@inproceedings{9683f572889d4a66bd7f159bf5074948,
title = "CLEAR-Net: A Discretization-Aware Framework for Scale and Domain Adaptive Metal Artifact Reduction in CT",
abstract = "Metal artifacts severely degrade the quality of CT images. Existing learning-based metal artifact reduction (MAR) methods often miss multi-scale anatomy and fail to transfer from synthetic to clinical scans. We present CLEAR-Net, which injects clinical priors and adaptively aligns corrupted features via two modules: CEAB, a quantized multi-scale anatomy bank distilled from clean clinical CTs, and FLAG, a scale-wise gating mechanism that aligns features to CEAB priors across domains. This structure-aware design preserves organ boundaries and fine textures, boosting robustness and generalization. Extensive experiments demonstrate CLEAR-Net's superior performance. The source code will be made publicly available.",
keywords = "CT Restoration, Deep Learning, Discretization Alignment, Metal Artifact Reduction, Multi-Scale Prior",
author = "Mingye Zou and Xinghua Ma and Yacong Li and Taiping Qu and Kuanquan Wang and Gongning Luo",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025 ; Conference date: 15-12-2025 Through 18-12-2025",
year = "2025",
doi = "10.1109/BIBM66473.2025.11356234",
language = "英语",
series = "Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4568--4571",
editor = "Juan Liu and Jingshan Huang and Xiaowo Wang and Fa Zhang and Xiufen Zou and Tian Tian and Xiaohua Hu and Bin Hu and Yi Xiong",
booktitle = "Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025",
address = "美国",
}