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Background <p>Contrast-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.</p> Methods <p>RICAUNet-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.</p> Results <p>CycleGAN-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.</p> Conclusions <p>RICAUNet-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.</p> Trial registration <p>Not applicable.</p>

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CycleGAN-based image-to-image translation for synthetic contrast enhancement in non-contrast cardiac CT: a ViT-CNN hybrid deep learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Doyoung Park,
  • Jinsoo Kim,
  • Xucong Ruan,
  • Weien Chow,
  • Lohendran Baskaran

摘要

Background

Contrast-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.

Methods

RICAUNet-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.

Results

CycleGAN-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.

Conclusions

RICAUNet-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.

Trial registration

Not applicable.