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Chinese Journal of Quantum Electronics, Volume. 41, Issue 5, 738(2024)

A method to solve lack of paired data in neutron computed tomography for deep learning by using photon images

GUO Hu1...2, CHEN Shuai2,*, YANG Minghan2, ZHANG Ziheng2,3, SHAO Hui4 and WANG Jianye2 |Show fewer author(s)
Author Affiliations
  • 1Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
  • 2Institute of Nuclear Energy Safety Technology, HFIPS, Chinese Academy of Sciences,Hefei 230031, China
  • 3University of Science and Technology of China, Hefei 230026, China
  • 4Anhui Jianzhu University, Hefei 230022, China
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    Figures & Tables(10)
    Unsupervised deep reconstruction network composed of encoder, decoder and migration unit

    Fig. 1. Unsupervised deep reconstruction network composed of encoder, decoder and migration unit

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    Images of the experiment sample. (a) Sample perspection of 0°; (b) Sample perspection of 90°;(c) A perspective of the sample

    Fig. 2. Images of the experiment sample. (a) Sample perspection of 0°; (b) Sample perspection of 90°;(c) A perspective of the sample

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    Reconstruction cross section of neutron CT and photon CT. (a) Neutron CT image;(b) Low-dose photon CT image; (c) Normal-dose photon CT image

    Fig. 3. Reconstruction cross section of neutron CT and photon CT. (a) Neutron CT image;(b) Low-dose photon CT image; (c) Normal-dose photon CT image

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    Training loss results for 100 training epochs

    Fig. 4. Training loss results for 100 training epochs

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    Reconstruction results of the simple structure cross-section of 60% dose neutron CT images by six reconstruction methods. (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN

    Fig. 5. Reconstruction results of the simple structure cross-section of 60% dose neutron CT images by six reconstruction methods. (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN

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    Local details of the reconstruction of simple structure cross sections of 60% dose neutron CT images by six reconstruction methods. (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN

    Fig. 6. Local details of the reconstruction of simple structure cross sections of 60% dose neutron CT images by six reconstruction methods. (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN

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    Local details of the reconstruction of complex structure cross sections of 60% dose neutron CT images by six reconstruction methods. (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN

    Fig. 7. Local details of the reconstruction of complex structure cross sections of 60% dose neutron CT images by six reconstruction methods. (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN (a) FBP; (b) SART; (c) MDAM; (d) DnCNN; (e) FBPConvNet; (f) REDCNN

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    Reconstruction results of two kinds of structure cross sections of 60% dose neutron CT images by MDAM_skip.(a) Simple structure cross section; (b) Complex structure cross section

    Fig. 8. Reconstruction results of two kinds of structure cross sections of 60% dose neutron CT images by MDAM_skip.(a) Simple structure cross section; (b) Complex structure cross section

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    Reconstruction results of two structural cross sections of 40% dose neutron CT images by three reconstruction methods. (a)(d) FBP; (b)(e) SART; (c)(f) MDAM

    Fig. 9. Reconstruction results of two structural cross sections of 40% dose neutron CT images by three reconstruction methods. (a)(d) FBP; (b)(e) SART; (c)(f) MDAM

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    • Table 1. Comparison of SSIM, PSNR and RMSE of neutron CT images reconstructed by various methods

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      Table 1. Comparison of SSIM, PSNR and RMSE of neutron CT images reconstructed by various methods

      MethodSSIMPSNRRMSE
      FBPConvNet0.898 ± 0.05436.98 ± 2.860.0127 ± 0.0063
      REDCNN0.863 ± 0.03335.36 ± 2.030.0148 ± 0.0042
      DnCNN0.824 ± 0.04434.76 ± 2.410.0162 ± 0.0063
      SART0.746 ± 0.00430.70 ± 0.097.5014 ± 0.0881
      MDAM0.905 ± 0.01634.90 ± 0.990.0181 ± 0.0025
      MDAM_skip0.864 ± 0.01732.48 ± 0.800.0239 ± 0.0024
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    Hu GUO, Shuai CHEN, Minghan YANG, Ziheng ZHANG, Hui SHAO, Jianye WANG. A method to solve lack of paired data in neutron computed tomography for deep learning by using photon images[J]. Chinese Journal of Quantum Electronics, 2024, 41(5): 738

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    Paper Information

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    Received: Nov. 22, 2022

    Accepted: --

    Published Online: Jan. 8, 2025

    The Author Email: CHEN Shuai (shuai.chen@inest.cas.cn)

    DOI:10.3969/j.issn.1007-5461.2024.05.004

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology