Diffusion Model Regularized Implicit Neural Representation for CT Metal Artifact Reduction
This addresses performance instability in clinical CT imaging due to metal artifacts, offering an unsupervised method that improves over existing approaches, though it appears incremental as it builds on prior techniques.
The paper tackled CT metal artifact reduction by integrating physical constraints with a pre-trained diffusion model to provide prior knowledge, achieving effective results on simulated and clinical data with demonstrated generalization ability.
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; 2) traditional heuristics regularization terms cannot fully capture the abundant prior knowledge available. To overcome these shortcomings, we propose diffusion model regularized implicit neural representation framework for MAR. The implicit neural representation integrates physical constraints and imposes data fidelity, while the pre-trained diffusion model 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 to clinical settings.