Anatomical density guides sharper 3D dental scans from fewer X-rays
AD-GS: Anatomical Density-guided 3D Gaussian Splatting for Sparse-View Dental CBCT Reconstruction
Computer Vision and Pattern Recognition
Summary
Taking 3D X-ray images of teeth usually requires many pictures, which exposes patients to more radiation. The authors created a new method that uses knowledge about how different tissues block X-rays to improve image quality even when fewer pictures are taken. Their approach also uses smart computer vision to keep the images consistent and clear. This results in sharper dental CT images with less radiation, which can help dentists see details better.
What this means in practice
- •For dental imaging software developers: Build dental CBCT reconstruction tools that create more detailed 3D images from fewer X-ray views by targeting high-density regions.
- •For medical device manufacturers: Design dental CBCT scanners with lower radiation doses by integrating this method to maintain image quality despite sparse data.$Commercial implications: Enables development of safer dental CBCT machines with reduced radiation exposure, appealing to clinics and patients.
Authors
Zonglin Li, Kexian Xie, Feiran Li, Chu Zhou, Yixing Huang, Ruiyang Chen, Zhanyu Ma, Heng Guo
Abstract
Sparse-view Cone-Beam Computed Tomography (CBCT) reconstruction is essential for reducing radiation dose and scan effort, but remains underconstrained due to limited projection information. Recent 3D Gaussian Splatting (3DGS)-based CBCT reconstruction methods rely on gradient-driven densification, which becomes unstable under sparse acquisition and often leads to over-smoothed structures. We propose AD-GS, an Anatomical Density-guided 3D Gaussian Splatting framework that formulates Gaussian densification as an attenuation-aware Markov Chain Monte Carlo (MCMC) sampling process. By interpreting Gaussians as samples from an underlying anatomical attenuation distribution, AD-GS adaptively concentrates representation capacity in high-attenuation regions, enabling sharper structural boundaries under sparse projections. To further constrain reconstruction, we incorporate a semantic regularization based on zero-shot features from Segment Anything in Medical Images, enforcing multi-view consistency through semantic priors. Experiments on the ToothFairy3 dental CBCT dataset demonstrate that AD-GS achieves superior structural fidelity and boundary preservation compared with existing 3DGS-based approaches under sparse-view settings within the dental and maxillofacial setting. Code is available at https://github.com/yulinjiaxue/AD-GS-BMVC2026.