Improving alignment in Tract-based spatial statistics: evaluation and optimization of image registration
Open accessAbstract
Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a "skeleton projection" that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration. To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study. The evaluation framework was highly reproducible for both algorithms (R(2) 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment.
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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 de Groot et al., 2013
The Introduction (page not provided) describes residual alignment errors and warns that TBSS skeleton projection can lose anatomical tract topology; the Conclusions report better alignment with an alternative registration approach. These passages support the misregistration limitation, but our copy does not identify crossing fibres or inaccurate assignment of voxels between neighbouring tracts as TBSS limitations. (LLM, conf=0.86)
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- Authors
- de Groot, M., Vernooij, M. W., Klein, S., Ikram, M. A., Vos, F. M., Smith, S. M., Niessen, W. J., & Andersson, J. L. R.
- Affiliations
- Erasmus MC (NL)
- Amsterdam UMC Location University of Amsterdam (NL)
- University of Oxford (GB)
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 76
- Pages
- 400-411
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC6588540
- OpenAlex
- W2127613226
- Cited by
- 205
- References
- 49
- Citations by year
- 2026: 8 · 2025: 21 · 2024: 23 · 2023: 15 · 2022: 15 · 2021: 16 · 2020: 25 · 2019: 18 · 2018: 14 · 2017: 14 · 2016: 11 · 2015: 15 · 2014: 7 · 2013: 3
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Netherlands Organization of Scientific Research (612.065.821 and 639.031.919)
- Alzheimer Association (NIRG-09-131680)
- National Computing Facilities Foundation (NCF)
- 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
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- tract-based spatial statistics, image registration, diffusion MRI, anatomical alignment, probabilistic tractography, white matter tracts, reproducibility
- MeSH terms
- Algorithms, Brain Mapping, Humans, Image Interpretation, Computer-Assisted, Diffusion Tensor Imaging
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