Abstract #2831

Deep Learning-based Detection of DSC-Defined Penumbral Tissue on pCASL in Acute Ischemic Stroke

Kai Wang¹, Qinyang Shou^2,3, Samantha J. Ma¹, David Liebeskind⁴, Xin J. Qiao⁵, Fabien Scalzo⁴, Jeffrey Saver⁴, Noriko Salamon⁵, and Danny JJ Wang^1,4

¹Lab of Functional MRI Technology, University of Southern California, Los Angeles, CA, United States, ²Stevens Institute of Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, United States, ³Shanghai Jiaotong University, Shanghai, China, ⁴Neurology, UCLA, Los Angeles, CA, United States, ⁵Radiology, UCLA, Los Angeles, CA, United States

Arterial spin labeled (ASL) techniques can provide cerebral blood flow (CBF) measures without the use of a contrast agent, and it has been shown to provide largely consistent results with DSC perfusion in delineating hypoperfused brain regions in AIS while also providing information on hyperemic lesion. In this study, we develop a deep learning-based model to identify the hypoperfusion lesion on ASL images based on the DSC perfusion-defined penumbra region and diffusion weighted imaging (DWI). Our results show that deep learning can predict the DSC-defined penumbral region in ASL with dice coefficient=0.43.

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