Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom
Open accessAbstract
As it provides the only method for mapping white matter fibers in vivo, diffusion MRI tractography is gaining importance in clinical and neuroscience research. However, despite the increasing availability of different diffusion models and tractography algorithms, it remains unclear how to select the optimal fiber reconstruction method, given certain imaging parameters. Consequently, it is of utmost importance to have a quantitative comparison of these models and algorithms and a deeper understanding of the corresponding strengths and weaknesses. In this work, we use a common dataset with known ground truth and a reproducible methodology to quantitatively evaluate the performance of various diffusion models and tractography algorithms. To examine a wide range of methods, the dataset, but not the ground truth, was released to the public for evaluation in a contest, the "Fiber Cup". 10 fiber reconstruction methods were evaluated. The results provide evidence that: 1. For high SNR datasets, diffusion models such as (fiber) orientation distribution functions correctly model the underlying fiber distribution and can be used in conjunction with streamline tractography, and 2. For medium or low SNR datasets, a prior on the spatial smoothness of either the diffusion model or the fibers is recommended for correct modelling of the fiber distribution and proper tractography results. The phantom dataset, the ground truth fibers, the evaluation methodology and the results obtained so far will remain publicly available on: http://www.lnao.fr/spip.php?rubrique79 to serve as a comparison basis for existing or new tractography methods. New results can be submitted to fibercup09@gmail.com and updates will be published on the webpage.
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Ex vivo imaging and tracing or physically and numerically simulated phantoms allow validation to some extent, and in specific circumstances such as basic fiber configurations.
“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 [26]
Previous studies have reported high invalid-connection ratios under simplified conditions.
“Previous studies have reported high invalid-connection ratios under simplified conditions26, 38”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [26]
There is no consensus on the best tractography algorithm.
“While many research studies have been performed to compare different tractography algorithms (Bastiani et al., 2012; Fillard et al., 2011; Petrov et al., 2017; Pujol et al., 2015; Sinke et al., 2018; Wilkins et al., 2015; Zhan et al., 2015), there is no consensus on “the best algorithm.””
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Fillard et al., 2011
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Neher, P. F., Götz, M., Norajitra, T., Weber, C., & Maier-Hein, K. H. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_6 · Cited by 47 · see citing works
Chen, H., Liu, T., Zhao, Y., Zhang, T., Li, Y., Li, M., Zhang, H., Kuang, H., Guo, L., Tsien, J. Z., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.050 · Cited by 46 · see citing works
- Authors
- Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., Ramirez-Manzanares, A., Reisert, M., Sakaie, K., Tensaouti, F., Yo, T., Mangin, J.-F., & Poupon, C.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Université de Sherbrooke (CA)
- National University of Singapore (SG)
- University of Utah (US)
- University of Antwerp (BE)
- Brigham and Women's Hospital (US)
- University Medical Center Freiburg (DE)
- Cleveland Clinic (US)
- Inserm (FR)
- Max Planck Institute for Human Cognitive and Brain Sciences (DE)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Corresponding author
- Fillard, Pierre
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-05
- Volume
- 56
- Issue
- 1
- Pages
- 220-234
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1979757232
- Cited by
- 418
- References
- 74
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 13 · 2023: 15 · 2022: 15 · 2021: 25 · 2020: 26 · 2019: 22 · 2018: 24 · 2017: 34 · 2016: 42 · 2015: 44 · 2014: 40 · 2013: 52 · 2012: 30
- FWCI
- 32.71
- Type
- journal article
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion)
- HAL (submittedVersion, other-oa)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- 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
- diffusion MRI tractography, white matter fiber reconstruction, diffusion models, orientation distribution function, signal-to-noise ratio
- MeSH terms
- Algorithms, Brain, Brain Mapping, Humans, Neural Pathways, Phantoms, Imaging, Diffusion Tensor Imaging
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