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Laser & Optoelectronics Progress, Volume. 62, Issue 12, 1234001(2025)

Application of Microfocused Cone-Beam CT Sparse Projection Detection Technique Based on Denoising Diffusion Probabilistic Model

Zhengheng Li1、** and Chenyin Ni2、*
Author Affiliations
  • 1School of Electronic and Optical Engineering, Nanjing University of Science & Technology, Nanjing 210094, Jiangsu , China
  • 2School of Physics, Nanjing University of Science & Technology, Nanjing 210094, Jiangsu , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (17)
    Equations (10)
    References (28)
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    Paper Information
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    Figures & Tables(17)
    Illustration of 2D-DWT

    Fig. 1. Illustration of 2D-DWT

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    DDPM network architecture based on 2D-DWT

    Fig. 2. DDPM network architecture based on 2D-DWT

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    High-frequency component recovery module

    Fig. 3. High-frequency component recovery module

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    Dilated resblock

    Fig. 4. Dilated resblock

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    Global algorithm flow

    Fig. 5. Global algorithm flow

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    Microfocus industrial CT equipment in kind

    Fig. 6. Microfocus industrial CT equipment in kind

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    Images for training purposes. (a) Reconstructed images with 1024 projection angles; (b) reconstructed images with 180 projection angles

    Fig. 7. Images for training purposes. (a) Reconstructed images with 1024 projection angles; (b) reconstructed images with 180 projection angles

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    Comparison of different slices and different methods. (a) Original images; (b) sparse scanning images (Ns=180); (c) FBP (Ns=500); (d) SART; (e) GAN; (f) DWT-DDPM

    Fig. 8. Comparison of different slices and different methods. (a) Original images; (b) sparse scanning images (Ns=180); (c) FBP (Ns=500); (d) SART; (e) GAN; (f) DWT-DDPM

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    Partial enlarged comparison images of controlled experiment. (a) Original images; (b) sparse scanning images (Ns=180); (c) FBP (Ns=500); (d) SART; (e) GAN; (f) DWT-DDPM

    Fig. 9. Partial enlarged comparison images of controlled experiment. (a) Original images; (b) sparse scanning images (Ns=180); (c) FBP (Ns=500); (d) SART; (e) GAN; (f) DWT-DDPM

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    Grayscale comparison of case1 images

    Fig. 10. Grayscale comparison of case1 images

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    Generalization comparison tests. (a) Original images; (b) sparse scanning images (Ns=120); (c) FBP (Ns=500); (d) SART; (e) GAN; (f) DWT-DDPM

    Fig. 11. Generalization comparison tests. (a) Original images; (b) sparse scanning images (Ns=120); (c) FBP (Ns=500); (d) SART; (e) GAN; (f) DWT-DDPM

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    Partial enlarged comparison images of generalization experiment. (a) Original images; (b) sparse scanning images (Ns=120); (c) FBP (Ns=500); (d) SART; (e) GAN; (f) DWT-DDPM

    Fig. 12. Partial enlarged comparison images of generalization experiment. (a) Original images; (b) sparse scanning images (Ns=120); (c) FBP (Ns=500); (d) SART; (e) GAN; (f) DWT-DDPM

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    Grayscale comparison of case2 images

    Fig. 13. Grayscale comparison of case2 images

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    • Table 1. CT scan parameters

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      Table 1. CT scan parameters

      ParameterValue
      Tube voltage105.00 kV
      Tube current120.00 μA
      Rotate angle30°
      Projection image width1536 pixel
      Projection image height1536 pixel
      Focus size10.0 μm
      Focus to object distance36.8691 mm
      Focus to detector distance313.387 mm

    • Table 2. Reconstruction quantitative results of case1 by five different methods

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      Table 2. Reconstruction quantitative results of case1 by five different methods

      MethodPSNR /dBSSIMResolution change rate /%
      Sparse scan13.370.27008.0901
      FBP23.060.37493.3056
      SART22.840.36322.2368
      GAN25.030.93461.8755
      DWT-DDPM39.080.98660.8227

    • Table 3. Reconstruction quantitative results of case3 by five different methods

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      Table 3. Reconstruction quantitative results of case3 by five different methods

      MethodPSNR /dBSSIMResolution change rate /%
      Sparse scan16.570.19619.6918
      FBP30.020.29552.7378
      SART27.690.26842.0539
      GAN31.230.93841.7970
      DWT-DDPM37.660.97600.8602

    • Table 4. Reconstruction time of five different methods

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      Table 4. Reconstruction time of five different methods

      MethodTime /s
      FBP （Ns=180）12.9935
      FBP （Ns=500）14.2896
      SART169.1514
      GAN5.2613
      DWT-DDPM7.3816
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    Zhengheng Li, Chenyin Ni. Application of Microfocused Cone-Beam CT Sparse Projection Detection Technique Based on Denoising Diffusion Probabilistic Model[J]. Laser & Optoelectronics Progress, 2025, 62(12): 1234001

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    Paper Information

    Category: X-Ray Optics

    Received: Oct. 8, 2024

    Accepted: Jan. 7, 2025

    Published Online: Jun. 25, 2025

    The Author Email: Zhengheng Li (li_zhengheng@njust.edu.cn), Chenyin Ni (chenyin.ni@njust.edu.cn)

    DOI:10.3788/LOP242076

    CSTR:32186.14.LOP242076

    Topics

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