公开(公告)号：US20220148301A1

公开(公告)日：2022-05-12

申请号：US17606751

申请日：2020-09-29

申请人： West China Hospital of Sichuan University ,  University of Electronic science and Technology of China

发明人： Na Hu ,  Su Lv ,  Shi Gu ,  Tianwei Zhang

IPC分类号： G06V10/82 ,  G06T3/40 ,  G06N3/04

摘要： This invention discloses an auxiliary diagnostic model and an image processing method for detecting acute ischemic stroke. This refers to the technical field of medical image processing. The technical essentials are described as follow: the presented deep-learning model is based on generative adversarial networks (GANs), comprising a generator (G) and a discriminator (D). G is the first three-dimensional convolutional neural network, used to synthesize realistic images from raw data, while D is the second three-dimensional convolutional neural network, used to classify images as real or fake (synthetic). The presented GAN model can learn the mapping relationship from non-enhanced computed tomography (NECT) images to T2-weighted fluid-attenuation inversion recovery (FLAIR) magnetic resonance imaging (MRI), then converting the raw CT to synthetic FLAIR with high sensitivity. This improves the efficiency of emergency scanning in acute ischemic stroke, reaching sensitivity that is poor in CT interpretation and immediacy that is limited in MRI examination.