A High-Fidelity Framework for 3D Dental Crown Reconstruction Using Intraoral Images

INTRODUCTION AND AIMS: This study aimed to develop a novel framework, DenGaussDiff, which employed three-dimensional (3D) Gaussian neural fields and diffusion priors for precise dental crown reconstruction from 5 intraoral images. METHODS: This study collected 1000 clinical cases with 5 intraoral images and paired intraoral scanning (IOS) models and a public dataset was employed. First, images were segmented and enhanced to improve input quality. Next, 3D basic representation of the crowns was constructed using integrated camera pose estimation and an optimised ControlNet++ model. Then, a learnable neural representation was introduced and multiscale Gaussian rendering was applied to optimise the 3D crown representation. Finally, the Iterative Closest Point (ICP) algorithm was used for registration with IOS models. RESULTS: On our dental crown dataset, DenGaussDiff outperformed other reconstruction methods, with a 1.33% increase in Peak Signal-to-Noise Ratio (PSNR), a 3.9% increase in Structural Similarity Index (SSIM) and a 4.3% reduction in Learned Perceptual Image Patch Similarity (LPIPS). The point cloud registration results demonstrated a high-precision match between the reconstructed 3D crown model and IOS model. On the public dataset, DenGaussDiff achieved excellent performance among various evaluation indicators in dental plaque, tooth absence and tooth decay. Each key component of our framework was shown to be critical for precise reconstruction in ablation study. CONCLUSION: The DenGaussDiff framework provides precise 3D dental crown reconstructions from sparse intraoral images, suggesting its potential in orthodontic diagnosis and monitoring. This further facilitates the clinical implementation of digital dentistry.