Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations.

Détails

ID Serval
serval:BIB_F41DCA3B8FB7
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations.
Périodique
NeuroImage
Auteur(s)
Rensonnet G., Scherrer B., Girard G., Jankovski A., Warfield S.K., Macq B., Thiran J.P., Taquet M.
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Statut éditorial
Publié
Date de publication
01/01/2019
Peer-reviewed
Oui
Volume
184
Pages
964-980
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural
Publication Status: ppublish
Résumé
Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.
Mots-clé
Animals, Brain/anatomy & histology, Computer Simulation, Diffusion Magnetic Resonance Imaging, Female, Humans, Image Processing, Computer-Assisted/methods, Models, Theoretical, Monte Carlo Method, Rats, Long-Evans, Signal-To-Noise Ratio, White Matter/anatomy & histology, Diffusion-weighted magnetic resonance imaging, Microstructure fingerprinting, Monte Carlo simulations, Sparse optimization, Tissue microstructure
Pubmed
Web of science
Création de la notice
16/10/2018 10:40
Dernière modification de la notice
20/08/2019 16:21
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