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基于可变阶变分模型的医用低剂量CT图像去噪

王娜 张权 刘祎 贾丽娜 桂志国

王娜, 张权, 刘祎, 等 . 基于可变阶变分模型的医用低剂量CT图像去噪[J]. 北京航空航天大学学报, 2019, 45(9): 1757-1764. doi: 10.13700/j.bh.1001-5965.2018.0775
引用本文: 王娜, 张权, 刘祎, 等 . 基于可变阶变分模型的医用低剂量CT图像去噪[J]. 北京航空航天大学学报, 2019, 45(9): 1757-1764. doi: 10.13700/j.bh.1001-5965.2018.0775
WANG Na, ZHANG Quan, LIU Yi, et al. Medical low-dose CT image denoising based on variable order variational model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1757-1764. doi: 10.13700/j.bh.1001-5965.2018.0775(in Chinese)
Citation: WANG Na, ZHANG Quan, LIU Yi, et al. Medical low-dose CT image denoising based on variable order variational model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1757-1764. doi: 10.13700/j.bh.1001-5965.2018.0775(in Chinese)

基于可变阶变分模型的医用低剂量CT图像去噪

doi: 10.13700/j.bh.1001-5965.2018.0775
基金项目: 

国家自然科学基金 61671413

国家自然科学基金 61801438

国家重大科学仪器设备开发专项 2014YQ24044508

山西省归国学者基金 2016-085

详细信息
    作者简介:

    王娜     女, 博士研究生。主要研究方向:图像处理

    桂志国    男, 博士, 教授, 博士生导师。主要研究方向:信号与信息处理、图像处理和识别、图像重建

    通讯作者:

    桂志国, E-mail: gzgtg@163.com

  • 中图分类号: TP391

Medical low-dose CT image denoising based on variable order variational model

Funds: 

National Natural Science Foundation of China 61671413

National Natural Science Foundation of China 61801438

National Key Scientific Instrument and Equipment Development Project of China 2014YQ24044508

Shanxi Provincial Foundation for Returned Scholars (Main Program), China 2016-085

More Information
  • 摘要:

    为了降低患者的辐射风险,低剂量CT(LDCT)广泛用于临床诊断,但辐射剂量的减少在重建的LDCT图像中引入了斑点噪声和条纹伪影。为了提高LDCT图像的质量,提出了一种基于可变阶变分模型的后处理技术。所提出的变分模型使用边缘指示器控制变分阶数,根据图像的特征在一阶全变分(TV)正则项和二阶有界Hessian(BH)正则项之间交替变换。采用基于快速傅里叶变换(FFT)的分裂Bregman算法求解所提出的变分模型。该模型在保留高剂量CT(HDCT)图像相应结构的同时,有效抑制了斑点噪声和条纹伪影。重建的图像和实验数据表明,所提出的变分模型比现有的先进模型具有更好的质量。

     

  • 图 1  真实胸腔体模LDCT图像降噪结果的视觉比较

    Figure 1.  Visual comparison of denoising results on LDCT image of actual thoracic phantom

    图 2  真实胸腔体模局部放大图的视觉比较

    Figure 2.  Visual comparison of denoising results on the local enlarged drawing by the squares in Fig. 1(a).

    图 3  临床胸腔LDCT图像降噪结果的视觉比较

    Figure 3.  Visual comparison of denoising results on LDCT image of clinical thoracic

    图 4  临床腹腔LDCT图像降噪结果的视觉比较

    Figure 4.  Visual comparison of denoising results on LDCT image of clinical abdominal

    图 5  临床盆腔LDCT图像降噪结果的视觉比较

    Figure 5.  Visual comparison of denoising results on LDCT image of clinical pelvic

    表  1  实际胸腔体模的定量比较

    Table  1.   Quantified comparison of actual thoracic phantom

    降噪模型
    PSNR/dB MSSIM 除噪时间/s
    TVBH模型 25.259 3 0.886 4 19.45
    EWSO模型 24.213 9 0.914 9 10.83
    NLM模型 26.664 1 0.908 8 379.78
    本文模型 27.205 1 0.926 6 27.88
    下载: 导出CSV

    表  2  临床数据的SNR值比较

    Table  2.   Comparison of SNR values of clinical data

    LDCT图像及降噪模型
    ROI1 ROI2 ROI3 ROI1 ROI2 ROI3 ROI1 ROI2 ROI3
    LDCT图像 0.529 0 0.049 7 0.093 7 0.073 3 0.275 6 0.120 0 0.067 0 1.201 4 0.069 9
    TVBH模型 9.306 3 0.054 7 0.098 3 0.165 0 0.697 8 0.209 4 0.341 7 2.421 4 0.078 0
    EWSO模型 14.386 6 0.054 6 0.099 2 0.093 5 0.485 6 0.162 4 0.108 5 1.320 2 0.072 2
    NLM模型 1.301 1 0.058 0 0.094 6 0.173 1 0.890 9 0.159 4 0.110 5 1.446 7 0.069 7
    本文模型 26.270 6 0.360 8 2.099 4 0.875 0 1.288 4 1.129 7 0.641 6 3.049 5 0.093 1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-02
  • 录用日期:  2019-04-05
  • 刊出日期:  2019-09-20

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