GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation

GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation

Conventional magnetic resonance imaging (cMRI) in multiple sclerosis (MS) patients provides measures of focal brain damage and activity, which are fundamental for disease diagnosis, prognosis, and the evaluation of response to therapy. However, cMRI is insensitive to the damage to the microenvironme...

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Bibliographic Details
Main Authors: Po-Jui Lu, Muhamed Barakovic, Matthias Weigel, Reza Rahmanzadeh, Riccardo Galbusera, Simona Schiavi, Alessandro Daducci, Francesco La Rosa, Meritxell Bach Cuadra, Robin Sandkühler, Jens Kuhle, Ludwig Kappos, Philippe Cattin, Cristina Granziera
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-04-01
Series:Frontiers in Neuroscience
Subjects:
multiple sclerosis
deep learning
advanced quantitative diffusion MRI
relative importance order
clinically correlated measure selection
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2021.647535/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2021.647535/full

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