained the model on a heterogeneous dataset comprising diverse structural and diffusion MRI acquisitions.
  • Evaluated the method against state-of-the-art techniques like FSL topup using quantitative metrics and visual assessment.

Main Results:

  • The proposed deep learning pipeline successfully corrected geometric distortions in withheld test datasets.
  • Synthesized b0 images exhibited geometric similarity to non-distorted structural images and closely matched results from state-of-the-art correction methods.
  • Demonstrated generalizability across varied populations, contrasts, resolutions, and distortion types, confirming efficacious correction.

Conclusions:

  • The developed deep learning approach provides an effective solution for susceptibility distortion correction in dMRI, particularly for datasets lacking specialized acquisition sequences.
  • The method avoids complex registration and additional scans, making distortion correction more accessible.
  • The tool's availability as a container, source code, and trained model facilitates broader adoption and evaluation in neuroimaging research.