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Flow-based fiber tracking with diffusion tensor and q-ball data: Validation and comparison to principal diffusion direction techniques
Abstract
In this study, we evaluate the performance of a flow-based surface evolution fiber tracking algorithm by means of a physical anisotropic diffusion phantom with known connectivity. We introduce a novel speed function for surface evolution that is derived from either diffusion tensor (DT) data, high angular resolution diffusion (HARD) data, or a combined DT-HARD hybrid approach. We use the model-free q-ball imaging (QBI) approach for HARD reconstruction. The anisotropic diffusion phantom allows us to compare and evaluate the performance of different fiber tracking approaches in the presence of real imaging artifacts, noise, and subvoxel partial volume averaging of fiber directions. The surface evolution approach, using the full diffusion tensor as opposed to the principal diffusion direction (PDD) only, is compared to PDD-based line propagation fiber tracking. Additionally, DT reconstruction is compared to HARD reconstruction for fiber tracking, both using surface evolution. We show the potential for surface evolution using the full diffusion tensor to map connections in regions of subvoxel partial volume averaging of fiber directions, which can be difficult to map with PDD-based methods. We then show that the fiber tracking results can be improved by using high angular resolution reconstruction of the diffusion orientation distribution function in cases where the diffusion tensor model fits the data poorly.
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“Ex vivo imaging and tracing17–23 or physically24–30 and numerically simulated phantoms31–34 allow validation to some extent, and in specific circumstances such as basic fiber configurations.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [18]
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Tractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,376
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Metadata
- Authors
- Campbell, J. S. W., Siddiqi, K., Rymar, V. V., Sadikot, A. F., & Pike, G. B.
- Affiliations
- McGill University (CA)
- Corresponding author
- Campbell, Jennifer S.W.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2005-10
- Volume
- 27
- Issue
- 4
- Pages
- 725-736
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2156127840
- Cited by
- 177
- References
- 47
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 3 · 2020: 1 · 2019: 2 · 2018: 4 · 2017: 13 · 2016: 6 · 2015: 10 · 2014: 12 · 2013: 15 · 2012: 4
- FWCI
- 11.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- tractography, surface evolution, diffusion tensor imaging, Q-ball imaging, high angular resolution diffusion MRI, orientation distribution function, white matter connectivity
- MeSH terms
- Algorithms, Brain, Humans, Image Processing, Computer-Assisted, Manikins, Nerve Fibers, Reproducibility of Results, Models, Statistical, Anisotropy, Diffusion Magnetic Resonance Imaging
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