[1]梁佩文,郑梦洵,唐龙,等.基于条件扩散模型的CT金属伪影校正[J].中国医学物理学杂志,2025,42(4):457-465.[doi:10.3969/j.issn.1005-202X.2025.04.006]
 LIANG Peiwen,ZHENG Mengxun,TANG Long,et al.CT metal artifact correction using conditional diffusion model[J].Chinese Journal of Medical Physics,2025,42(4):457-465.[doi:10.3969/j.issn.1005-202X.2025.04.006]
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基于条件扩散模型的CT金属伪影校正()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第4期
页码:
457-465
栏目:
医学影像物理
出版日期:
2025-04-20

文章信息/Info

Title:
CT metal artifact correction using conditional diffusion model
文章编号:
1005-202X(2025)04-0457-09
作者:
梁佩文郑梦洵唐龙张华
南方医科大学生物医学工程学院/广东省医学图像处理重点实验室,广东 广州 510515
Author(s):
LIANG Peiwen ZHENG Mengxun TANG Long ZHANG Hua
Guangdong Provincial Key Laboratory of Medical Image Processing/School of Biomedical Engineering, Southern Medical University,Guangzhou 510515, China
关键词:
计算机断层扫描金属伪影条件扩散模型
Keywords:
computed tomography metal artifact conditional diffusion model
分类号:
R318;TP391.41
DOI:
10.3969/j.issn.1005-202X.2025.04.006
文献标志码:
A
摘要:
目的:提出一种基于模板先验的条件扩散模型,以校正计算机断层扫描在扫描过程中因金属种植体导致的重建图像出现明暗相间的条纹状金属伪影。方法:首先利用图像分割技术将金属部分进行分割,并生成对应金属轨迹投影数据,去除金属轨迹的投影数据以及模板先验图像对应投影数据;然后构建条件扩散模型,恢复投影数据对应金属区域的缺失部分;最后对恢复投影进行滤波反投影重建获得校正图像。结果:本文方法与伪影解耦网络相比,峰值信噪比提高3.44 dB,结构相似性指数提高0.079;与扩散模型相比,峰值信噪比提高0.749 dB,结构相似性指数提高0.015。结论:本文方法优于传统金属伪影校正方法,在条形伪影及阴影型伪影校正方面具有较好表现。
Abstract:
Objective To propose a conditional diffusion model based on template priors for correcting streaky metal artifactscharacterized by alternating bright and dark patterns that appear in reconstructed computed tomography (CT) images due tothe presence of metallic implants. Methods After isolating metal region using image segmentation technology to generatemetal trajectory projection data, the projection data of metal trajectory and the corresponding projection data of template priorimages were excluded. Subsequently, a conditional diffusion model was developed to recover the missing portions of theprojection data in the metal-affected regions. Finally, the restored projection data were reconstructed using filtered backprojection to obtain the corrected image. Results Compared with artifact disentanglement network and diffusion model, theproposed approach improved peak signal to noise ratio by 3.44 and 0.749 dB, and increased structural similarity index by0.079 and 0.015, respectively. Conclusion The proposed approach outperforms traditional metal artifact correction methodsand exhibits superior performance in reducing both streaky and shadow artifacts.

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备注/Memo

备注/Memo:
【收稿日期】2024-12-20【基金项目】广东省自然科学基金(2024A1515010523)【作者简介】梁佩文,硕士研究生,研究方向:CT 金属伪影校正,E-mail: 915643910@qq.com【通信作者】张华,副教授,研究方向:医学 CT 重建与分析,E-mail:xinsier@smu.edu.cn
更新日期/Last Update: 2025-04-30