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Updated: Oct 8, 2025
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.
Abstract:
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
Insights
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.
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.
