Panoramic radiography (Panoramic X-ray, PX) is a widely used imaging modality for dental examination. Since PX only provides 2D flattened views of the oral structure, its applicability is limited as compared to 3D Cone-beam computed tomography (CBCT). In this paper, we propose a framework to estimate CBCT-like 3D structures from real-world PX. Our framework 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 CBCT data which is based both 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/refinement modules to produce high-quality outputs. Experiments show that our method outperforms prior state-of-the-art in reconstruction tasks both quantitatively and qualitatively. Our method 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.

翻译：全景X光摄影（Panoramic X-ray，PX）是口腔检查中广泛使用的影像学手段。由于PX仅提供口腔结构的二维平面视图，其应用性相较于三维锥形束计算机断层扫描（CBCT）受到限制。本文提出一种从实际PX图像中估计类CBCT三维结构的框架。该框架针对不同受试者（患者）实现完整的三维重建，每次重建仅基于单张全景图像。我们基于X射线成像的比尔-朗伯定律与PX成像的旋转原理，从CBCT数据创建名为模拟PX（SimPX）的中间表征。SimPX不仅旨在真实模拟PX，同时便于逆向还原至三维数据。我们提出一种基于光线追踪的新型神经模型，通过利用全局与局部输入特征将SimPX转化为三维输出。在推理阶段，实际PX图像通过语义正则化转换为SimPX风格图像，再经生成/精化模块处理以产生高质量输出。实验表明，本方法在重建任务中，无论在定量还是定性评估上均优于先前最先进技术。该方法无需任何先验信息（如牙弓形态），亦无需在实际临床中难以获取的配对PX-CBCT数据集进行训练。