A Denoising Diffusion Probabilistic Model for Metal Artifact Reduction in CT.

ABSTRACT: The presence of metal objects leads to corrupted CT projection measurements, resulting in metal artifacts in the reconstructed CT images. AI promises to offer improved solutions to estimate missing sinogram data for metal artifact reduction (MAR), as previously shown with convolutional neural networks (CNNs) and generative adversarial networks (GANs). Recently, denoising diffusion probabilistic models (DDPM) have shown great promise in image generation tasks, potentially outperforming GANs. In this study, a DDPM-based approach is proposed for inpainting of missing sinogram data for improved MAR. The proposed model is unconditionally trained, free from information on metal objects, which can potentially enhance its generalization capabilities across different types of metal implants compare

SUBMITTER: Karageorgos GM 

PROVIDER: S-EPMC11657996 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature