Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Acta Optica Sinica, Volume. 42, Issue 11, 1134025(2022)

Analytical Reconstruction for Source Translation Scanning Computed Tomography Based on Derivative-Hilbert Transform-Back projection

Wenjie Ge1, Haijun Yu2, jie Chen2, Song Ni1, and Fenglin Liu1,2,3、*
Author Affiliations
  • 1College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
  • 2Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
  • 3Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
    Equations (0)
    References (32)
    Cited By (2)
    Tools
    Paper Information
    Topics
    Figures & Tables(14)
    Illustration of STCT scanning mode

    Fig. 1. Illustration of STCT scanning mode

    Download full size
    Planar geometric model of STCT

    Fig. 2. Planar geometric model of STCT

    Download full size
    Geometric model of mSTCT[13]

    Fig. 3. Geometric model of mSTCT[13]

    Download full size
    Data redundancy analysis for mSTCT

    Fig. 4. Data redundancy analysis for mSTCT

    Download full size
    Reconstruction process by mSTCT-DHB algorithm. (a) Original image; (b) image reconstructed by using one segment of STCT projection data; (c) image reconstructed by using two segments of STCT projection data; (d) image reconstructed by using three segments of STCT projection data; (e) image reconstructed by using four segments of STCT projection data; (f) image reconstructed by using five segments of STCT projection data

    Fig. 5. Reconstruction process by mSTCT-DHB algorithm. (a) Original image; (b) image reconstructed by using one segment of STCT projection data; (c) image reconstructed by using two segments of STCT projection data; (d) image reconstructed by using three segments of STCT projection data; (e) image reconstructed by using four segments of STCT projection data; (f) image reconstructed by using five segments of STCT projection data

    Download full size
    Comparison of reconstruction results. (a) Original image; (b) reconstruction result of STCT-SIRT algorithm; (c) reconstruction result of STCT-FBP algorithm; (d) reconstruction result of STCT-DHB algorithm; (e) local magnification of Fig. 6 (a); (f) local magnification of Fig. 6 (b); (g) local magnification of Fig. 6 (c); (h) local magnification of Fig. 6 (d)

    Fig. 6. Comparison of reconstruction results. (a) Original image; (b) reconstruction result of STCT-SIRT algorithm; (c) reconstruction result of STCT-FBP algorithm; (d) reconstruction result of STCT-DHB algorithm; (e) local magnification of Fig. 6 (a); (f) local magnification of Fig. 6 (b); (g) local magnification of Fig. 6 (c); (h) local magnification of Fig. 6 (d)

    Download full size
    Sine diagrams of projection data and image of Poisson noise. (a) Original image; (b) image after adding noise; (c) image of Poisson noise

    Fig. 7. Sine diagrams of projection data and image of Poisson noise. (a) Original image; (b) image after adding noise; (c) image of Poisson noise

    Download full size
    Comparison of reconstruction results after adding noise. (a) Original image; (b) reconstruction result of STCT-SIRT algorithm; (c) reconstruction result of STCT-FBP algorithm; (d) reconstruction result of STCT-DHB algorithm; (e) local magnification of Fig. 8 (a); (f) local magnification of Fig. 8 (b); (g) local magnification of Fig. 8 (c); (h) local magnification of Fig. 8 (d)

    Fig. 8. Comparison of reconstruction results after adding noise. (a) Original image; (b) reconstruction result of STCT-SIRT algorithm; (c) reconstruction result of STCT-FBP algorithm; (d) reconstruction result of STCT-DHB algorithm; (e) local magnification of Fig. 8 (a); (f) local magnification of Fig. 8 (b); (g) local magnification of Fig. 8 (c); (h) local magnification of Fig. 8 (d)

    Download full size
    Profiles along 370th row of images reconstructed by different algorithms

    Fig. 9. Profiles along 370th row of images reconstructed by different algorithms

    Download full size
    Experimental system of STCT

    Fig. 10. Experimental system of STCT

    Download full size
    Comparison of reconstruction results of actual experiment. (a) Reconstruction result of STCT-SIRT algorithm; (b) reconstruction result of STCT-FBP algorithm; (c) reconstruction result of STCT-DHB algorithm; (d) local magnification of Fig. 11(a); (e) local magnification of Fig. 11(b); (f) local magnification of Fig. 11(c)

    Fig. 11. Comparison of reconstruction results of actual experiment. (a) Reconstruction result of STCT-SIRT algorithm; (b) reconstruction result of STCT-FBP algorithm; (c) reconstruction result of STCT-DHB algorithm; (d) local magnification of Fig. 11(a); (e) local magnification of Fig. 11(b); (f) local magnification of Fig. 11(c)

    Download full size
    Profiles along 370th row of images reconstructed by different algorithms in actual experiment

    Fig. 12. Profiles along 370th row of images reconstructed by different algorithms in actual experiment

    Download full size

    • Table 1. Parameters of numerical simulation

      View table

      Table 1. Parameters of numerical simulation

      ParameterValue
      Detector pixel size /mm0.127
      Detector array length /pixel1024
      Number of sampling points3201
      Source translation distance 2S /mm16
      L /mm15
      H /mm190
      Reconstruction matrix size512×512

    • Table 2. Quantitative metrics of images reconstructed by different algorithms

      View table

      Table 2. Quantitative metrics of images reconstructed by different algorithms

      MetricSTCT-SIRTSTCT-FBPSTCT-DHB
      RMSE0.04140.09400.0518
      PSNR29.617323.241127.0629
      SSIM0.89280.70400.8437
    Tools

    Get Citation

    Copy Citation Text

    Wenjie Ge, Haijun Yu, jie Chen, Song Ni, Fenglin Liu. Analytical Reconstruction for Source Translation Scanning Computed Tomography Based on Derivative-Hilbert Transform-Back projection[J]. Acta Optica Sinica, 2022, 42(11): 1134025

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: X-Ray Optics

    Received: Nov. 19, 2021

    Accepted: Jan. 13, 2022

    Published Online: Jun. 3, 2022

    The Author Email: Liu Fenglin (liufl@cqu.edu.cn)

    DOI:10.3788/AOS202242.1134025

    Topics

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