mlsanityData to 14 Oct 2026

Evaluation of distortion correction methods in diffusion MRI of the spinal cord

arXiv 2108.03817 ↗First posted 9 Aug 2021eess.IV physics.med-phcited by 3

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Abstract (arXiv)

Background: Magnetic field inhomogeneities generate important geometric distortions in reconstructed echo-planar images. Various procedures were proposed for correcting these distortions on brain images; yet, few neuroimaging studies tailored and incorporated the use of these techniques in spinal cord diffusion MRI. Purpose: We present a comparative evaluation of distortion correction methods that use the reversed gradient polarity technique on spinal cord. We propose novel geometric metrics to measure the alignment of the reconstructed diffusion model with the apparent centerline of the spinal cord. Subjects: 95 subjects, among which 29 healthy controls and 66 multiple sclerosis patients. Assessment: Geometric distortions were corrected using 4 state-of-the-art methods. We measured the alignment of the principal direction of diffusion with the apparent centerline of the spine after correction and the correlation with the reference anatomical image. Results are computed per vertebral level, to evaluate the impact on different portions of the spine. Besides, subjective evaluation of the quality of the correction of healthy subjects images was performed by three expert raters. Results: As a result of distortion correction, the diffusion directions are better aligned locally with the centerline, in particular at both ends of the acquisition window. The cross-correlation with anatomical image is also improved by Hyperelastic Susceptibility Artefact Correction (HySCO) and block-matching. The subjective evaluation for HySCO is significantly better (p < 0.05) than for Block-Matching; TOPUP performs significantly worse than the three other methods. Conclusion: Correction based on HySCO provide best results among the selected methods.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Aug 2021.

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Citing papers per year: 2024: 2; 2025: 1.

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