GANs-guided Conditional Diffusion Model for Synthesizing Contrast-enhanced Computed Tomography Images.

In contrast to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans can highlight discrepancies between abnormal and normal areas, commonly used in clinical diagnosis of focal liver lesions. However, the use of contrast agents in CE-CT scans imposes significant physical and economic burdens on patients in clinical practice. Recently, Generative Adversarial Networks (GANs)-based synthesis models offer an alternative approach that obtains CE-CT images from NC-CT images. However, poor coverage and mode collapse greatly limit their performance. Diffusion models (DMs)-based methods have demonstrated superior performance in natural image synthesis tasks. Nevertheless, our experiment shows that CE-CT images synthesized from DMs-based method exhibit higher overall quality but lower local quality. The quality of local areas, particularly those related to lesion areas, is crucial in medical image synthesis tasks. Hence, we propose a GANs-guided conditional diffusion model (GANs-CDM), combining the GANs and conditional diffusion model (CDM), to generate CECT images. In the proposed GANs-CDM, the GANs is to generate a preliminary CE-CT image, serving as conditional input for guiding the subsequent CDM to produce refined CE-CT images. Qualitative and quantitative evaluation on arterial and portal venous phase synthesis tasks demonstrates that our proposed GANs-CDM can significantly improve both the local and global quality of synthetic images.