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This study introduces a novel reconstruction method for dental cone-beamcomputed tomography (CBCT), focusing on effectively reducing metal-inducedartifacts commonly encountered in the presence of prevalent metallic implants.Despite significant progress in metal artifact reduction techniques, challengespersist owing to the intricate physical interactions between polychromaticX-ray beams and metal objects, which are further compounded by the additionaleffects associated with metal-tooth interactions and factors specific to thedental CBCT data environment. To overcome these limitations, we propose animplicit neural network that generates two distinct and informative tomographicimages. One image represents the monochromatic attenuation distribution at aspecific energy level, whereas the other captures the nonlinear beam-hardeningfactor resulting from the polychromatic nature of X-ray beams. In contrast toexisting CT reconstruction techniques, the proposed method relies exclusivelyon the Beer--Lambert law, effectively preventing the generation ofmetal-induced artifacts during the backprojection process commonly implementedin conventional methods. Extensive experimental evaluations demonstrate thatthe proposed method effectively reduces metal artifacts while providinghigh-quality image reconstructions, thus emphasizing the significance of thesecond image in capturing the nonlinear beam-hardening factor.

Neural Representation-Based Method for Metal-induced Artifact Reduction in Dental CBCT Imaging