TY - GEN
T1 - Physical-Priors-Guided Aortic Dissection Detection Using Non-Contrast-Enhanced CT Images
AU - Ding, Zhengyao
AU - Hu, Yujian
AU - Zhang, Hongkun
AU - Wu, Fei
AU - Yang, Shifeng
AU - Du, Xiaolong
AU - Xiang, Yilang
AU - Li, Tian
AU - Chu, Xuesen
AU - Huang, Zhengxing
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/10/3
Y1 - 2024/10/3
N2 - 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.
AB - 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.
KW - Aortic dissection
KW - NCE CT
KW - Physical-priors-guided
UR - https://www.scopus.com/pages/publications/85212510774
U2 - 10.1007/978-3-031-72104-5_53
DO - 10.1007/978-3-031-72104-5_53
M3 - Conference article published in proceeding or book
AN - SCOPUS:85212510774
SN - 9783031721038
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 551
EP - 561
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings
A2 - Linguraru, Marius George
A2 - Dou, Qi
A2 - Feragen, Aasa
A2 - Giannarou, Stamatia
A2 - Glocker, Ben
A2 - Lekadir, Karim
A2 - Schnabel, Julia A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
Y2 - 6 October 2024 through 10 October 2024
ER -