Tooth alignment by diffusion model with hierarchical spatial relationship learning

Tooth alignment is crucial for oral health and aesthetics, requiring dentists to consider complex spatial relationships among teeth, including local, regional, and global spatial relationships. Manual planning is time-consuming and highly dependent on clinician experience. Therefore, researchers have developed AI techniques for tooth alignment. However, while AI techniques improve planning efficiency, their performance in tooth alignment applications is usually limited. They typically model local tooth neighborhoods and the global dental arch, neglecting the hierarchical multiscale spatial constraints among teeth across ever-expanding regions. To address this issue and effectively use clinical priors, we propose a tooth alignment framework that incorporates a Prior-guided Hierarchical Window Attention (PHWA) module and a Continuous Guidance Diffusion Model (CGDM) to enhance alignment accuracy and efficiency. Specifically, the PHWA module utilizes multilevel window divisions and skip connections to progressively model spatial relationships from local tooth environments to global arch structures, capturing both fine-grained details and comprehensive spatial dependencies of teeth in alignment planning and improving planning accuracy. Furthermore, we apply an improved diffusion model to predict orthodontic transformation matrices. The CGDM module leverages historical estimation information as prior guidance to accelerate convergence toward high-quality alignment schemes. This approach reduces sampling steps while improving the quality of generated samples. Experimental results demonstrate that our method outperforms six state-of-the-art approaches, achieving a superior alignment accuracy of 1.513 mm in ADD and enhanced spatial modeling. Our framework thus presents a promising solution for orthodontic treatment planning.