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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