Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning

Jallais, Maëliss ORCID: https://orcid.org/0000-0001-5939-388X and Palombo, Marco ORCID: https://orcid.org/0000-0003-4892-7967 2024. Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning. eLife 13 , RP101069. 10.7554/elife.101069.3

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Abstract

This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Psychology Computer Science & Informatics Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	eLife Sciences Publications
ISSN:	2050-084X
Date of First Compliant Deposit:	27 November 2024
Date of Acceptance:	30 June 2024
Last Modified:	14 Jan 2025 15:45
URI:	https://orca.cardiff.ac.uk/id/eprint/174328

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