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Abstract #1648

Deep learning based Ischemic core prediction from perfusion-weighted imaging in acute ischemic stroke

Yannan Yu1, Yuan Xie1, Thoralf Thamm1, Enhao Gong1, Jiahong Ouyang1, Soren Christensen1, Michael P Marks1, Maarten G Lansberg1, Gregory W Albers1, and Greg Zaharchuk1
1Stanford University, Stanford, CA, United States

Ischemic core of acute ischemic stroke is commonly defined by diffusion-weighted imaging (DWI). CT perfusion, although widely used for acute stroke triaging, is challenging to identify the ischemic core as precise as DWI. In this study, we predicted the DWI lesion from MR perfusion-weighted imaging using U-Net. We found U-net model can predict the ischemic core from perfusion imaging with a better performance compared to clinically-used relative cerebral blood flow map thresholding. In the future study, we will apply the model to patients underwent CT perfusion using transfer learning.

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