MGMAR: Metal-Guided Metal Artifact Reduction for X-ray Computed Tomography

Abstract

An X-ray computed tomography (CT), metal artifact reduction (MAR) remains a major challenge because metallic implants violate standard CT forward-model assumptions, producing severe streaking and shadowing artifacts that degrade diagnostic quality. We propose MGMAR, a metal-guided MAR method that explicitly leverages metal-related information throughout the reconstruction pipeline. MGMAR first generates a high-quality prior image by training a conditioned implicit neural representation (INR) using metal-unaffected projections, and then incorporates this prior into a normalized MAR (NMAR) framework for projection completion. To improve robustness under severe metal corruption, we pretrain the encoder-conditioned INR on paired metal-corrupted and artifact-free CT images, thereby embedding data-driven prior knowledge into the INR parameter space. This prior-embedded initialization reduces sensitivity to random initialization and accelerates convergence during measurement-specific refinement. The encoder takes a metal-corrupted reconstruction together with a recursively constructed metal artifact image, enabling the latent field to capture metal-dependent global artifact patterns. After projection completion using the INR prior, we further suppress residual artifacts using a metal-conditioned correction network, where the metal mask modulates intermediate features via adaptive instance normalization to target metal-dependent secondary artifacts while preserving anatomical structures. Experiments on the public AAPM-MAR benchmark demonstrate that MGMAR achieves state-of-the-art performance, attaining an average final score of 0.89 on 29 clinical test cases.

Figures (8)

• Figure 1: Schematic diagram of the MGMAR framework, which exploits metal-related information throughout the pipeline via a metal artifact-aware latent-conditioned INR for prior image generation and a metal-embedded residual learning network.
• Figure 2: Prior images obtained by an INR with different random initializations. Given the projection data $P$ from a validation case (B02 #916), the INR is optimized by minimizing the naive loss $\mathcal{L}_{\text{\tiny naive}}$ for $10{,}000$ iterations (WW/WL $=1500/-250$ HU for CT images). Although the final loss values are similar, the resulting reconstructions differ substantially.
• Figure 3: Constructed metal-artifact images $\mu_{\text{\tiny MA}}^{(k)}$ across recursion steps ($k=0,1,2$). The RMSE with respect to the target $\mu_{\text{\tiny MA}}^{*}$ is reported at the top of each panel (WW/WL $=2000/-500$).
• Figure 4: Comparison of prior images obtained by INRs initialized with random, meta, and the proposed data-driven weights across the number of iterations $N_{\text{\tiny iter}}$ (WW/WL = 1500/-250 HU for CT images).
• Figure 5: Visual comparison of prior, NMAR-corrected, and residual-corrected images on a validation case (B04 #948) (WW/WL = 300/0 HU for CT images, WW/WL = 50/0 HU for difference images).