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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