bibtext: @InProceedings{ She_DCDiff_MICCAI2024,
                   author = { Shen, Ruochong and Li, Xiaoxu and Li, Yuan-Fang and Sui, Chao and Peng, Yu and Ke, Qiuhong },
                   title = { { DCDiff: Dual-Domain Conditional Diffusion for CT Metal Artifact Reduction } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: Metallic implants in X-ray Computed Tomography (CT) scans can lead to undesirable artifacts, adversely affecting the quality of images and, consequently, the effectiveness of clinical treatment. Metal Artifact Reduction (MAR) is essential for improving diagnostic accuracy, yet this task is challenging due to the uncertainty associated with the affected regions. In this paper, inspired by the capabilities of diffusion models in generating high-quality images, we present a novel MAR framework termed Dual-Domain Conditional Diffusion (DCDiff). Specifically, our DCDiff takes dual-domain information as the input conditions for generating clean images: 1) the image domain incorporating raw CT image and the filtered back project (FBP) output of the metal trace, and 2) the sinogram domain achieved with a new diffusion interpolation algorithm. Experimental results demonstrate that our DCDiff outperforms state-of-the-art methods, showcasing its effectiveness for MAR.
title: DCDiff: Dual-Domain Conditional Diffusion for CT Metal Artifact Reduction
authors: Shen, Ruochong Li, Xiaoxu Li, Yuan-Fang Sui, Chao Peng, Yu Ke, Qiuhong