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Abstract #0599

CT-to-MR image synthesis: A generative adversarial network-based method for detecting hypoattenuating lesions in acute ischemic stroke

Na Hu1, Tianwei Zhang2, Yifan Wu3, Biqiu Tang1, Minlong Li1, Qiyong Gong1, Shi Gu2, and Su Lui1
1Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, 2Department of Computer and Engineering, University of Electronic Science and Technology of China, Chengdu, China, 3Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States

We aimed to develop a method of CT-to-MR image synthesis to assist in detecting hypoattenuating brain lesions in acute ischemic stroke. Emergency head CT images of 193 patients with suspected stroke and follow-up MR images were collected. A generative-adversarial-network model was developed for CT-to-MR image synthesis. With synthetic MRI compared to CT, sensitivity was improved by 116% in patient detection and 300% in lesion detection, and extra 75% of patients and 15% of lesions missed on CT were detected on synthetic MRI. Our method could be a rapid tool to improve readers’ detection of hypoattenuating lesions in AIS.

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