This study introduces a novel reconstruction method for dental cone-beam computed tomography (CBCT), focusing on effectively reducing metal-induced artifacts commonly encountered in the presence of prevalent metallic implants. Despite significant progress in metal artifact reduction techniques, challenges persist owing to the intricate physical interactions between polychromatic X-ray beams and metal objects, which are further compounded by the additional effects associated with metal-tooth interactions and factors specific to the dental CBCT data environment. To overcome these limitations, we propose an implicit neural network that generates two distinct and informative tomographic images. One image represents the monochromatic attenuation distribution at a specific energy level, whereas the other captures the nonlinear beam-hardening factor resulting from the polychromatic nature of X-ray beams. In contrast to existing CT reconstruction techniques, the proposed method relies exclusively on the Beer--Lambert law, effectively preventing the generation of metal-induced artifacts during the backprojection process commonly implemented in conventional methods. Extensive experimental evaluations demonstrate that the proposed method effectively reduces metal artifacts while providing high-quality image reconstructions, thus emphasizing the significance of the second image in capturing the nonlinear beam-hardening factor.

翻译：本研究提出了一种针对牙科锥束计算机断层扫描（CBCT）的新型重建方法，重点解决由常见金属植入物引发的金属伪影问题。尽管金属伪影抑制技术已取得显著进展，但由于多色X射线束与金属物体之间复杂的物理相互作用，加之金属与牙齿间相互作用以及牙科CBCT数据环境特有因素带来的附加效应，该问题仍面临诸多挑战。为克服这些限制，我们提出了一种隐式神经网络，可生成两幅具有特异性信息的分层断层图像。其中一幅图像表征特定能量级下的单色衰减分布，另一幅则捕捉由X射线束多色性引发的非线性束硬化因子。与现有CT重建技术不同，该方法仅依赖比尔-朗伯定律，有效避免了常规方法反投影过程中易产生的金属伪影。大量实验评估表明，该方法在有效抑制金属伪影的同时，可提供高质量的重建图像，凸显了第二幅图像在捕捉非线性束硬化因子方面的重要意义。