Cited by 292Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

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How and how not to correct for CSF-contamination in diffusion MRI

Metzler-Baddeley, Claudia, O'Sullivan, Michael J., Bells, Sonya, Pasternak, Ofer, Jones, Derek K.·2012·NeuroImage

DOI 10.1016/j.neuroimage.2011.08.043

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Abstract

Diffusion MRI is used extensively to investigate changes in white matter microstructure related to brain development and pathology. Ageing, however, is also associated with significant white and grey matter loss which in turn can lead to cerebrospinal fluid (CSF) based partial volume artefacts in diffusion MRI metrics. This is especially problematic in regions prone to CSF contamination, such as the fornix and the genu of corpus callosum, structures that pass through or close to the ventricles respectively. The aim of this study was to model the effects of CSF contamination on diffusion MRI metrics, and to evaluate different post-acquisition strategies to correct for CSF-contamination: Controlling for whole brain volume and correcting on a voxel-wise basis using the Free Water Elimination (FWE) approach. Using the fornix as an exemplar of a structure prone to CSF-contamination, corrections were applied to tract-specific and voxel-based [tract based spatial statistics (TBSS)] analyses of empirical DT-MRI data from 39 older adults (53-93 years of age). In addition to significant age-related decreases in whole brain volume and fornix tissue volume fraction, age was also associated with a reduction in mean fractional anisotropy and increase in diffusivity metrics in the fornix. The experimental data agreed with the simulations in that diffusivity metrics (mean diffusivity, axial and radial diffusivity) were more prone to partial volume CSF-contamination errors than fractional ani…

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Pierpaoli, 2003, Analyzing the contribution of cardiac pulsation to the variability of quantities derived from the diffusion tensor, Proc. Int. Soc. Magn. Reson. Med., 11, 70

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Authors: Metzler-Baddeley, C., O'Sullivan, M. J., Bells, S., Pasternak, O., & Jones, D. K.
Affiliations: King's College London (GB)
Cardiff University (GB)
Brigham and Women's Hospital (US)
ORCID: O'Sullivan, Michael J.
Bells, Sonya
Pasternak, Ofer
Jones, Derek K.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2012-01
Volume: 59
Issue: 2
Pages: 1394-1403
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2011.08.043
OpenAlex: W2166305102
Cited by: 291
References: 56
Citations by year: 2026: 7 · 2025: 14 · 2024: 18 · 2023: 16 · 2022: 26 · 2021: 17 · 2020: 23 · 2019: 16 · 2018: 23 · 2017: 24 · 2016: 22 · 2015: 21 · 2014: 29 · 2013: 20 · 2012: 14
FWCI: 15.40
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Research Bank (Australian Catholic University) (submittedVersion)
Research Portal (King's College London) (submittedVersion)
Funders: Medical Research Council, UK
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: cerebrospinal fluid contamination, diffusion MRI, fractional anisotropy, axial diffusivity, radial diffusivity, mean diffusivity, fornix, corpus callosum, white matter microstructure, brain aging, tract-based spatial statistics
MeSH terms: Aged, Aged, 80 and over, Algorithms, Brain, Cerebrospinal Fluid, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Male, Middle Aged, Sensitivity and Specificity, Reproducibility of Results, Artifacts, Diffusion Magnetic Resonance Imaging

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