Fast and robust quantification of uncertainty in non-linear diffusion MRI models.
Cramér Rao Lower Bound (CRLB)
Diffusion MRI
Fisher Information Matrix (FIM)
Microstructure
Uncertainty estimates
Variances
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
13 Dec 2023
13 Dec 2023
Historique:
received:
23
06
2023
revised:
08
12
2023
accepted:
10
12
2023
medline:
16
12
2023
pubmed:
16
12
2023
entrez:
15
12
2023
Statut:
aheadofprint
Résumé
Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.
Identifiants
pubmed: 38101495
pii: S1053-8119(23)00646-8
doi: 10.1016/j.neuroimage.2023.120496
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
120496Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest None