Supplementary Material Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction

These metals are supposed as Titanium. Detailed parameters of the acquisition geometry can be found in Table 1. This sample is 3D cone-beam data. The estimated spectrum is illustrated in Figure 1 ( Right). 2 2 Additional Details of Baselines In our experiments, we compare our proposed method against eight baseline MAR approaches. Specifically, it learns the prior distribution of metal-free CT images with a generative model in order to infer the lost sinogram in the metal-affected regions.