Generation of Cardiac CT Images with and Without Contrast Using a Cycle-Consistent Adversarial Networks with Diffusion

Victor Rogerio Sousa Ferreira, Anselmo Cardoso de Paiva, João Dallyson Sousa de Almeida, Geraldo Braz Junior, Aristofanes Correa Silva, Francesco Renna

Abstract: This paper explores a Cycle-GAN architecture based on diffusion models for translating cardiac CT images with and without contrast, aiming to enhance the quality and accuracy of medical imaging. The combination of GANs and diffusion models has demonstrated promising results, particularly in generating high-quality, visually similar contrast-enhanced cardiac images. This effectiveness is evidenced by metrics such as a PSNR of 32.85, an SSIM of 0.766, and an FID of 42.348, highlighting the model’s capability for accurate and detailed image generation. Although these results indicate substantial potential for improving diagnostic accuracy, challenges remain, particularly concerning the generation of image artefacts and brightness inconsistencies, which could affect the clinical validation of these images. These issues have important implications for the reliability of the images in real medical diagnoses. The results of this study suggest that future research should focus on optimizing these aspects, improving the handling of artefacts, and investigating alternative architectures further to enhance the quality and reliability of the generated images, ensuring their applicability in clinical settings

External IDs:doi:10.1007/978-3-032-08570-2_12