CAS-GAN for Contrast-free Angiography Synthesis

TL;DR

CAS-GAN is a novel generative framework that synthesizes angiography images without contrast agents, potentially reducing health risks in interventional procedures by disentangling vessel and background features.

Contribution

This work introduces CAS-GAN, a contrast-free angiography synthesis method using disentanglement learning and vessel semantic guidance, achieving state-of-the-art image quality.

Findings

Achieved a FID of 5.87 on XCAD dataset.

Demonstrated high visual fidelity of generated images.

Showed potential for clinical application.

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.