Moderating registration misalignment in voxelwise comparisons of DTI data: a performance evaluation of skeleton projection
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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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).”
From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Zalesky, 2011
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References 48 · Cited by 88
- Authors
- Zalesky, A.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Zalesky, Andrew
- ORCID
- 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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