Panoramic radiography (Panoramic X-ray, PX) is a widely used imaging modality for dental examination. However, PX only provides a flattened 2D image, lacking in a 3D view of the oral structure. In this paper, we propose NeBLa (Neural Beer-Lambert) to estimate 3D oral structures from real-world PX. NeBLa tackles full 3D reconstruction for varying subjects (patients) where each reconstruction is based only on a single panoramic image. We create an intermediate representation called simulated PX (SimPX) from 3D Cone-beam computed tomography (CBCT) data based on the Beer-Lambert law of X-ray rendering and rotational principles of PX imaging. SimPX aims at not only truthfully simulating PX, but also facilitates the reverting process back to 3D data. We propose a novel neural model based on ray tracing which exploits both global and local input features to convert SimPX to 3D output. At inference, a real PX image is translated to a SimPX-style image with semantic regularization, and the translated image is processed by generation module to produce high-quality outputs. Experiments show that NeBLa outperforms prior state-of-the-art in reconstruction tasks both quantitatively and qualitatively. Unlike prior methods, NeBLa does not require any prior information such as the shape of dental arches, nor the matched PX-CBCT dataset for training, which is difficult to obtain in clinical practice. Our code is available at https://github.com/sihwa-park/nebla.

翻译：全景X光摄影（Panoramic X-ray, PX）是牙科检查中广泛使用的成像方式。然而，PX仅提供扁平的二维图像，缺乏口腔结构的三维视图。本文提出NeBLa（神经比尔-朗伯）方法，从真实世界PX图像中估计三维口腔结构。NeBLa针对不同受试者（患者）实现完整三维重建，每次重建仅基于单张全景图像。我们基于X射线渲染的比尔-朗伯定律和PX成像的旋转原理，从三维锥形束计算机断层扫描（CBCT）数据创建称为模拟PX（SimPX）的中间表示。SimPX不仅旨在真实模拟PX，还便于逆向还原为三维数据。我们提出一种基于光线追踪的新型神经模型，利用全局和局部输入特征将SimPX转换为三维输出。在推理阶段，通过语义正则化将真实PX图像转换为SimPX风格图像，并由生成模块处理以产生高质量输出。实验表明，NeBLa在定量和定性重建任务中均优于先前最先进方法。与先前方法不同，NeBLa无需任何先验信息（如牙弓形状），也无需临床实践中难以获取的匹配PX-CBCT数据集进行训练。我们的代码已开源在https://github.com/sihwa-park/nebla。