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<p>Diffusion MRI fiber tractography is sensitive to noise and artifacts in diffusion-weighted images, and these challenges can propagate into fiber-orientation estimation and the tractography process. In this “Did You Know” communication, we synthesize evidence that state-of-the-art preprocessing improves tractography anatomical fidelity and test-retest reproducibility compared to minimally processed data. We summarize best-practice preprocessing – including denoising, motion and eddy current correction, EPI distortion correction, and Gibbs ringing removal – along with additional and emerging steps, and highlight integrated, publicly available pipelines that implement these methods in standardized, containerized workflows. We also outline practical acquisition and data-handling considerations that maximize the benefits of modern processing, providing a foundation for reliable tractography-based studies of the brain.</p>

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Did you know? State-of-the-art preprocessing diffusion MRI data can improve tractography

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

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  • Kurt G. Schilling,
  • Matthew Cieslak,
  • Maxime Descoteaux,
  • Bennett A. Landman,
  • Franco Pestilli,
  • Ariel Rokem,
  • Stamatios N. Sotiropoulos,
  • Jacques-Donald Tournier,
  • Jelle Veraart

摘要

Diffusion MRI fiber tractography is sensitive to noise and artifacts in diffusion-weighted images, and these challenges can propagate into fiber-orientation estimation and the tractography process. In this “Did You Know” communication, we synthesize evidence that state-of-the-art preprocessing improves tractography anatomical fidelity and test-retest reproducibility compared to minimally processed data. We summarize best-practice preprocessing – including denoising, motion and eddy current correction, EPI distortion correction, and Gibbs ringing removal – along with additional and emerging steps, and highlight integrated, publicly available pipelines that implement these methods in standardized, containerized workflows. We also outline practical acquisition and data-handling considerations that maximize the benefits of modern processing, providing a foundation for reliable tractography-based studies of the brain.