Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework

被引:0
|
作者
Jiang, Shibo [1 ,2 ]
Sun, Yuewen [1 ,2 ]
Xu, Shuo [1 ,2 ]
Zhang, Zehuan [1 ,2 ]
Wu, Zhifang [1 ,2 ]
机构
[1] Tsinghua Univ, Inst Nucl & New Energy Technol, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Beijing Key Lab Nucl Detect Technol, Beijing 100084, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 08期
关键词
industrial CT; metal artifact correction; dual-domain; UNet; ResNet; joint framework; REDUCTION;
D O I
10.3390/app14083261
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Dual-domain metal trace inpainting network for metal artifact reduction in baggage CT images
    Hai, Chao
    He, Jingze
    Li, Baolei
    He, Penghui
    Sun, Liang
    Wu, Yapeng
    Yang, Min
    MEASUREMENT, 2023, 207
  • [2] Synthesize monochromatic images in spectral CT by dual-domain deep learning
    Feng, Chuqing
    Chen, Zhiqiang
    Kang, Kejun
    Xing, Yuxiang
    15TH INTERNATIONAL MEETING ON FULLY THREE-DIMENSIONAL IMAGE RECONSTRUCTION IN RADIOLOGY AND NUCLEAR MEDICINE, 2019, 11072
  • [3] Dual-domain fusion network for metal artifact reduction in CT
    Wu, Jiayi
    Li, Yuan
    Wang, Zhe
    Wang, Huamin
    Tonetti, Maurizio S.
    Cao, Guohua
    MEDICAL IMAGING 2024: PHYSICS OF MEDICAL IMAGING, PT 1, 2024, 12925
  • [4] Sinogram domain metal artifact correction of CT via deep learning
    Zhu, Yulin
    Zhao, Hanqing
    Wang, Tangsheng
    Deng, Lei
    Yang, Yupeng
    Jiang, Yuming
    Li, Na
    Chan, Yinping
    Dai, Jingjing
    Zhang, Chulong
    Li, Yunhui
    Xie, Yaoqin
    Liang, Xiaokun
    COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 155
  • [5] DCDiff: Dual-Domain Conditional Diffusion for CT Metal Artifact Reduction
    Shen, Ruochong
    Li, Xiaoxu
    Li, Yuan-Fang
    Sui, Chao
    Peng, Yu
    Ke, Qiuhong
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT VII, 2024, 15007 : 223 - 232
  • [6] An improved dual-domain network for metal artifact reduction in CT images using aggregated contextual transformations
    Tang, Hui
    Jiang, Sudong
    Lin, Yubing
    Li, Yu
    Bao, Xudong
    PHYSICS IN MEDICINE AND BIOLOGY, 2023, 68 (17):
  • [7] A new dental CBCT metal artifact reduction method based on a dual-domain processing framework
    Tang, Hui
    Lin, Yu Bing
    Jiang, Su Dong
    Li, Yu
    Li, Tian
    Bao, Xu Dong
    PHYSICS IN MEDICINE AND BIOLOGY, 2023, 68 (17):
  • [8] Metal artifact correction of industrial CT images based on generative adversarial networks
    Jiang, Shibo
    Sun, Yuewen
    Xu, Shuo
    Wu, Zhifang
    Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University, 2022, 43 (12): : 1766 - 1771
  • [9] IRDNet: Iterative Relation-Based Dual-Domain Network via Metal Artifact Feature Guidance for CT Metal Artifact Reduction
    Wang, Huamin
    Yang, Shuo
    Bai, Xiao
    Wang, Zhe
    Wu, Jiayi
    Lv, Yang
    Cao, Guohua
    IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES, 2024, 8 (08) : 959 - 972
  • [10] U-DuDoNet: Unpaired Dual-Domain Network for CT Metal Artifact Reduction
    Lyu, Yuanyuan
    Fu, Jiajun
    Peng, Cheng
    Zhou, S. Kevin
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VI, 2021, 12906 : 296 - 306