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一种基于伪影估计的低剂量CT图像降噪方法

韩兴隆 上官宏 张雄 韩泽芳 崔学英 王安红

韩兴隆,上官宏,张雄,等. 一种基于伪影估计的低剂量CT图像降噪方法[J]. 北京航空航天大学学报,2023,49(2):491-502 doi: 10.13700/j.bh.1001-5965.2021.0263
引用本文: 韩兴隆,上官宏,张雄,等. 一种基于伪影估计的低剂量CT图像降噪方法[J]. 北京航空航天大学学报,2023,49(2):491-502 doi: 10.13700/j.bh.1001-5965.2021.0263
HAN X L,SHANGGUAN H,ZHANG X,et al. A low-dose CT image denoising method based on artifact estimation[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(2):491-502 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0263
Citation: HAN X L,SHANGGUAN H,ZHANG X,et al. A low-dose CT image denoising method based on artifact estimation[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(2):491-502 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0263

一种基于伪影估计的低剂量CT图像降噪方法

doi: 10.13700/j.bh.1001-5965.2021.0263
基金项目: 国家自然科学基金(62001321);山西省高等学校科技创新项目(2019L0642);山西省研究生教育创新项目(2020SY417,2020SY423);山西省自然科学基金(201901D111261)
详细信息
    通讯作者:

    E-mail:shangguan_hong@tyust.edu.cn

  • 中图分类号: TN911.73;TP391

A low-dose CT image denoising method based on artifact estimation

Funds: National Natural Science Foundation of China (62001321); Scientific and Technologial Innovation Programs of Higher Education Institutions in Shanxi (2019L0642); Excellent Graduate Innovation Project of Shanxi Province (2020SY417,2020SY423); Natural Science Foundation of Shanxi Province (201901D111261)
More Information
  • 摘要:

    低剂量CT(LDCT)包含丰富组织结构、病理信息和分布极其不规律的噪声伪影,这2种信息的幅度值分布规律相似。因此,LDCT降噪任务易出现特征提取不充分、网络对噪声伪影方向特性敏感度不足及降噪结果过度平滑等问题。为此,应用U-Net网络作为去噪网络的基本模型,设计了一种基于伪影估计的LDCT降噪网络。所提网络模型主要包括主特征提取网络和方向敏感注意力子网络2部分。为充分利用不同尺度特征之间的差异性,提高特征提取有效性,在编解码U-Net结构基础上增加了一个稠密特征增强模块;为提高降噪网络对噪声伪影方向特征的敏感度,设计了一个方向敏感注意力子网络;为保障网络训练稳定性,设计了多种损失函数来共同优化网络训练过程。实验结果表明:与目前主流的LDCT降噪方法相比,所提方法降噪结果的视觉效果与量化指标均表现最佳。

     

  • 图 1  本文降噪整体框架

    Figure 1.  Overall architecture of our proposed denoising network

    图 2  随迭代次数增加不同损失函数值的收敛曲线

    Figure 2.  Network convergence curves of different loss function values as number of iterations increases

    图 3  四种降噪方法对受横条状伪影污染的胸部LDCT的降噪结果

    Figure 3.  Denoising results of 4 denoising methods on chest LDCT contaminated by horizontal stripe artifacts

    图 4  图3中不同降噪方法降噪结果与NDCT的差值图

    Figure 4.  Difference between denoising results of different denoising methods and NDCT shown in Fig.3

    图 5  四种降噪方法对含有低衰减病变的腹部LDCT图像的降噪结果

    Figure 5.  Denoising results of 4 denoising methods for abdominal LDCT image with lesions

    图 6  四种降噪方法对组织结构比较丰富的腹部LDCT图像的降噪结果

    Figure 6.  Denoising results of 4 denoising methods on abdomenal LDCT with rich tissue structure

    图 7  不同降噪方法在Piglet数据集上对LDCT图像的降噪结果

    Figure 7.  Denoising results of different denoising methods on LDCT image on Piglet dataset

    图 8  四种降噪方法在MAYO测试集上平均PSNR与SSIM量化指标表现

    Figure 8.  Average PSNR and SSIM performance of four denoising methods on MAYO test sets

    图 9  四种降噪方法在Piglet测试集上平均PSNR与SSIM量化指标表现

    Figure 9.  Average PSNR and SSIM performance of four denoising methods on Piglet test sets

    图 10  降噪图像局部ROI的PSNR、SSIM与VIF值

    Figure 10.  PSNR, SSIM and VIF values of local ROI of denoised image

    表  1  主观评价得分

    Table  1.   Subjective evaluation score

    降噪方法组织识别度噪声抑制度整体图像质量
    LDCT2.852.752.80
    BM3D2.953.153.10
    RED-CNN3.603.553.65
    pix2pix4.054.254.20
    本文方法4.354.454.45
    NDCT4.904.954.95
    下载: 导出CSV

    表  2  四种降噪方法在MAYO测试集上平均PSNR与SSIM(平均值±标准差)

    Table  2.   Average PSNR and SSIM (MEAN±SD) of four denoising methods on MAYO test set

    降噪方法PSNR(MEAN±SD)SSIM(MEAN±SD)
    LDCT26.7891±1.97820.8100±0.0535
    BM3D30.2235±1.91930.8557±0.0455
    RED-CNN30.9054±1.76470.8610±0.0418
    Pix2pix28.3183±1.65990.8111±0.0545
    本文方法31.3091±1.74420.8738±0.0410
    下载: 导出CSV

    表  3  四种降噪方法对2幅具有代表性的LDCT图像的降噪结果

    Table  3.   Denoising results of two representative LDCT images with four denoising methods

    降噪
    方法
    胸部LDCT图像腹部LDCT图像
    PSNRSSIMVIFIFCNQMPSNRSSIMVIFIFCNQM
    LDCT23.07290.82880.40951.998516.925322.88690.69600.24321.949824.5226
    BM3D23.87030.84030.36881.777216.918727.14700.76120.29372.382525.7574
    RED-CNN25.27970.87640.31471.395016.341727.54320.76880.29332.372625.2474
    pix2pix26.41410.87000.37411.769417.436125.58310.70630.26322.048423.4076
    本文方法28.07880.89510.43872.199819.073127.95210.77360.30782.519926.1564
    下载: 导出CSV

    表  4  网络结构消融对方法性能的影响

    Table  4.   Influence of network structure ablation on method performance

    网络结构平均SSIM平均PSNR
    w/o DFF0.857030.4679
    w/o DA0.872231.2892
    本文算法0.873831.3091
    下载: 导出CSV

    表  5  不同消融损失函数在测试集上降噪结果的平均PSNR与SSIM值

    Table  5.   Average PSNR and SSIM values of denoising results of different ablation loss functions on test set

    子损失平均SSIM平均PSNR
    伪影一致性损失伪影掩码损失像素级L1损失
    0.872631.2517
    0.872431.2734
    0.873331.2687
    0.873031.2873
    0.872831.2747
    0.873831.3091
    下载: 导出CSV

    表  6  四种降噪方法的训练与测试时间比较

    Table  6.   Comparison of training and testing time under four denoising methods

    降噪方法训练时间/s测试时间(s/幅)迭代次数
    BM3D1.2078
    RED-CNN 2248.920.1041100
    pix2pix31603.880.0679200
    本文方法85766.740.0147100
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-19
  • 录用日期:  2021-08-08
  • 网络出版日期:  2021-11-15
  • 整期出版日期:  2023-02-28

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