Panoramic radiography (panoramic X-ray, PX) is a widely used imaging modality for dental examination. However, its applicability is limited as compared to 3D Cone-beam computed tomography (CBCT), because PX only provides 2D flattened images of the oral structure. In this paper, we propose a new framework which estimates 3D oral structure from real-world PX images. Since there are not many matching PX and CBCT data, we used simulated PX from CBCT for training, however, we used real-world panoramic radiographs at the inference time. We propose a new ray-sampling method to make simulated panoramic radiographs inspired by the principle of panoramic radiography along with the rendering function derived from the Beer-Lambert law. Our model consists of three parts: translation module, generation module, and refinement module. The translation module changes the real-world panoramic radiograph to the simulated training image style. The generation module makes the 3D structure from the input image without any prior information such as a dental arch. Our ray-based generation approach makes it possible to reverse the process of generating PX from oral structure in order to reconstruct CBCT data. Lastly, the refinement module enhances the quality of the 3D output. Results show that our approach works better for simulated and real-world images compared to other state-of-the-art methods.

本文提出了一种新的框架，通过射线采样和渲染函数，将仿真的牙科摄影机图像转化为真实的口腔成像，利用相应的训练数据，生成口腔的三维结构，最终完成CBCT数据的重建，结果表明，与其他先进技术相比，该方法效果更佳。

NeBLa：基于神经系统的Beer-Lambert法进行口腔结构在全景放射状图像中的三维重建