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Titel:

A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images.

Dokumenttyp:
Journal Article
Autor(en):
Tetteh, Giles; Navarro, Fernando; Meier, Raphael; Kaesmacher, Johannes; Paetzold, Johannes C; Kirschke, Jan S; Zimmer, Claus; Wiest, Roland; Menze, Bjoern H
Abstract:
Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by arterial obstruction. The quality of collateral circulation has been established as a key factor in determining the likelihood of a favorable clinical outcome and goes a long way to determining the choice of a stroke care model. Though many imaging and grading methods exist for quantifying collateral blood flow, the actual grading i...     »
Zeitschriftentitel:
Front Neurol
Jahr:
2023
Band / Volume:
14
Volltext / DOI:
doi:10.3389/fneur.2023.1039693
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/36895903
Print-ISSN:
1664-2295
TUM Einrichtung:
Professur für Neuroradiologie (Prof. Zimmer)
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