用AI生成血管影像，减少患者对造影剂依赖
CAS-GAN for Contrast-free Angiography Synthesis
- 分离背景与血管成分，用语义引导生成更真实图像
- 在XCAD数据集上FID达5.87，生成质量优于现有方法
- 适合介入手术辅助、降低造影剂风险的临床场景
碘化造影剂广泛用于多种介入手术，但存在显著健康风险。本文提出CAS-GAN，一种新型GAN框架，通过解耦表征学习与血管语义引导，将X射线血管造影图分解为背景和血管成分，实现无造影剂的虚拟造影。该方法利用医学先验知识分离图像结构，并设计专用预测器建模各成分间关系。同时引入血管语义引导生成器及对应损失函数，提升生成图像视觉保真度。在XCAD数据集上的实验表明，CAS-GAN达到5.87的FID和0.016的MMD，性能处于领先水平，展现出良好的临床应用潜力。
原文摘要 · Abstract (English)
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.
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