The DOI matches its Crossref record; 69 of the 71 references it cites were found in a registry or an index.
Twenty‐five pitfalls in the analysis of diffusion MRI data
Open accessAbstract
Obtaining reliable data and drawing meaningful and robust inferences from diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into pre‐processing of data; estimation of the tensor; derivation of voxelwise quantitative parameters; strategies for extracting quantitative parameters; and finally intra‐subject and inter‐subject comparison, including region of interest, histogram, tract‐specific and voxel‐based analyses. The article covers important aspects of diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel‐based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new diffusion MRI‐based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on diffusion MRI. Copyright © 2010 John Wiley & Sons, Ltd.
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“as pointed out by Jeurissen et al. (2013), even regions with crossing fibers, many observed differences/changes in FA, AD and/or RD are likely to reflect actual anatomical and biophysical phenomena, as long as other pitfalls in the acquisition and analysis have been avoided (Jones and Cercignani, 2010).”
From Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure (2022) · cites this paper as Jones and Cercignani, 2010
In the citing paper
“While DTI based tractography (Basser et al., 2000) has been widely applied, it has difficulties in resolving fibre tracking in areas with mixed fibre orientations due to, for instance, intra-voxel fibre crossing (Jones and Cercignani, 2010; Jeurissen et al., 2017).”
From The cingulum bundle: Anatomy, function, and dysfunction (2018) · cites this paper as Jones and Cercignani, 2010
In the citing paper
“While DTI has provided extraordinary insights into the WM structure of the brain in both health and disease, the interpretation of diffusion data is often hampered by several important limitations and challenges (Beaulieu, 2002; Jones and Cercignani, 2010). Primary among these limitations is a lack of understanding of the actual biological sources underlying widely used DTI-derived measures.”
From The role of myelination in measures of white matter integrity: Combination of diffusion tensor imaging and two-photon microscopy of CLARITY intact brains (2017) · cites this paper as Jones and Cercignani, 2010
In the citing paper
“Moreover, diffusion-weighted imaging has many limitations and sources of error at each step of the analysis pipeline that must be considered when interpreting results236, 237.”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [236]
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Metadata
- Authors
- Jones, D. K., & Cercignani, M.
- Affiliations
- Cardiff University (GB)
- Fondazione Santa Lucia (IT)
- Corresponding author
- Jones, Derek K.
- Venue
- NMR in Biomedicine
- Publisher
- Wiley
- Published
- 2010-08
- Volume
- 23
- Issue
- 7
- Pages
- 803-820
- ISSN
- 0952-3480 (ISSN-L)
- 1099-1492
- OpenAlex
- W2015391962
- Cited by
- 827
- References
- 71
- Citations by year
- 2026: 36 · 2025: 27 · 2024: 36 · 2023: 40 · 2022: 51 · 2021: 51 · 2020: 52 · 2019: 56 · 2018: 51 · 2017: 72 · 2016: 63 · 2015: 79 · 2014: 68 · 2013: 60 · 2012: 51
- FWCI
- 6.57
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Locations
- NMR in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- diffusion MRI, eddy current correction, susceptibility distortion correction, motion correction, diffusion tensor estimation, model fitting, region of interest placement, voxel-based analysis, histogram analysis, diffusion indices
- MeSH terms
- Algorithms, Brain, Cerebrospinal Fluid, Humans, Image Enhancement, Image Processing, Computer-Assisted, Artifacts, Diffusion Magnetic Resonance Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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