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

BACKGROUND: We address computed tomography (CT) metal artifacts reduction (MAR) using a generative deep-learning model in the imaging physics framework. Existing deep learning-based MAR methods, though promising, generally lack explicit physical modeling of artifact formation and rely heavily on data-driven mappings. The absence of physics priors not only limits scalability, as they often require paired or task-specific datasets, but also makes such methods prone to hallucination, anatomical distortion, and unstable artifact suppression. PURPOSE: We propose a novel self-supervised framework for CT MAR, integrating a lightweight multi-layer perceptron (MLP)-based beam-hardening correction with a conditional latent diffusion model (LDM). By incorporating a physics-informed correction step and an artifact-reproducing simulation technique, the framework aims to enhance scalability across diverse scenarios, reduce hallucination effects, and improve structural fidelity in the reconstructed images. METHODS: The proposed MLP performs physics-driven polynomial correction, serving as a simplified but efficient alternative to existing approaches. Also, the proposed MLP implicitly incorporates sinogram consistency into its optimization objective, allowing case-specific adaptation and convergence toward the desired solution. Additionally, the learned MLP parameters are reused to simulate artifact-contaminated images from artifact-free scans, generating pseudo paired data for self-supervised training without requiring real paired datasets. A conditional LDM is trained on these synthetic pairs to remove residual artifacts. RESULTS: By operating in a low-dimensional latent space, the LDM significantly reduces inference time while maintaining high-quality reconstructions. The proposed method is evaluated on both the SynDeepLesion dataset and real clinical data, demonstrating superior artifact removal and structural preservation compared to the existing state-of-the-art MAR techniques. We particularly highlight the robustness, generalizability, and clinical applicability of the proposed framework. CONCLUSION: 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.