Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results.
Détails
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Accès restreint UNIL
Etat: Public
Version: Final published version
Licence: Non spécifiée
Accès restreint UNIL
Etat: Public
Version: Final published version
Licence: Non spécifiée
ID Serval
serval:BIB_B5B9F4CB73C5
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results.
Périodique
Frontiers in neuroinformatics
ISSN
1662-5196 (Print)
ISSN-L
1662-5196
Statut éditorial
Publié
Date de publication
2020
Peer-reviewed
Oui
Volume
14
Pages
8
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Résumé
Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate's size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework's capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work, along with the simulator developed, pave the way toward more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI.
Mots-clé
MRI, Monte-Carlo, diffusion, microstructure, simulations, white matter
Pubmed
Web of science
Open Access
Oui
Création de la notice
01/04/2020 18:26
Dernière modification de la notice
09/08/2024 14:52