A strategy for simulation‐driven CT metal artifact reduction toward improving network generalizability

We proposed a self-supervised metal artifact reduction framework that combines MLP-based beam-hardening correction with a conditional latent diffusion model in the imaging physics framework. The MLP module provides physics motivated beam-hardening corrected CT images, while the residual artifact simulation strategy enables fully self-supervised training without the need for paired data. The proposed method demonstrated superior artifact suppression and structural preservation on both synthetic and clinical datasets, outperforming existing approaches.