Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging.
MR value
autonomous MRI
deep learning
explainable AI
multi-contrast denoising
Journal
Frontiers in neuroimaging
ISSN: 2813-1193
Titre abrégé: Front Neuroimaging
Pays: Switzerland
ID NLM: 9918402387106676
Informations de publication
Date de publication:
2023
2023
Historique:
received:
17
10
2022
accepted:
15
03
2023
medline:
9
8
2023
pubmed:
9
8
2023
entrez:
9
8
2023
Statut:
epublish
Résumé
Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI
Identifiants
pubmed: 37554641
doi: 10.3389/fnimg.2023.1072759
pmc: PMC10406274
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1072759Informations de copyright
Copyright © 2023 Ravi, Nandakumar, Thomas, Lim, Qian, Jimeno, Poojar, Jin, Quarterman, Srinivasan, Fung, Vaughan and Geethanath.
Déclaration de conflit d'intérêts
GN, NT, ML, and GS were employed by PMX. PQ and MF were employed by GE Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from PMX. The funder had the following involvement in the study: data analysis and preparation of the manuscript.
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