Deep learning-based identification of acute ischemic core and deficit from non-contrast CT and CTA.
Aged
Brain Ischemia
/ diagnostic imaging
Cerebral Angiography
/ methods
Cerebrovascular Circulation
/ physiology
Computed Tomography Angiography
/ methods
Deep Learning
/ standards
Emergency Medicine
/ statistics & numerical data
Female
Humans
Ischemic Stroke
/ therapy
Male
Middle Aged
Neural Networks, Computer
Perfusion Imaging
/ methods
Predictive Value of Tests
Retrospective Studies
Tomography, X-Ray Computed
/ methods
Acute ischemic stroke
deep learning
deficit
ischemic core
multiphase CTA
Journal
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
ISSN: 1559-7016
Titre abrégé: J Cereb Blood Flow Metab
Pays: United States
ID NLM: 8112566
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
pubmed:
10
6
2021
medline:
18
12
2021
entrez:
9
6
2021
Statut:
ppublish
Résumé
The accurate identification of irreversible infarction and salvageable tissue is important in planning the treatments for acute ischemic stroke (AIS) patients. Computed tomographic perfusion (CTP) can be used to evaluate the ischemic core and deficit, covering most of the territories of anterior circulation, but many community hospitals and primary stroke centers do not have the capability to perform CTP scan in emergency situation. This study aimed to identify AIS lesions from widely available non-contrast computed tomography (NCCT) and CT angiography (CTA) using deep learning. A total of 345AIS patients from our emergency department were included. A multi-scale 3D convolutional neural network (CNN) was used as the predictive model with inputs of NCCT, CTA, and CTA+ (8 s delay after CTA) images. An external cohort with 108 patients was included to further validate the generalization performance of the proposed model. Strong correlations with CTP-RAPID segmentations (
Identifiants
pubmed: 34102912
doi: 10.1177/0271678X211023660
pmc: PMC8756471
doi:
Types de publication
Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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