activity
20202024
most citedModel-Informed Machine Learning for Multi-component T2 Relaxometry

7 citations · 9 across the 4 of their papers we have counts for

collaborators

4 papers

physics.med-ph2024★ 2 cited

A diffusion MRI model for random walks confined on cylindrical surfaces: Towards non-invasive quantification of myelin sheath radius

Erick J Canales-Rodríguez, Chantal M. W. Tax, Elda Fischi-Gomez +3

Quantifying the myelin sheath radius of myelinated axons in vivo is important for understanding, diagnosing, and monitoring various neurological disorders. Despite advancements in…

cs.CE2023

CACTUS: A Computational Framework for Generating Realistic White Matter Microstructure Substrates

Juan Luis Villarreal-Haro, Remy Gardier, Erick J Canales-Rodriguez +4

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in c…

eess.IV2021

Multi-compartment diffusion MRI, T2 relaxometry and myelin water imaging as neuroimaging descriptors for anomalous tissue detection

Elda Fischi-Gomez, Jonathan Rafael-Patino, Marco Pizzolato +5

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease characterized by diffuse and focal areas of tissue loss. Conventional MRI techniques such as T1-weighted an…

physics.med-ph2020★ 7 cited

Model-Informed Machine Learning for Multi-component T2 Relaxometry

Thomas Yu, Erick Jorge Canales Rodriguez, Marco Pizzolato +9

Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-st…