CAS-GAN for Contrast-free Angiography Synthesis
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916521469542400 |
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| author | Huang, De-Xing Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Li, Hao Xiang, Tian-Yu Hou, Zeng-Guang |
| author_facet | Huang, De-Xing Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Li, Hao Xiang, Tian-Yu Hou, Zeng-Guang |
| contents | Iodinated contrast agents are widely utilized in numerous interventional procedures, yet posing substantial health risks to patients. This paper presents CAS-GAN, a novel GAN framework that serves as a "virtual contrast agent" to synthesize X-ray angiographies via disentanglement representation learning and vessel semantic guidance, thereby reducing the reliance on iodinated contrast agents during interventional procedures. Specifically, our approach disentangles X-ray angiographies into background and vessel components, leveraging medical prior knowledge. A specialized predictor then learns to map the interrelationships between these components. Additionally, a vessel semantic-guided generator and a corresponding loss function are introduced to enhance the visual fidelity of generated images. Experimental results on the XCAD dataset demonstrate the state-of-the-art performance of our CAS-GAN, achieving a FID of 5.87 and a MMD of 0.016. These promising results highlight CAS-GAN's potential for clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08490 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CAS-GAN for Contrast-free Angiography Synthesis Huang, De-Xing Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Li, Hao Xiang, Tian-Yu Hou, Zeng-Guang Image and Video Processing Computer Vision and Pattern Recognition Iodinated contrast agents are widely utilized in numerous interventional procedures, yet posing substantial health risks to patients. This paper presents CAS-GAN, a novel GAN framework that serves as a "virtual contrast agent" to synthesize X-ray angiographies via disentanglement representation learning and vessel semantic guidance, thereby reducing the reliance on iodinated contrast agents during interventional procedures. Specifically, our approach disentangles X-ray angiographies into background and vessel components, leveraging medical prior knowledge. A specialized predictor then learns to map the interrelationships between these components. Additionally, a vessel semantic-guided generator and a corresponding loss function are introduced to enhance the visual fidelity of generated images. Experimental results on the XCAD dataset demonstrate the state-of-the-art performance of our CAS-GAN, achieving a FID of 5.87 and a MMD of 0.016. These promising results highlight CAS-GAN's potential for clinical applications. |
| title | CAS-GAN for Contrast-free Angiography Synthesis |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.08490 |