d-MAR: Deep Metal Artifact Reduction via Diffusion-Driven Domain Transformations

Metal implants introduce severe artifacts in CT images, compromising diagnostic reliability. Supervised metal artifact reduction (MAR) models trained on simulated data are effective but often fail due to domain gaps when applied to real clinical data. Unsupervised methods trained on real images avoid such gaps but suffer from weak artifact suppression and training instability. To address these challenges, we propose d-MAR, a novel MAR framework that performs diffusion-driven domain transformations between simulated and real image domains. Specifically, real image domain (RID) data is transformed into the simulated image domain (SID), processed by a MAR model trained on simulation-paired data, and transformed back into RID. We harness diffusion models as a transformation bridge and introduce two targeted conditional sampling techniques-conditional input and sampling enhancement-based on Fourier-extracted low-frequency image components. This enables domain alignment without random generation, ensuring consistent anatomical fidelity. The proposed d-MAR can reduce real metal artifacts originating from different scanning protocols and devices with a MAR model trained with simulated paired data. Evaluations on Clinical Head, Clinical Body, and dental CBCT datasets show that d-MAR consistently outperforms conventional MAR methods in both quantitative metrics and visual quality, demonstrating strong generalization capability.