Computerized Tomography Pulmonary Angiography Image Simulation using Cycle Generative Adversarial Network from Chest CT imaging in Pulmonary Embolism Patients

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Published Nov 29, 2024
DOI: https://doi.org/10.18103/mra.v12i11.5930

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Main Article Content

Chia Hung Yang
Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan
Yi-Wei Chua
Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan
Wen-Liang Lin
Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan
Ching-Chun Huang
Department of Computer Science, College of Computer Science, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan
Chin Kuo
College of Artificial Intelligence, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan;  Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
Yun-Chien Cheng
Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan

Abstract

This study primarily focused on developing a system to generate simulated computed tomography pulmonary angiography (CTPA) images for pulmonary embolism diagnosis and aiding medical practitioners gain a more intuitive understanding of the occurrence of pulmonary embolism (PE) in diagnosis. Compared to existing methods, this system provides a non-invasive and cost-effective way to identify patients with possible pulmonary embolism. The research methodology employed the use of CycleGAN architecture to simulate CTPA images and additional implement classifier modulus to enhance ability to restore pulmonary vessel features, using computed tomography (CT) images from 22 patients and their corresponding CTPA images as training data. The experimental and simulation results provide a new approach to clinical diagnosis, which can assist physicians in the complex screening process, allowing physicians to assess whether a patient needs to undergo detailed testing for CTPA, improving the speed of detection of PE and significantly reducing the number of undetected patients.

Keywords: Deep learning, Medical Images, Pulmonary embolism, Image generation, Generative Adversarial Network, Computer tomography, Pulmonary angiography

Article Details

How to Cite
YANG, Chia Hung et al. Computerized Tomography Pulmonary Angiography Image Simulation using Cycle Generative Adversarial Network from Chest CT imaging in Pulmonary Embolism Patients. Medical Research Archives, [S.l.], v. 12, n. 11, nov. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/5930>. Date accessed: 12 dec. 2024. doi: https://doi.org/10.18103/mra.v12i11.5930.
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Issue
Vol 12 No 11 (2024): November Issue, Issue 11, VOl.12
Section
Research Articles

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References

1. Righini M, Robert‐Ebadi H, Le Gal G. Diagnosis of acute pulmonary embolism. Journal of Thrombosis and Haemostasis. 2017;15(7):1251-1261.
2. Goggs R, Benigni L, Fuentes VL, Chan DL. Pulmonary thromboembolism. Journal of Veterinary Emergency and Critical Care. 2009;19(1):30-52.
3. Stein PD, Sostman HD, Hull RD, et al. Diagnosis of pulmonary embolism in the coronary care unit. The American journal of cardiology. 2009;103(6):881-886.
4. Apfaltrer P, et al. CT Imaging of Pulmonary Embolism: Current Status. Current Cardiovascular Imaging Reports. 2011:4.6: 476-484.
5. Page P, et al. Effectiveness of managing suspected pulmonary embolism using an algorithm combining clinical probability, D-dimer testing, and computed tomography. Jama 2952 (2006). 2006:172-179.
6. Migneault D, et al. An unusual presentation of a massive pulmonary embolism with misleading investigation results treated with tenecteplase. Case reports in emergency medicine. 2015;
7. Huisman MV. Effectiveness of managing suspected pulmonary embolism using an algorithm combining clinical probability, D-dimer testing, and computed tomography. JAMA: Journal of the American Medical Association. 2006;295(2)
8. Sang J, Alam, M. S., & Xiang, H. Automated detection and classification for early stage lung cancer on CT images using deep learning. In Pattern recognition and tracking XXX2019. p. (Vol. 10995, pp. 200-207). SPIE.
9. Armanious K, et al. MedGAN: Medical image translation using GANs. Computerized Medical Imaging and Graphics 79. 2020:101684.
10. Goodfellow I, et al. Generative adversarial nets. Advances in neural information processing systems 27. 2014;
11. Tien H-J, et al. Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients. Scientific Reports 111. 2021:1-12.
12. McKenzie EM SA, Ruan D, O'Connor D, Cao M, Sheng K. Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge. Med Phys 2019. 2019:47:1094–1104.
13. Yan S, Wang, C., Chen, W., & Lyu, J. Swin transformer-based GAN for multi-modal medical image translation. Frontiers in Oncology2022. p. 12, 942511.
14. Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:151106434. 2015;
15. Zhu J-Y, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. 2017:2223-2232.
16. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Springer; 2015:234-241.
17. Hore A, Ziou D. Image quality metrics: PSNR vs. SSIM. IEEE; 2010:2366-2369.
18. Zhang R, Isola P, Efros AA, Shechtman E, Wang O. The unreasonable effectiveness of deep features as a perceptual metric. 2018:586-595.
19. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems. 2017;30