Learning to Predict Ischemic Stroke Growth on Acute CT Perfusion Data by Interpolating Low-Dimensional Shape Representations

Learning to Predict Ischemic Stroke Growth on Acute CT Perfusion Data by Interpolating Low-Dimensional Shape Representations

Cerebrovascular diseases, in particular ischemic stroke, are one of the leading global causes of death in developed countries. Perfusion CT and/or MRI are ideal imaging modalities for characterizing affected ischemic tissue in the hyper-acute phase. If infarct growth over time could be predicted acc...

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Bibliographic Details
Main Authors: Christian Lucas, André Kemmling, Nassim Bouteldja, Linda F. Aulmann, Amir Madany Mamlouk, Mattias P. Heinrich
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-11-01
Series:Frontiers in Neurology
Subjects:
ischemic
stroke
prediction
growth
learning
shape
Online Access:https://www.frontiersin.org/article/10.3389/fneur.2018.00989/full

Internet

https://www.frontiersin.org/article/10.3389/fneur.2018.00989/full

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