μGUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning
Maëliss Jallais,
Marco Palombo
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 MRI. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulationbased inference and efficient sampling of the posterior distributions, μGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and do… Show more
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