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$μ$GUIDE a framework for microstructure imaging via generalized uncertainty-driven inference using deep learning

M Jallais, M Palombo - arXiv preprint arXiv:2312.17293, 2023 - arxiv.org
Electrical Engineering and Systems Science paper eess.IV Suggest

… Harnessing a new deep learning architecture for automatic signal feature selection combined with simulationbased inference and efficient sampling of the posterior …

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BibTeX

@article{2312.17293v4,
Author        = {Maëliss Jallais and Marco Palombo},
Title         = {$μ$GUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning},
Eprint        = {2312.17293v4},
ArchivePrefix = {arXiv},
PrimaryClass  = {eess.IV},
Abstract      = {This work proposes $μ$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, $μ$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.},
Year          = {2023},
Month         = {Dec},
Url           = {http://arxiv.org/abs/2312.17293v4},
File          = {2312.17293v4.pdf}
}

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