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.

翻译：碘对比剂广泛应用于众多介入手术，但给患者带来重大健康风险。本文提出CAS-GAN，一种新颖的GAN框架，通过解耦表征学习和血管语义引导来合成X射线血管造影，充当"虚拟对比剂"，从而减少介入手术中对碘对比剂的依赖。具体而言，我们的方法利用医学先验知识将X射线血管造影解耦为背景和血管成分。随后，一个专用预测器学习映射这些成分间的相互关系。此外，我们引入了血管语义引导生成器及相应的损失函数，以提升生成图像的视觉保真度。在XCAD数据集上的实验结果表明，我们的CAS-GAN取得了最先进的性能，FID达到5.87，MMD达到0.016。这些令人鼓舞的结果彰显了CAS-GAN在临床应用中的潜力。