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作者

Zhengyao Ding Yujian Hu Hongkun Zhang Fei Wu Shifeng Yang Xiaolong Du Yilang Xiang Tian Li Xuesen Chu & Zhengxing Huang

作者单位

²The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China ³The Hong Kong Polytechnic University, Kowloon, Hong Kong ¹¹Universitat de Barcelona, Barcelona, Spain ⁷Children’s National Hospital/George Washington University, Washington, DC, USA ¹⁰Imperial College London, London, UK ⁵Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China ⁴Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China ¹Zhejiang University, Hangzhou, China ¹²Helmholtz Munich, Technical University of Munich and King’s College London, Munich, Germany ⁸The Chinese University of Hong Kong, Hong Kong, China ⁶China Ship Scientific Research Center, Wuxi, China ⁹Technical University of Denmark, Kgs Lyngby, Denmark

会议名称

Medical Image Computing and Computer Assisted Intervention – MICCAI 2024

召开年

2024

摘要

Aortic dissection is a severe cardiovascular emergency requiring prompt and precise diagnosis for better survival chances. Given the limited use of Contrast-Enhanced Computed Tomography in routine clinical screenings, this study presents a new method that enhances the diagnostic process using Non-Contrast-Enhanced CT images. In detail, we integrate biomechanical and hemodynamic physical priors into a 3D U-Net model and utilize a transformer encoder to extract superior global features, along w...更多

Aortic dissection is a severe cardiovascular emergency requiring prompt and precise diagnosis for better survival chances. Given the limited use of Contrast-Enhanced Computed Tomography in routine clinical screenings, this study presents a new method that enhances the diagnostic process using Non-Contrast-Enhanced CT images. In detail, we integrate biomechanical and hemodynamic physical priors into a 3D U-Net model and utilize a transformer encoder to extract superior global features, along with a cGAN-inspired discriminator for the generation of realistic CE-CT-like images. The proposed model not only innovates AD detection on NCE-CT but also provides a safer alternative for patients contraindicated for contrast agents. Comparative evaluations and ablation studies against existing methods demonstrate the superiority of our model in terms of recall, AUC, and F1 score metrics standing at 0.882, 0.855, and 0.829, respectively. Incorporating physical priors into diagnostics offers a significant, nuanced, and non-invasive advancement, seamlessly integrating medical imaging with the dynamic aspects of human physiology. Our code is available at https://github.com/Yukui-1999/PIAD.收起

文献类型

会议论文