A data-driven T
SAME-ECOS
data-driven
machine learning
myelin water imaging
non-negative least squares
resolution limit
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
revised:
16
08
2021
received:
31
08
2020
accepted:
17
08
2021
pubmed:
8
9
2021
medline:
1
2
2022
entrez:
7
9
2021
Statut:
ppublish
Résumé
The decomposition of multi-exponential decay data into a T The theory of SAME-ECOS was derived. Then, a paradigm was presented to demonstrate the SAME-ECOS workflow, consisting of a series of calculation, simulation, and model training operations. The performance of the trained SAME-ECOS model was evaluated using simulations and six in vivo brain datasets. The code is available at https://github.com/hanwencat/SAME-ECOS. Using NNLS as the baseline, SAME-ECOS achieved over 15% higher overall cosine similarity scores in producing the T Compared with NNLS, the SAME-ECOS method yields much more reliable T
Substances chimiques
Water
059QF0KO0R
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
915-931Informations de copyright
© 2021 International Society for Magnetic Resonance in Medicine.
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