Tooth-Diffusion: Guided 3D CBCT Synthesis with Fine-Grained Tooth Conditioning

TL;DR

This paper introduces a novel 3D diffusion model guided by tooth-level attributes for realistic, controllable dental CBCT synthesis, enabling precise modifications for clinical and research applications.

Contribution

It presents a new conditional diffusion framework with tooth-level control, wavelet denoising, and masked loss for high-fidelity, localized dental image synthesis.

Findings

Achieves low FID scores indicating high realism

Maintains SSIM above 0.91 on unseen data

Demonstrates effective tooth addition, removal, and full dentition synthesis

Abstract

Despite the growing importance of dental CBCT scans for diagnosis and treatment planning, generating anatomically realistic scans with fine-grained control remains a challenge in medical image synthesis. In this work, we propose a novel conditional diffusion framework for 3D dental volume generation, guided by tooth-level binary attributes that allow precise control over tooth presence and configuration. Our approach integrates wavelet-based denoising diffusion, FiLM conditioning, and masked loss functions to focus learning on relevant anatomical structures. We evaluate the model across diverse tasks, such as tooth addition, removal, and full dentition synthesis, using both paired and distributional similarity metrics. Results show strong fidelity and generalization with low FID scores, robust inpainting performance, and SSIM values above 0.91 even on unseen scans. By enabling realistic, localized modification of dentition without rescanning, this work opens opportunities for surgical planning, patient communication, and targeted data augmentation in dental AI workflows. The codes are available at: https://github.com/djafar1/tooth-diffusion.