$μ$GUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913491227508736 |
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| author | Jallais, Maëliss Palombo, Marco |
| author_facet | Jallais, Maëliss Palombo, Marco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17293 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | $μ$GUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning Jallais, Maëliss Palombo, Marco Image and Video Processing Machine Learning Medical Physics 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. |
| title | $μ$GUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning |
| topic | Image and Video Processing Machine Learning Medical Physics |
| url | https://arxiv.org/abs/2312.17293 |