ArticleeLife2024
Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed.
- Comparative Systematic Analysis of Gray Matter Biophysical Models on a Public Dataset.Magnetic resonance in medicine · 2026Article
- Simulation-based inference at the theoretical limit for fast, robust microstructural MRI with minimal diffusion data.Communications medicine · 2026Article
- Multiscale Approximations to Understand the Complex Role of Microglia in Alzheimer's Disease.The European journal of neuroscience · 2026Review
- Multi-modal Monte Carlo MRI simulator of tissue microstructure.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Implicit neural representations for accurate estimation of the Standard Model of white matter.Communications biology · 2025Article
- Article
- Histology-informed microstructural diffusion simulations for MRI cancer characterisation-the Histo-μSim framework.Communications biology · 2025Article
- FORCE: FORward modeling for Complex microstructure Estimation.Research square · 2025Article
- In vivo cortical microstructure mapping using high-gradient diffusion MRI accounting for intercompartmental water exchange effects.NeuroImage · 2025Article
- Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes.Medical image analysis · 2025Article
- Implicit neural representations for accurate estimation of the standard model of white matter.ArXiv · 2025Article
- A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI.Medical image analysis · 2025Article
- Non-parametric prediction of brain MRI microstructure using transfer learning.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Microstructure.jl: A Julia package for probabilistic microstructure model fitting with diffusion MRI.Imaging neuroscience (Cambridge, Mass.)Article
- Bayesian insights into exchange and restriction in gray matter diffusion MRI.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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 magnetic resonance imaging. 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.