Deep-Learning Generated Synthetic Material Decomposition Images Based on Single-Energy CT to Differentiate Intracranial Hemorrhage and Contrast Staining Within 24 Hours After Endovascular Thrombectomy.

Journal: CNS neuroscience & therapeutics
PMID: 39853936

Abstract

AIMS: To develop a transformer-based generative adversarial network (trans-GAN) that can generate synthetic material decomposition images from single-energy CT (SECT) for real-time detection of intracranial hemorrhage (ICH) after endovascular thrombectomy.

Authors

  • Tianyu Wang
    State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University; University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University.
  • Caiwen Jiang
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Weili Ding
    Zhejiang Chinese Medical University, Hangzhou, China.
  • Qing Chen
    Institute of Toxicology, Facutly of Military Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing 400038, China.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Zhongxiang Ding

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