COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography.

Détails

ID Serval
serval:BIB_2DCD509DE29F
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography.
Périodique
Ieee Transactions On Medical Imaging
Auteur⸱e⸱s
Daducci A., Dal Palu A., Lemkaddem A., Thiran J.P.
ISSN
1558-254X (Electronic)
ISSN-L
0278-0062
Statut éditorial
Publié
Date de publication
2015
Peer-reviewed
Oui
Volume
34
Numéro
1
Pages
246-257
Langue
anglais
Notes
Publication types: Journal Article Publication Status: ppublish
Résumé
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.
Pubmed
Web of science
Création de la notice
29/01/2015 20:14
Dernière modification de la notice
20/08/2019 13:12
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