Exploratory thyroid ultrasound–computed tomography synthesis using anatomy-preserving contrastive unpaired translation

OBJECTIVES: When ultrasound (US) and computed tomography (CT) are both clinically indicated, unpaired image translation may support model development but cannot replace either examination. We evaluated Anatomy-Preserving Contrastive Unpaired Translation (AP-CUT), a CUT-based framework with a CT-source Sobel edge-consistency term for CT → US translation. METHODS: The study used 832 malignant thyroid US images from TN5000 and 800 cervical CT slices from Head-Neck-CHUM. CycleGAN, CUT, and AP-CUT were compared with a low-dimensional Fréchet proxy and nearest-neighbor SSIM/PSNR. Bounding boxes were used only to prepare local US regions for qualitative US → CT examples. A separate single-run experiment treated 600 CT → US outputs as malignant-class training examples and evaluated three classifiers. RESULTS: AP-CUT had the highest CT → US nearest-neighbor SSIM (0.1388), compared with 0.0982 for CUT and 0.1247 for CycleGAN. CycleGAN had the lowest US → CT proxy FID (10.4; lower is better). In the classification experiment, the absolute AUC change was +0.0002 for ResNet-50, +0.0261 for EfficientNet-B4 (0.7297 to 0.7558), and -0.0109 for EfficientNet-B4 + CBAM. CONCLUSIONS: The CT-source regularizer improved one relative structural proxy over CUT, but absolute structural scores remained low. US → CT outputs are local CT-domain-styled patches rather than reconstructed axial CT images, and the mixed single-run classification results do not establish clinical utility.