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基于深度学习的重建算法在健康志愿者肝脏低剂量薄层CT检查中的应用研究

曾令明 徐旭 曾文 彭婉琳 张金戈 胡斯娴 刘科伶 夏春潮 李真林

曾令明, 徐旭, 曾文, 等. 基于深度学习的重建算法在健康志愿者肝脏低剂量薄层CT检查中的应用研究[J]. 四川大学学报(医学版), 2021, 52(5): 807-812. doi: 10.12182/20210660103
引用本文: 曾令明, 徐旭, 曾文, 等. 基于深度学习的重建算法在健康志愿者肝脏低剂量薄层CT检查中的应用研究[J]. 四川大学学报(医学版), 2021, 52(5): 807-812. doi: 10.12182/20210660103
ZENG Ling-ming, XU Xu, ZENG Wen, et al. Application of Deep Learning Reconstruction Algorithm in Low-Dose Thin-Slice Liver CT of Healthy Volunteers[J]. JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCE EDITION), 2021, 52(5): 807-812. doi: 10.12182/20210660103
Citation: ZENG Ling-ming, XU Xu, ZENG Wen, et al. Application of Deep Learning Reconstruction Algorithm in Low-Dose Thin-Slice Liver CT of Healthy Volunteers[J]. JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCE EDITION), 2021, 52(5): 807-812. doi: 10.12182/20210660103

基于深度学习的重建算法在健康志愿者肝脏低剂量薄层CT检查中的应用研究

doi: 10.12182/20210660103
基金项目: 四川省科技计划项目(No. 2019YFS0522)和四川大学华西医院学科卓越发展1·3·5工程项目(No. ZYGD18019)资助
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    E-mail:HX_lizhenlin@126.com

Application of Deep Learning Reconstruction Algorithm in Low-Dose Thin-Slice Liver CT of Healthy Volunteers

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  • 摘要:   目的  比较基于深度学习(deep learning, DL)的重建算法、滤波反投影(filtered back projection filtering, FBP)重建算法和迭代重建(iterative reconstruction, IR)算法,探讨DL重建算法在健康志愿者肝脏低剂量薄层CT检查中临床应用的可行性。  方法  采用联影160层CT对直径180 mm的标准水模进行扫描,比较DL、FBP和IR算法的噪声功率谱。前瞻性纳入健康志愿者100例,其中常规剂量组(normal dose, ND)50例、低剂量组(low dose, LD)50例。ND组采用IR算法;LD组分别采用DL、FBP和IR算法。使用单因素方差分析比较ND-IR、LD-FBP、LD-IR和LD-DL 4组的肝CT值、肝噪声、肝信噪比(signal-to-noise ratio, SNR)、对比噪声比(contrast noise ratio, CNR)和质量因数(figure of merit, FOM)。采用Kruskal-Wallis检验比较4组图像的解剖结构主观评分。  结果  DL噪声功率谱平均峰值最低,形态与中等迭代等级IR算法相似。ND-IR、LD-FBP、LD-IR和LD-DL的肝CT值差异无统计学意义,LD-DL的噪声低于LD-FBP、LD-IR和ND-IR(P<0.05),LD-DL的SNR、CNR和FOM均高于LD-FBP、LD-IR和ND-IR(P<0.05)。LD-DL解剖结构的主观评分均与ND-IR无明显差异(P>0.05),且均高于LD-FBP和LD-IR(P<0.05)。LD组相对于ND组减少约50.2%辐射剂量。  结论  噪声形态与临床常用的中等迭代等级IR相近的DL算法降噪能力高于IR,与FBP相比噪声形态较平滑但降噪能力大幅提高,在健康志愿者肝脏低剂量薄层CT检查中可获得肝脏常规剂量厚层CT的图像质量。
  • 图  1  单张模体CT图像的噪声功率谱(A)和50张模体CT图像的平均噪声功率谱(B)

    Figure  1.  Noise power spectrum of single phantom CT image (A) and the average noise power spectrum of 50 phantom CT images (B)

    图  2  49岁女性低剂量CT分别使用滤波反投影(A)、迭代重建(B)和基于深度学习的算法(C)重建(体质量指数:19.3 kg/m2;容积CT剂量指数:3.4 mGy;剂量-长度乘积:92.13 mGy∙cm;有效剂量:1.38 mSv)。56岁女性常规剂量CT使用迭代重建算法(D)重建(体质量指数:19.1 kg/m2;容积CT剂量指数:7.76 mGy;剂量-长度乘积:237.14 mGy∙cm;有效剂量:3.56 mSv)

    Figure  2.  Low-dose CT in a 49-year-old woman was reconstructed using filtered back projection (A), iterative reconstruction (B), and deep learning algorithm (C), respectively (body mass index, 19.3 kg/m2; volume CT dose index, 3.4 mGy; dose-length product, 92.13 mGy∙cm; estimated effective dose, 1.38 mSv). A normal-dose CT image of a 56-year-old woman was reconstructed with iterative reconstruction of a 56-year-old woman (D) (body mass index, 19.1 kg/m2; volume CT dose index, 7.76 mGy; dose-length product, 237.14 mGy∙cm; estimated effective dose, 3.56 mSv)

    表  1  3种重建方式的噪声功率谱平均峰值和平均空间频率(n=50)

    Table  1.   Noise power spectrum average peaks and average spatial frequencies obtained for three reconstruction types (n=50)

    ItemFBPIRDL
    NPS average peak/HU2·mm2 147.70 90.68 65.25
    NPS average spatial frequency/mm−1 2.93 2.73 2.45
     NPS: Noise power spectrum; FBP: Filtered back projection; IR: Iterative reconstruction; DL: Deep learning.
    下载: 导出CSV

    表  2  常规剂量和低剂量图像质量比较(n=100)

    Table  2.   Comparison of the image quality of the normal-dose images and low-dose images (n=100)

    VariableND-IRLD-FBPLD-IRLD-DL
    CT value/HU 108.9±8.59 108.89±10.91 108.98±10.83 109.13±10.55
    Noise/HU 9.71±1.19* 20.36±1.49* 15.26±1.19* 9.10±1.26
    SNR 11.41±1.77* 5.39±0.72* 7.19±0.97* 12.22±2.07
    CNR 4.51±1.28* 2.22±0.61* 2.95±0.8* 5.11±1.31
    FOM 25.99±12.97* 11.85±6.21* 21.08±10.95* 62.45±35.5
     ND: Normal dose; LD: Low dose; IR: Iterative reconstruction; FBP: Filtered back projection; DL: Deep learning; SNR: Signal-to-noise ratio; CNR: Contrast-to-noise ratio; FOM: Figure of merit. *P<0.05, vs. LD-DL.
    下载: 导出CSV

    表  3  解剖结构主观评分比较(n=100)

    Table  3.   Comparison of subjective scores of anatomical structures (n=100)

    VariableND-IRLD-FBPLD-IRLD-DL
    Intrahepatic portal veins 3.48±0.58 2.36±0.66* 2.46±0.61* 3.36±0.56
    Hepatic veins 3.78±0.42 2.48±0.68* 2.56±0.70* 3.86±0.35
    Common hepatic duct 2.92±0.57 2.06±0.77* 2.20±0.69* 2.81±0.53
    Common bile duct 2.64±0.53 1.90±0.76* 2.08±0.78* 2.72±0.54
    Gallbladder wall 3.10±0.54 2.22±0.76* 2.38±0.88* 3.01±0.64
     ND: Normal dose; LD: Low dose; IR: Iterative reconstruction; FBP: Filtered back projection; DL: Deep learning. *P<0.001, vs. LD-DL.
    下载: 导出CSV

    表  4  两组辐射剂量比较(n=100)

    Table  4.   Radiation dose in the two groups (n=100)

    GroupCTDIVOL/mGyDLP/mGy∙cmED/mSv
    ND 9.09±1.24 390.71±138.02 5.86±2.07
    LD 4.41±0.67 194.68±62.46 2.92±0.94
    t 23.409 9.15 9.15
    P <0.001 <0.001 <0.001
     ND: Normal dose; LD: Low dose; CTDIVOL: Volume CT dose index; DLP: Dose-length product; ED: Effective dose.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-10-13
  • 修回日期:  2021-05-12
  • 网络出版日期:  2021-12-06
  • 刊出日期:  2021-09-20

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