Diffusion model‐regularized implicit neural representation for computed tomography metal artifact reduction

Abstract Computed tomography (CT) images are often severely corrupted by artifacts in the presence of metals. Existing supervised metal artifact reduction (MAR) approaches suffer from performance instability on known data due to their reliance on limited paired metal‐clean data, which limits their clinical applicability. Moreover, existing unsupervised methods face two main challenges: (1) The CT physical geometry is not effectively incorporated into the MAR process to ensure data fidelity; and (2) traditional heuristic regularization terms cannot fully capture the abundant prior knowledge. To overcome these shortcomings, we propose a diffusion model (DM)‐regularized implicit neural representation (INR) framework for CT image MAR. The INR integrates physical constraints and imposes data fidelity, whereas the pretrained DM provides prior knowledge to regularize the solution. Experimental results on both simulated and clinical data demonstrate the effectiveness and generalization ability of our method, highlighting its potential to be applied in clinical settings.