Fig. 2:

Schematic diagram of our proposed image and sinogram domain joint learning framework for metal artifact reduction. Given the metal-affected sinogram S_ma and metal trace mask Tr, we use linear interpolation to acquire LI corrected sinogram S_LI. We jointly train a prior image generation network, i.e., PriorNet, to generate a good prior image X_prior and a sinogram completion network, i.e., SinoNet, to restore the metal-affected sinogram with the guidance of the prior sinogram S_prior, which is the forward projection of the prior image X_prior. The S_res is the residual sinogram map between S_LI and S_prior.

The final metal-free image is reconstructed from the corrected sinogram S_corr with the FBP algorithm.