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Abstract #0989

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

Maëliss Jallais1,2 and Marco Palombo1,2
1Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom, 2School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

Synopsis

Keywords: Signal Modeling, Microstructure, dMRIThis work proposes µGUIDE: 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, 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.

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Keywords