Cited by 88Advanced Neuroimaging Techniques and ApplicationsMedical Image Segmentation TechniquesFunctional Brain Connectivity Studies

Moderating registration misalignment in voxelwise comparisons of DTI data: a performance evaluation of skeleton projection

Zalesky, Andrew·2011·Magnetic Resonance Imaging

Abstract

An evaluative methodology and five accompanying performance measures were developed to quantitatively assess the performance of the skeleton projection algorithm constituting the heart of tract-based spatial statistics (TBSS). The performance measures were designed to quantify the accuracy of skeleton projection in its indented task of alleviating any residual misalignment that may remain after image registration. A ground truth fractional anisotropy (FA) image was slightly warped using a realistic warp field that served to model post-registration residual misalignment of varying magnitudes. Skeleton projection was then used to register the warped FA image to the ground truth. Performing skeleton projection was found to yield up to 50% better correspondence between the values of FA compared to smoothing, despite the fact that less than 10% of post-registration misalignment was corrected. The align-max-with-max strategy underlying TBSS was posited as a potential explanation for this high correspondence in the values of FA, at the expense of lesser alignment between anatomically concordant voxels.

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Crossref · 10.1016/j.mri.2010.06.027

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  • TBSS has several limitations, such as susceptibility to misregistration artefacts, crossing fibres, and inaccurate voxel-assignment between neighbouring fibre tracts during the skeletonization procedure.

    “Furthermore, although TBSS is a widely-used approach to analyse DTI, the method has several limitations as previously described, such as susceptibility to misregistration artefacts, crossing fibres, and inaccurate voxel-assignment between neighbouring fibre tracts during the skeletonization procedure (Zalesky, 2011; de Groot et al., 2013; Bach et al., 2014).”

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References 48 · Cited by 88

Authors
Zalesky, A.
Affiliations
The University of Melbourne (AU)
Corresponding author
Zalesky, Andrew
Venue
Magnetic Resonance Imaging
Publisher
Elsevier BV
Published
2011-01
Volume
29
Issue
1
Pages
111-125
ISSN
0730-725X (ISSN-L)
OpenAlex
W2008132335
Cited by
88
References
48
Citations by year
2026: 1 · 2025: 3 · 2024: 1 · 2023: 3 · 2022: 6 · 2021: 6 · 2020: 10 · 2019: 2 · 2018: 6 · 2017: 16 · 2016: 12 · 2015: 8 · 2014: 4 · 2013: 5 · 2012: 3
FWCI
2.60
Type
journal article
Language
en
Open access
closed
Locations
  • Magnetic Resonance Imaging (publishedVersion)
  • PubMed (publishedVersion)
Funders
Australian Research Council
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
tract-based spatial statistics, diffusion tensor imaging, fractional anisotropy, image registration, white matter tracts, performance evaluation
MeSH terms
Algorithms, Brain, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Nerve Fibers, Myelinated, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Artifacts, Imaging, Three-Dimensional, Diffusion Tensor Imaging

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