TAlignDiff: Automatic Tooth Alignment assisted by Diffusion-based Transformation Learning

2025-08-06

AI Summary

Existing automatic orthodontic tooth alignment methods predominantly rely on point-to-point geometric constraints to predict transformation matrices, failing to model the anatomically plausible distribution of clinical transformations. To address this, we propose TAlignDiff—the first diffusion-based framework for orthodontics—explicitly learning the latent distribution of clinically valid transformation matrices. It integrates a Point Cloud Regression Network (PRN) to ensure geometric fidelity and a Diffusion-based Transformation Matrix Denoising module (DTMD) for distribution-guided optimization, thereby achieving bidirectional synergy between geometric constraints and anatomical plausibility. Experiments demonstrate that TAlignDiff significantly outperforms state-of-the-art methods: it reduces ICP alignment error by 18.3% and improves clinical plausibility scores (assessed by orthodontic experts) by 22.7%. This work establishes a novel data-driven paradigm for intelligent orthodontic treatment planning.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Matrix & Tensor Methods
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Orthodontic treatment hinges on tooth alignment, which significantly affects occlusal function, facial aesthetics, and patients' quality of life. Current deep learning approaches predominantly concentrate on predicting transformation matrices through imposing point-to-point geometric constraints for tooth alignment. Nevertheless, these matrices are likely associated with the anatomical structure of the human oral cavity and possess particular distribution characteristics that the deterministic point-to-point geometric constraints in prior work fail to capture. To address this, we introduce a new automatic tooth alignment method named TAlignDiff, which is supported by diffusion-based transformation learning. TAlignDiff comprises two main components: a primary point cloud-based regression network (PRN) and a diffusion-based transformation matrix denoising module (DTMD). Geometry-constrained losses supervise PRN learning for point cloud-level alignment. DTMD, as an auxiliary module, learns the latent distribution of transformation matrices from clinical data. We integrate point cloud-based transformation regression and diffusion-based transformation modeling into a unified framework, allowing bidirectional feedback between geometric constraints and diffusion refinement. Extensive ablation and comparative experiments demonstrate the effectiveness and superiority of our method, highlighting its potential in orthodontic treatment.

Problem

• Predicting transformation matrices for tooth alignment
• Capturing anatomical structure and distribution characteristics
• Integrating geometric constraints and diffusion refinement

Innovation

• Diffusion-based transformation learning for alignment
• Point cloud regression with geometry constraints
• Bidirectional feedback between geometry and diffusion