Myo-regressor Deep Informed Neural NetwOrk (Myo-DINO) for fast MR parameters mapping in neuromuscular disorders.
Deep Learning
Extended Phase Graph
MR parameter mapping
Neuromuscular Disorders
Physics-Informed Neural Network
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
28 Aug 2024
28 Aug 2024
Historique:
received:
06
06
2024
revised:
25
08
2024
accepted:
26
08
2024
medline:
6
9
2024
pubmed:
6
9
2024
entrez:
5
9
2024
Statut:
aheadofprint
Résumé
Magnetic Resonance (MR) parameters mapping in muscle Magnetic Resonance Imaging (mMRI) is predominantly performed using pattern recognition-based algorithms, which are characterised by high computational costs and scalability issues in the context of multi-parametric mapping. Deep Learning (DL) has been demonstrated to be a robust and efficient method for rapid MR parameters mapping. However, its application in mMRI domain to investigate Neuromuscular Disorders (NMDs) has not yet been explored. In addition, data-driven DL models suffered in interpretation and explainability of the learning process. We developed a Physics Informed Neural Network called Myo-Regressor Deep Informed Neural NetwOrk (Myo-DINO) for efficient and explainable Fat Fraction (FF), water-T
Identifiants
pubmed: 39236561
pii: S0169-2607(24)00392-4
doi: 10.1016/j.cmpb.2024.108399
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
108399Informations de copyright
Copyright © 2024. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of competing interest LB, FB, RFC, MP, MM, FL, XD, NB, LC, LD, CB, AL, SF, ER, IP report no conflict of interest related to this study. AP has received honorary as consultant and Advisory Board for Genzyme and Amicus Ther. S.R. has received honorary as consultant and Advisory Board for Genzyme, Amicus Ther and Astellas. FS is a consultant for Roche.