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Abstract #1288

Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke

Kai Wang1, Qinyang Shou1, Samantha Ma1, David Liebeskin2, Xin Qiao2, Jeffrey Saver2, Noriko Salamon2, Songlin Yu3, Hosung Kim1, Yannan Yu4, Yuan Xie4, Greg Zaharchuk4, Fabien Scalzo2, and Danny Wang1
1University of Southern California, Los Angeles, CA, United States, 2University of California, Los Angeles, Westwood, CA, United States, 3Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 4Stanford University, Stanford, CA, United States

A deep learning (DL)-based algorithm was developed to automatically identify the hypoperfusion lesion and penumbra in ASL images of arterial ischemic stroke (AIS) patients. A total of 167 3D pCASL datasets from 137 AIS patients on Siemens MR were used for training, using concurrently acquired DSC MRI as the label. The DL model achieved a voxel-wise area under the curve (AUC) of 0.958, and 92% accuracy for retrospective determination for subject-level endovascular treatment eligibility. The DL-model was cross validated on 12 GE pCASL data with 92% accuracy without fine-tuning of parameters.

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