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AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
[article]
2020
arXiv
pre-print
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input
arXiv:2010.01617v1
fatcat:teesd6upvbgirgss7sldyv2g4m