Feb. 9, 2024, 5:44 a.m. | Ma\"eliss Jallais Marco Palombo

cs.LG updates on arXiv.org arxiv.org

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 …

architecture bayesian cs.lg deep learning diffusion eess.iv feature feature selection framework general generalized guide imaging inference mri parameters physics.med-ph posterior representation signal simulation uncertainty via work

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