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Objective: Hemorrhagic transformation (HT) is a major complication of reperfusion therapy in acute stroke. We aim to explore the feasibility of HT location prediction from baseline magnetic resonance perfusion (MRP) source images.Methods: Consecutive acute ischemic stroke patients who had HT after reperfusion therapy (IV tPA and/or endovascular thrombectomy) were reviewed from two stroke centers. Patients with MRP and diffusion-weighted imaging (DWI) before and 24h after treatment and follow-up susceptibility weighted imaging (SWI) were included. The HT was depicted on follow-up SWI semi-automatically. The DWI lesions and HT were classified into 12 cerebral vascular territories (Fig 1A). Then a total of 80,000 tissue-voxels were extracted from both HT region and non-HT region randomly with the ratio of 1:1. Each voxel included a set of values of MRP source image and baseline DWI. Convolutional neural networks (CNN) were trained with 3-fold cross-validation. Prediction maps were generated from the CNN for each patient. The prevalence of HT after infarction and the sensitivity and specificity of HT prediction in each vascular territory were calculated.Results: Seventy-seven HT patients were analyzed (40 male, age 73 ±14 years, median baseline NIHSS score 14 [IQR 8-19], median onset-to-treatment time 242 min [IQR 152-355], median modified Rankin score 3 [IQR 1-4], 28 with Parenchymal hematoma). HT occurred most frequently in the territory of middle cerebral artery superficial branches (67.5%) and basal ganglia (37.6%), and least frequently in brain stem or thalamus (3.9%). HT was not evenly distributed throughout the vascular territories (Fig 1A). The best model was a locally-connected CNN (area under curve of 0.88). The average sensitivity and specificity for HT location prediction in anterior circulation were 89% and 60% (Fig 1BC).Conclusion: The CNN trained from DWI and MRP source images could predict the HT locations in acute ischemic stroke patients.