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Acta Optica Sinica, Volume. 38, Issue 8, 0811003(2018)

Weighted NLTV Reconstruction Algorithm Based on Structural Prior Information for Spectral CT

Haijiao Zhang1,2、*, Huihua Kong1,2、*, and Yonggang Sun1,2
Author Affiliations
  • 1 College of Science, North University of China, Taiyuan, Shanxi 0 30051, China
  • 2 Shanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan, Shanxi 0 30051, China;
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
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    References (23)
    Cited By (7)
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    Figures & Tables(12)
    Three transformations apply to head model. (a) TV operator; (b) NLTV operator; (c) RE_NLTV operator

    Fig. 1. Three transformations apply to head model. (a) TV operator; (b) NLTV operator; (c) RE_NLTV operator

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    Energy spectrum of 120 kVp simulated voltages

    Fig. 2. Energy spectrum of 120 kVp simulated voltages

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    Two-dimensional image of the simulation model

    Fig. 3. Two-dimensional image of the simulation model

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    Reference images used in RI_NLTV algorithm. (a) For chapter 3.1; (b) for chapter 3.2

    Fig. 4. Reference images used in RI_NLTV algorithm. (a) For chapter 3.1; (b) for chapter 3.2

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    Reconstructions with different algorithms under high and low noise level. (a) TV algorithm; (b) NLTV algorithm; (c) RE_NLTV algorithm; (d) RI_NLTV algorithm

    Fig. 5. Reconstructions with different algorithms under high and low noise level. (a) TV algorithm; (b) NLTV algorithm; (c) RE_NLTV algorithm; (d) RI_NLTV algorithm

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    Convergence of different algorithms under the low noise level

    Fig. 6. Convergence of different algorithms under the low noise level

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    Reconstructions with different algorithms underthree energy bins. (a) TV algorithm; (b) NLTV algorithm; (c) RE_NLTV algorithm; (d) RI_NLTV algorithm

    Fig. 7. Reconstructions with different algorithms underthree energy bins. (a) TV algorithm; (b) NLTV algorithm; (c) RE_NLTV algorithm; (d) RI_NLTV algorithm

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    Reconstructions with different algorithms under high and low noise level in the mouse model. (a) TV algorithm; (b) NLTV algorithm; (c) RE_NLTV algorithm; (d) RI_NLTV algorithm

    Fig. 8. Reconstructions with different algorithms under high and low noise level in the mouse model. (a) TV algorithm; (b) NLTV algorithm; (c) RE_NLTV algorithm; (d) RI_NLTV algorithm

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    • Table 1. Position and size of the corresponding material in the model

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      Table 1. Position and size of the corresponding material in the model

      ObjectCenter /cmSize /cmMaterial
      1(0.00,0.00)(0.92,0.69)Soft tissue
      2(0.20,0.20)(0.03,0.03)Air
      3(0.00,-0.10)(0.05,0.05)Blood
      4(0.00,0.50)(0.20,0.08)0.3% Iodine+99.7% blood
      5(0.65,0.20)(0.20,0.08)0.2% Kalium+99.8% blood
      6(0.45,-0.38)(0.20,0.08)1% Barium+99% water
      7(-0.45,-0.38)(0.20,0.08)0.3% Ferrum+99.7% blood
      8(-0.65,0.20)(0.20,0.08)10% Calcium+90% water

    • Table 2. Evaluation parameters of reconstruction with different algorithms under high and low noise level

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      Table 2. Evaluation parameters of reconstruction with different algorithms under high and low noise level

      ItemLow noise levelHigh noise level
      TVNLTVRE_NLTVRI_NLTVTVNLTVRE_NLTVRI_NLTV
      SNR19.384518.166718.408621.118317.314617.110017.201219.9054
      MSE8.62×10-51.14×10-41.07×10-45.78×10-51.39×10-41.46×10-41.42×10-47.64×10-4
      NMSD0.10730.12350.12010.08790.13620.13950.13800.1011

    • Table 3. Evaluation parameter of different algorithms under three energy bins

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      Table 3. Evaluation parameter of different algorithms under three energy bins

      AlgorithmBin1Bin2Bin3
      SNRMSENMSDSNRMSENMSDSNRMSENMSD
      TV17.400.00110.134817.712.62×10-40.130217.643.06×10-40.1312
      NLTV16.570.00140.148416.803.22×10-40.144416.793.73×10-40.1448
      RE_NLTV17.060.00120.140317.123.00×10-40.139317.093.48×10-40.1398
      RI_NLTV17.640.00110.131218.192.70×10-40.123218.372.25×10-40.1207

    • Table 4. Evaluation parameters of reconstruction with different algorithms under high and low noise level

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      Table 4. Evaluation parameters of reconstruction with different algorithms under high and low noise level

      ItemLow noise levelHigh noise level
      TVNLTVRE_NLTVRI_NLTVTVNLTVRE_NLTVRI_NLTV
      SNR16.452716.522116.607317.290016.430916.380316.457217.3558
      MSE6.17×10-45.20×10-45.10×10-44.36×10-55.31×10-45.37×10-45.28×10-44.29×10-4
      NMSD0.15040.14920.14780.13660.15080.15170.15040.1356
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    Haijiao Zhang, Huihua Kong, Yonggang Sun. Weighted NLTV Reconstruction Algorithm Based on Structural Prior Information for Spectral CT[J]. Acta Optica Sinica, 2018, 38(8): 0811003

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    Paper Information

    Category: Imaging Systems

    Received: Jan. 2, 2018

    Accepted: Apr. 9, 2018

    Published Online: Sep. 6, 2018

    The Author Email:

    DOI:10.3788/AOS201838.0811003

    Topics

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