COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography
Open accessAbstract
Tractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion magnetic resonance imaging (MRI) data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic microstructural features of the tissue, such as axonal density and diameter, by using multicompartment models. In this paper, we present a novel framework to reestablish the link between tractography and tissue microstructure. Starting from an input set of candidate fiber-tracts, which are estimated from the data using standard fiber-tracking techniques, we model the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, we seek for the global weight of each of them, i.e., the effective contribution or volume, such that they globally fit the measured signal at best. We demonstrate that these weights can be easily recovered by solving a global convex optimization problem and using efficient algorithms. The effectiveness of our approach has been evaluated both on a realistic phantom with known ground-truth and in vivo brain data. Results clearly demonstrate the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically plausible assessment of the structural connectivity of the brain.
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Streamline filtering techniques can optimize the signal prediction error in order to reduce tractography biases.
“Streamline filtering techniques can optimize the signal prediction error in order to reduce tractography biases14, 16, 51.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [16]
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Self-citation
- Authors
- Daducci, A., Dal Palu, A., Lemkaddem, A., & Thiran, J.-P.
- Affiliations
- Centre Hospitalier Universitaire Vaudois (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2015-01
- Volume
- 34
- Issue
- 1
- Pages
- 246-257
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2077095532
- Cited by
- 255
- References
- 49
- Citations by year
- 2026: 15 · 2025: 23 · 2024: 26 · 2023: 18 · 2022: 25 · 2021: 32 · 2020: 27 · 2019: 26 · 2018: 21 · 2017: 18 · 2016: 12 · 2015: 9 · 2014: 2 · 2013: 1
- FWCI
- 10.69
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- Swiss National Science Foundation (205321-144529)
- Center for Biomedical Imaging (CIBM) of the Geneva-Lausanne Universities and the EPFL
- 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
- tractography, diffusion MRI, white matter microstructure, multi-compartment model, convex optimization, axon density, structural connectivity, quantitative diffusion MRI
- MeSH terms
- Algorithms, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Phantoms, Imaging, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging
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