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Fast and robust quantification of uncertainty in non-linear diffusion MRI models.
Harms, R L; Fritz, F J; Schoenmakers, S; Roebroeck, A.
Afiliación
  • Harms RL; Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands. Electronic address: robbert@xkls.nl.
  • Fritz FJ; Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
  • Schoenmakers S; Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
  • Roebroeck A; Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands. Electronic address: a.roebroeck@maastrichtuniversity.nl.
Neuroimage ; 285: 120496, 2024 Jan.
Article en En | MEDLINE | ID: mdl-38101495
ABSTRACT
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 (MCMC) 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neuritas / Imagen de Difusión por Resonancia Magnética Límite: Humans Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neuritas / Imagen de Difusión por Resonancia Magnética Límite: Humans Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos