Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes

Medical Image Analysis, Journal Year: 2025, Volume and Issue: 103, P. 103580 - 103580

Published: April 20, 2025

Language: Английский

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

eLife, Journal Year: 2024, Volume and Issue: 13

Published: Nov. 26, 2024

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 new deep learning architecture for automatic feature selection combined simulation-based inference and efficient sampling the distributions, µGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

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

eLife, Journal Year: 2024, Volume and Issue: 13

Published: Aug. 22, 2024

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 new deep learning architecture for automatic feature selection combined simulation-based inference and efficient sampling the distributions, µGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

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μGUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning

Published: Aug. 22, 2024

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 MRI. Harnessing new deep learning architecture for automatic feature selection combined simulationbased inference and efficient sampling the distributions, μGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

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μGUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning

Published: Oct. 30, 2024

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 MRI. Harnessing new deep learning architecture for automatic feature selection combined simulationbased inference and efficient sampling the distributions, μGUIDE bypasses high computational time cost conventional approaches does not rely on acquisition constraints define model-specific summary statistics. The obtained allow highlight degeneracies present definition quantify uncertainty ambiguity estimated parameters.

Language: Английский

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