Paper
20 November 2024 DPD-Net: a dual-domain progressive diffusion network for joint limited-angle and metal artifact removal
Author Affiliations +
Abstract
Computed Tomography (CT) is a high-precision medical imaging technique that utilizes X-rays and computer reconstruction to provide detailed three-dimensional images of human anatomy. It is used for clinical diagnosis and treatment. Non-ideal scanning conditions often occur, including the presence of metal implants in the human body and limited-angle scanning. These non-ideal conditions result in serious metal artifacts and limited-angle artifacts. To address the above challenge, in this paper, we propose a novel deep dual-domain progressive diffusion network, namely DPD-Net, to jointly suppress metal artifact and limited-angle artifact for the first time. DPD-Net leverages the advantage of dual-domain strategy for limited-angle artifact suppression in image-domain and metal trace inpainting in sinogram-domain simultaneously. To sufficiently solve dual-artifact problem, the dual-domain generative diffusion models are designed for data distribution learning. The proposed DPD-Net is trained and evaluated on a publicly available dataset. Extensive experimental results validate that the proposed method outperforms the state-of-the-art competing methods.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yijie Shi, Tianling Lyu, Xusheng Zhang, Xinyun Zhong, Yang Yang, Pinzheng Zhang, Zhan Wu, and Yang Chen "DPD-Net: a dual-domain progressive diffusion network for joint limited-angle and metal artifact removal", Proc. SPIE 13242, Optics in Health Care and Biomedical Optics XIV, 132420M (20 November 2024); https://doi.org/10.1117/12.3034419
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Metals

Diffusion

Education and training

X-ray computed tomography

Computed tomography

Data modeling

Image processing

Back to Top