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Magnetic Resonance Imaging

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Updated: Oct 8, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

What's new and what's next in diffusion MRI preprocessing.

Chantal M W Tax¹, Matteo Bastiani², Jelle Veraart³

¹Image Sciences Institute, University Medical Center Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre, School of Physics and Astronomy, Cardiff University, UK.

|December 29, 2021

Summary

This review details diffusion MRI (dMRI) artifacts and preprocessing steps, highlighting recent advancements and new strategies for improved brain connectivity analysis. It covers new tools and quality control for more reliable dMRI results.

Keywords:

Artifacts Diffusion MRI Distortion Preprocessing

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Area of Science:

Neuroimaging
Biomedical Engineering
Radiology

Background:

Diffusion MRI (dMRI) is crucial for studying brain microstructure and connectivity.
dMRI data is susceptible to various artifacts that compromise analysis and interpretation.
Recent years have seen significant advances in addressing these challenges.

Purpose of the Study:

To provide a comprehensive review of dMRI artifacts and preprocessing techniques.
To highlight novel strategies and recent developments since the Human Connectome Project (HCP).
To discuss practical considerations and future directions in dMRI preprocessing.

Main Methods:

Review of established and emerging dMRI artifacts (e.g., motion, distortions, noise).
Examination of preprocessing steps including brain/skull extraction, denoising, and spatial normalization.
Discussion of new tools, open-source pipelines, and quality control frameworks.

Main Results:

Identification of key artifacts: B-matrix incompatibilities, signal drift, Gibbs ringing, motion, eddy currents, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, and B1 bias fields.
Emphasis on advancements in artifact correction and preprocessing strategies.
Progress in open-source tools, reproducible pipelines, and automated quality control.

Conclusions:

Effective dMRI preprocessing is essential for accurate analysis of brain microstructure and connectivity.
Novel strategies and open-source tools are enhancing the reliability and reproducibility of dMRI studies.
Continued development in preprocessing is vital for the future of connectomics research.