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.

本研究提出了一种新颖的牙科锥形束计算机断层扫描重建方法，旨在有效减少常见金属植入物引起的伪影。通过使用隐式神经网络生成两张不同的信息性层面的断层图像，一张图像代表特定能量水平上的单色衰减分布，而另一张图像则捕捉了由多色 X 射线束的非线性光束硬化因素引起的影响。与现有的计算机断层扫描重建方法相比，该方法仅依赖于比尔 - 朗伯定律，在背投影过程中有效地防止了金属引起的伪影的产生。广泛的实验评估表明，该方法有效地减少了金属伪影同时提供了高质量的图像重建结果，凸显了第二张图像在捕捉非线性光束硬化因素方面的重要性。