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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Bibliographic Details

Published in:	Frontiers in Neuroscience
Main Authors:	Anthony J. Winder, Matthias Wilms, Kimberly Amador, Fabian Flottmann, Jens Fiehler, Nils D. Forkert
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2022-11-01
Subjects:	stroke ischemic stroke brain ischemia deep learning precision medicine perfusion CT
Online Access:	https://www.frontiersin.org/articles/10.3389/fnins.2022.1009654/full