Semantic-Aware Synthesis Network for Dental Caries Image Generation from CBCT to Micro-CT

Dental caries is a common oral disease, and accurate imaging is vital for diagnosing, especially for assessing carious lesions and the pulp area. Cone-Beam Computed Tomography (CBCT) is a widely used imaging technology for the clinical diagnosis of caries, but its low resolution and blurred boundaries hinder clear visualization of the pulp and lesion extent. Micro Computed Tomography (Micro-CT) provides higher resolution and precision, but it’s only used for ex vivo imaging. Recently, image synthesis techniques have been extensively applied to enhance the quality of medical images. However, existing synthesis methods often focus on pixel-level correspondence, neglecting the semantic information within the images. This results in limitations in reconstructing structural edges and accurately simulating anatomical regions. To address these challenges, we propose a novel model that generates Micro-CT images from CBCT images. The proposed Semantic-Aware Synthesis Network (SASN) employs a multi-task learning strategy, integrating a segmentation task to enhance the information learning capability of the shared encoder, thereby facilitating synthesis. To achieve better semantic-based synthesis, we employ a Semantic-Guided Attention Module (SGAM) to facilitate feature fusion between branches. Additionally, we introduce a semantic alignment loss to ensure semantic consistency, thereby further enhancing network performance. Experimental results demonstrate that SASN outperforms other existing methods, achieving 27.05 (± 0.18) in PSNR, 84.41[%] (± 0.97) in SSIM, and 45.47 [×1e-3] (± 1.37) in RMSE.