$\mu$GUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning

Autor:	Jallais, Maëliss, Palombo, Marco
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Machine Learning Physics - Medical Physics
Druh dokumentu:	Working Paper
Popis:	This work proposes $\mu$GUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or MRI signal representation, with exemplar demonstration in diffusion-weighted MRI. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, $\mu$GUIDE 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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.17293 Zobrazit plný text záznamu View this record from Arxiv