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Spectroscopy and Spectral Analysis, Volume. 41, Issue 9, 2950(2021)

Research on Spectral CT Image Denoising Via Fully Convolution Pyramid Residual Network

Xue-zhi REN1、*, Peng HE1、1; 2; *;, Zou-rong LONG1、1;, Xiao-dong GUO1、1;, Kang AN2、2;, Xiao-jie LÜ1、1;, Biao WEI1、1; 2;, and Peng FENG1、1; 2; *;
Author Affiliations
  • 11. Key Laboratory of Optoelectronics Technology & System (Chongqing University), Ministry of Education, Chongqing 400044, China
  • 22. ICT-NDT Engineering Research Center (Chongqing University), Ministry of Education, Chongqing 400044, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
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    References (13)
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    Figures & Tables(8)
    FCPRN structure

    Fig. 1. FCPRN structure

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    Spectral CT system based on photon-counting detector

    Fig. 2. Spectral CT system based on photon-counting detector

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    Reconstructed spectral CT images of the six energy ranges at one positionThe energy ranges from (a) to (f) are 25~90, 30~90, 35~90, 40~90, 45~90 and 50~90 keV; The noise in reconstructed spectral CT images of different energy ranges are distinct

    Fig. 3. Reconstructed spectral CT images of the six energy ranges at one position
    The energy ranges from (a) to (f) are 25~90, 30~90, 35~90, 40~90, 45~90 and 50~90 keV; The noise in reconstructed spectral CT images of different energy ranges are distinct

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    Reference images of the six energy ranges at one positionThe energy ranges from (a) to (f) are 25~90, 30~90, 35~90, 40~90, 45~90 and 50~90 keV

    Fig. 4. Reference images of the six energy ranges at one position
    The energy ranges from (a) to (f) are 25~90, 30~90, 35~90, 40~90, 45~90 and 50~90 keV

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    Denoising results of DNCNN, REDCNN and FCPRN in three energy bins (25~90, 35~90 and 45~90 keV)

    Fig. 5. Denoising results of DNCNN, REDCNN and FCPRN in three energy bins (25~90, 35~90 and 45~90 keV)

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    Details of denoising based on DNCNN, REDCNN and FCPRN in the three energy bins (25~90, 35~90 and 45~90 keV)

    Fig. 6. Details of denoising based on DNCNN, REDCNN and FCPRN in the three energy bins (25~90, 35~90 and 45~90 keV)

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    • Table 1. The structure and parameters of FCPRN

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      Table 1. The structure and parameters of FCPRN

      LayersOut Dimension
      Input3
      3×3 Convolution48
      PR-blocks(8 layers)+TD(2 layers)64
      PR-blocks(8 layers)+TD(2 layers)80
      PR-blocks (8 layers)+TD(2 layers)96
      PR-blocks (8 layers)+TD(2 layers)112
      PR-blocks (8 layers)112
      PR-blocks (8 layers)112
      TU+PR-blocks (8 layers)96
      TU+PR-blocks (8 layers)80
      TU+PR-blocks (8 layers)64
      TU+PR-blocks (8 layers)48
      3×3Convolution3

    • Table 2. Quantitative results of different networks

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      Table 2. Quantitative results of different networks

      Energy/keVNetworksRMSEPSNRSSIM
      DNCNN0.008 341.663 10.980 1
      25~90REDCNN0.007 642.422 30.995 3
      FCPRN0.006 344.088 10.996 7
      DNCNN0.007 642.412 30.992 7
      30~90REDCNN0.007 242.915 90.992 9
      FCPRN0.005 944.576 80.993 2
      DNCNN0.007 542.541 20.979 3
      35~90REDCNN0.007 142.943 40.992 7
      FCPRN0.006 044.428 70.993 1
      DNCNN0.007 642.393 40.979 7
      40~90REDCNN0.007 342.743 80.992 3
      FCPRN0.006 144.212 30.995 7
      DNCNN0.007 842.180 20.979 8
      45~90REDCNN0.007 542.497 70.983 5
      FCPRN0.006 443.929 50.992 4
      DNCNN0.008 441.535 90.964 2
      50~90REDCNN0.008 141.802 90.983 1
      FCPRN0.007 143.035 60.990 0
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    Xue-zhi REN, Peng HE, Zou-rong LONG, Xiao-dong GUO, Kang AN, Xiao-jie LÜ, Biao WEI, Peng FENG. Research on Spectral CT Image Denoising Via Fully Convolution Pyramid Residual Network[J]. Spectroscopy and Spectral Analysis, 2021, 41(9): 2950

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    Paper Information

    Category: Research Articles

    Received: Jun. 17, 2020

    Accepted: --

    Published Online: Oct. 29, 2021

    The Author Email: REN Xue-zhi (809233433@qq.com)

    DOI:10.3964/j.issn.1000-0593(2021)09-2950-06

    Topics

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