CAS-GAN for Contrast-free Angiography Synthesis

2024/10/11 by De-Xing Huang, Huang, De-Xing, Xiao-Hu Zhou +15 · 1 citation
Engineering · #Photoacoustic and Ultrasonic Imaging

paper · pdf · doi:10.48550/arxiv.2410.08490

Abstract

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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• CAS-IQA: Teaching Vision-Language Models for Synthetic Angiography Quality Assessment

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