NeBLa：用于全景X射线口腔结构三维重建的神经比尔-朗伯方法
NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs
AI总结：
NeBLa提出基于比尔-朗伯定律和光线追踪的神经模型，从单张全景X射线重建三维口腔结构，无需先验信息或匹配数据集，性能优于现有方法。
摘要：
全景X射线摄影（Panoramic X-ray, PX）是一种广泛用于牙科检查的成像方式。然而，PX仅提供扁平的二维图像，缺乏口腔结构的三维视图。在本文中，我们提出NeBLa（Neural Beer-Lambert）从真实世界的PX中估计三维口腔结构。NeBLa针对不同受试者（患者）进行完整的三维重建，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）数据创建了一种称为模拟PX（SimPX）的中间表示。SimPX不仅旨在真实地模拟PX，而且有助于将数据还原回三维。我们提出了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。在推理时，真实PX图像通过语义正则化转换为SimPX风格的图像，转换后的图像由生成模块处理以产生高质量输出。实验表明，NeBLa在重建任务中定量和定性上均优于先前的最先进方法。与先前方法不同，NeBLa不需要任何先验信息，如牙弓形状，也不需要匹配的PX-CBCT数据集进行训练，而这些在临床实践中难以获得。我们的代码可在https://github.com/sihwa-park/nebla获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
