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

arXiv: https://arxiv.org/abs/2312.17293 · HTML: https://mlsanity.com/paper/2312.17293/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 89 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Masked Autoregressive Flow for Density Estimation](https://mlsanity.com/paper/1705.07057/) (2017)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [MADE: Masked Autoencoder for Distribution Estimation](https://mlsanity.com/paper/1502.03509/) (2015)
- [Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows](https://mlsanity.com/paper/1805.07226/) (2018)
- [Automatic Posterior Transformation for Likelihood-Free Inference](https://mlsanity.com/paper/1905.07488/) (2019)
- [Flexible statistical inference for mechanistic models of neural dynamics](https://mlsanity.com/paper/1711.01861/) (2017)
- [Benchmarking Simulation-Based Inference](https://mlsanity.com/paper/2101.04653/) (2021)
- [Approximating Likelihood Ratios with Calibrated Discriminative Classifiers](https://mlsanity.com/paper/1506.02169/) (2015)
- [Likelihood-free inference with emulator networks](https://mlsanity.com/paper/1805.09294/) (2018)
- [Gibbs-Ringing Artifact Removal Based on Local Subvoxel-shifts](https://mlsanity.com/paper/1501.07758/) (2015)
- [Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI](https://mlsanity.com/paper/1609.09144/) (2016)
- [Complex diffusion-weighted image estimation via matrix recovery under general noise models](https://mlsanity.com/paper/1812.05954/) (2018)
- [Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements](https://mlsanity.com/paper/2111.08693/) (2021)
- [Resolving quantitative MRI model degeneracy with machine learning via training data distribution design](https://mlsanity.com/paper/2303.05464/) (2023)

## Abstract (arXiv)

This 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.