Neural Representation-Based Method for Metal-induced Artifact Reduction
  in Dental CBCT Imaging
Hyoung Suk Park Kiwan Jeon Jin Keun Seo
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.