Abstract
Aortic dissection (AD) is a severe cardiovascular emergency requiring prompt and precise diagnosis for better survival chances. Given the limited use of Contrast-Enhanced Computed Tomography (CE-CT) in routine clinical screenings, this study presents a new method that enhances the diagnostic process using Non-Contrast-Enhanced CT (NCE-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.
First Author and Second Author contribute equally to this work.
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Acknowledgments
This work was supported by the Technical Innovation key project of Zhejiang Province (2024C03023) to H.Z, the National Key Research and Development Program of China (Grant No. 2022YFF1202400), and the National Nature Science Foundation of China (Grant No. 82272129).
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Ding, Z. et al. (2024). Physical-Priors-Guided Aortic Dissection Detection Using Non-Contrast-Enhanced CT Images. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15007. Springer, Cham. https://doi.org/10.1007/978-3-031-72104-5_53
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