Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis
Abstract
Quantitative diffusion tensor imaging (DTI) has become the major imaging modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics of tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that systematically includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. A new measure of tensor anisotropy, called geodesic anisotropy (GA) is applied and compared with FA. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics (average and variance) calculated within cross-sections. Feasibility of our approach is demonstrated on various fiber tracts of a single data set. A validation study, based on six repeated scans of the same subject, assesses the reproducibility of this new DTI data analysis framework.
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Most often, data are averaged across corresponding points along each streamline in the pathway, so data can be analyzed versus streamline arc length or other parameterization.
“Most often, data are averaged across corresponding points along each streamline in the pathway, such that data can be analyzed (e.g., an along-tract or along-pathway plot) versus streamline arc length or other parameterization (Colby et al., 2012b; Corouge et al., 2006; Yeatman et al., 2012).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Corouge et al., 2006
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Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
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Self-citationAdult age differences in functional connectivity during executive control
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- Authors
- COROUGE, I., FLETCHER, P., JOSHI, S., GOUTTARD, S., & GERIG, G.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of Utah (US)
- Corresponding author
- COROUGE, I
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2006-10
- Volume
- 10
- Issue
- 5
- Pages
- 786-798
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2006006542
- Cited by
- 177
- References
- 3
- Citations by year
- 2026: 1 · 2025: 5 · 2023: 2 · 2022: 2 · 2021: 6 · 2020: 4 · 2019: 6 · 2018: 5 · 2017: 7 · 2016: 8 · 2015: 14 · 2014: 11 · 2013: 10 · 2012: 10
- FWCI
- 14.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (MH064065, P50MH064065)
- National Institute of Biomedical Imaging and Bioengineering (U54EB005149, P01EB002779)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (#HD03110, P30HD003110)
- 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 tensor imaging, fiber tracts, tensor interpolation, fractional anisotropy, mean diffusivity, white matter, reproducibility
- MeSH terms
- Algorithms, Artificial Intelligence, Brain, Computer Simulation, Feasibility Studies, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Models, Neurological, Neural Pathways, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Information Storage and Retrieval, Imaging, Three-Dimensional, Diffusion Magnetic Resonance Imaging
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