ArticleBMC medical informatics and decision making2026
CycleGAN-based image-to-image translation for synthetic contrast enhancement in non-contrast cardiac CT: a ViT-CNN hybrid deep learning approach.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundContrast-enhanced computed tomography (CECT) is essential for visualizing cardiac structures and stenosis; however, it is unsuitable for patients with contrast agent allergies. To address this, we propose RICAUNet-v2, a ViT-CNN hybrid architecture designed for CycleGAN-based similarity-guided unpaired NCCT-to-CECT translation.
methodsRICAUNet-v2 integrates RICA blocks and Vision Transformer (ViT) layers to capture local and global features. We compared our architecture against UNet, RICAU-Net, MaskGAN, and a supervised pix2pix-RICAUNet-v2 using metrics including Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Fréchet Inception Distance (FID). Statistical significance was assessed using the Wilcoxon signed-rank test, and a blinded reader study conducted by two clinicians evaluated perceptual realism.
resultsCycleGAN-RICAUNet-v2 achieved the lowest FID score (0.0639). Although pix2pix-RICAUNet-v2 attained the best MAE (92.72) and SSIM (0.5380), and MaskGAN achieved the highest PSNR (13.50), the superior FID score of CycleGAN-RICAUNet-v2 indicates better perceptual realism for unpaired translation. Ablation studies confirmed that using three-channel windowed inputs and similarity-guided sampling reduced the FID compared to single-channel input and unpaired cross-patient sampling (FID of 0.0802). Clinical evaluation demonstrated 100% precision and 15% recall, with a Cohen’s kappa of 0.40, suggesting that most misclassifications of real images as synthetic may be due to conservative or expectation bias rather than a lack of realism.
conclusionsRICAUNet-v2 improves perceptual realism in similarity-guided unpaired NCCT-to-CECT translation by integrating ViT and RICA blocks. Although further clinically relevant validation of diagnostic utility is required, the proposed architecture demonstrates potential for contrast-free cardiac CT enhancement.
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