earticle

영어

Cone-Beam Computed Tomography (CBCT) plays a central role in Image-Guided Radiation Therapy (IGRT), but its relatively long acquisition time often leads to motion artifacts that reduce diagnostic quality. This work presents a framework for artifact correction based on residual learning within a conditional Denoising Diffusion Probabilistic Model (DDPM). In this setting, the model learns to predict the residual artifact component instead of the entire CT image. To encourage stable learning, a hybrid loss function incorporating L1 regularization on the predicted residual is introduced. The L1 term is intended to promote sparsity, guiding the model to focus on localized artifact regions while maintaining robustness against anatomical inconsistencies between CBCT and CT pairs. Experiments on paired CBCT-CT datasets showed improved quantitative and perceptual results compared to baseline diffusion and residual models, suggesting that the sparsity constraint may contribute to more reliable artifact suppression.