1 citations · 1 across the 2 of their papers we have counts for
4 papers
Contextual Cellular Growth (ConCeG) of neural cells for realistic grey matter tissue generation for diffusion MRI simulations
Charlie Aird-Rossiter, Kadir Şimşek, Maëliss Jallais +3
Accurate interpretation of diffusion magnetic resonance imaging (dMRI) signals in grey matter (GM) remains challenging due to the complex, heterogeneous, and densely packed cellula…
Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning
Bradley G. Karat, Maëliss Jallais, Ali R. Khan +3
Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects. In supervised machine learning framew…
Bayesian Insights into Exchange and Restriction in Gray Matter Diffusion MRI
Maëliss Jallais, Quentin Uhl, Tommaso Pavan +4
Biophysical models in diffusion MRI (dMRI) hold promise for characterizing gray matter tissue microstructure. Yet, the reliability of their parameter estimates remains largely unde…
GUIDE: a framework for quantitative imaging via generalized uncertainty-driven inference using deep learning
Maëliss Jallais, Marco Palombo
This work proposes GUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or MRI signal rep…