Predicting the tissue outcome of acute ischemic stroke from acute 4D computed tomography perfusion imaging using temporal features and deep learning

Predicting follow-up lesions from baseline CT perfusion (CTP) datasets in acute ischemic stroke patients is important for clinical decision making. Deep convolutional networks (DCNs) are assumed to be the current state-of-the-art for this task. However, many DCN classifiers have not been validated a...

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Detalles Bibliográficos
Autores principales: Winder, Anthony J., Wilms, Matthias, Amador, Kimberly, Flottmann, Fabian, Fiehler, Jens, Forkert, Nils D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672821/
https://www.ncbi.nlm.nih.gov/pubmed/36408399
http://dx.doi.org/10.3389/fnins.2022.1009654

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672821/
https://www.ncbi.nlm.nih.gov/pubmed/36408399
http://dx.doi.org/10.3389/fnins.2022.1009654

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