AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language

INTRODUCTION: The complexity of Magnetic Resonance Imaging (MRI) sequences requires expert knowledge about the underlying contrast mechanisms to select from the wide range of available applications and protocols. Automation of this process using machine learning (ML) can support the radiologists and...

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Detalles Bibliográficos
Autores principales: Hoinkiss, Daniel Christopher, Huber, Jörn, Plump, Christina, Lüth, Christoph, Drechsler, Rolf, Günther, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Neuroimaging
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406289/
https://www.ncbi.nlm.nih.gov/pubmed/37554629
http://dx.doi.org/10.3389/fnimg.2023.1090054

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406289/
https://www.ncbi.nlm.nih.gov/pubmed/37554629
http://dx.doi.org/10.3389/fnimg.2023.1090054

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