Metal Inpainting in CBCT Projections Using Score-based Generative Model

During the orthopaedic surgery, the insertion of metallic implants or screws is often performed under mobile C-arm systems. However, due to the high attenuation of metals, severe metal artifacts occur in 3D reconstructions, which degrade the image quality significantly. Therefore, many metal artifact reduction algorithms have been developed to reduce the artifacts, and metal inpainting in the projection domain is an essential step. In this work, a score-based generative model is trained on simulated knee projections, and the in-painted images are obtained by removing the noise in the conditional resampling process. The result implies that the inpainted images by the score-based generative model have more detailed information and achieve the lowest mean absolute error of 0.069 and the highest peak-signal-to-noise ratio of 43.07 compared with the inverse distance weighting interpolation method and the mask pyramid network. Besides, the score-based model can also recover projections with large circular and rectangular masks, showing its generalization in in-painting tasks.