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Methods of Preprocessing Tomographic Images Taking into Account the Thermal Instability of the X-ray Tube

  • A. S. IngachevaEmail author
  • A. B. Buzmakov
Physical and Engineering Fundamentals of Microelectronics and Optoelectronics
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Abstract

For correct numerical interpretation of tomographic images, i.e., estimates of the attenuation coefficients of objects, it is important to obtain reconstruction of high quality, which depends directly on the methods of processing experimental data. Data processing flow begins with its preparation for the application of the reconstruction algorithm. The necessary part of data processing contains the subtraction of the black field, normalization considering empty data, and taking logarithm. This part is not sufficient for obtaining high-quality reconstruction when working with real data since it is not ideal. Real data include noise and distortions due to changes in the setup geometrical parameters during the experiment. We have analyzed two possible types of data distortions during experiment and suggested corrections for them. The first one corrects thermal shifts regarding beam decentralization, and the second eliminates the effect of the polychromatic nature of X-ray radiation on the results of tomographic reconstruction. These methods were tested with both real and synthetic data. Both synthetic and real experiments show that suggested methods improve the reconstruction quality. In real experiments, the level of agreement between the automatic parameter adjustment and experts is about 90%.

Keywords

computed tomography artifacts beam hardening invariance of the Radon transform drift of the center of the X-ray beam 

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Notes

Acknowledgements

This work was supported by the State Task of the Ministry of Higher Education and Science for the Crystallography and Photonics Federal Research Center, RAS (obtaining experimental data) and the Russian Science Foundation (development of methods for tomographic data correction, project No. 17-29-03492).

References

  1. 1.
    S. Sawall, M. Knaup, and M. Kachelrie, “A Robust Geometry Estimation Method for Spiral, Sequential and Circular Cone-Beam Micro-CT,” Med. Phys. 39(9), 5384–5392 (2012). DOI:  https://doi.org/10.1118/1.4739506.CrossRefGoogle Scholar
  2. 2.
    Y. Cho, D. J. Moseley, J. H. Siewerdsen, and D. A. Jaffray, “Accurate Technique for Geometric Calibration of Cone-Beam CT Systems,” Med. Phys. 32(4), 968–983 (2005).CrossRefGoogle Scholar
  3. 3.
    A. Rougée, C. Picard, Y. Trousset, and C. Ponchut, “Geometrical Calibration for 3D X-ray Imaging,” Proc. SPIE. 1897, 161–169 (1993).CrossRefGoogle Scholar
  4. 4.
    M. Yang, J. Sun, and G. Wang, “A New Method of Measuring the Origin of the 3D-CT Scanning System Projection Coordinates with High Accuracy,” J. Shanghai Jiaotong Univers. 42(4), 590–593 (2008).Google Scholar
  5. 5.
    K. Yang, A. L. C. Kwan, D. F. Miller, and J. M. Boone, “A Geometric Calibration Method for Cone Beam CT Systems,” Med. Phys. 3(6), 1695–1700 (2006).CrossRefGoogle Scholar
  6. 6.
    S. Luo, B. Zhang, L. Wang, et al., “Correction of Geometric Artifacts on Cone-Beam CT System,” in Proc. of the 3rd Intern. Congress on Image and Signal Processing (CISP 2010), Yantai, China, 16–18 Oct., 2010, Vol. 3, pp. 1226–1230.CrossRefGoogle Scholar
  7. 7.
    C. Debbeler, N. Maass, M. Elter, et al., “A New CT Rawdata Redundancy Measure Applied to Automated Misalignment Correction,” in Proc. of the 12th Intern. Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, California, USA, 16–21 June, 2013, Vol. 1, pp. 264–267.Google Scholar
  8. 8.
    N. Maass, F. Dennerlein, A. Aichert, and A. Maier, “Geometrical Jitter Correction in Computed Tomography,” in Proc. of the 3rd Intern. Conf. on Image Formation in X-ray Computed Tomography, Salt Lake City, USA, 22–25 June, 2014, Vol. 1, pp. 338–342.Google Scholar
  9. 9.
    D. Panetta, N. Belcari, A. Del Guerra, and S. Moehrs, “An Optimization-Based Method for Geometrical Calibration in Cone-Beam CT without Dedicated Phantoms,” Phys. Med. Biol. 53(14), 3841–3861 (2008). DOI:  https://doi.org/10.1088/0031-9155/53/14/009.CrossRefGoogle Scholar
  10. 10.
    P. L. Salmon, X. Liu, and A. Sasov, “A Post-Scan Method for Correcting Artefacts of Slow Geometry Changes during Micro-Tomographic Scans,” J. X-Ray Sci. Technol. 17(2), 161–174 (2009). DOI:  https://doi.org/10.3233/XST-2009-0220.Google Scholar
  11. 11.
    A. Sasov, X. Liu, and P. L. Salmon, “Compensation of Mechanical Inaccuracies in Micro-CT and Nano-CT,” Proc. SPIE. 7078, 70781C (2008). DOI:  https://doi.org/10.1117/12.793212.CrossRefGoogle Scholar
  12. 12.
    R. J. Jennings, “A Method for Comparing Beam-Hardening Filter Materials for Diagnostic Radiology,” Med. Phys. 15(4), 588–599 (1988).CrossRefGoogle Scholar
  13. 13.
    S. Chen, X. Xi, L. Li, et al., “A Filter Design Method for Beam Hardening Correction in Middle-Energy X-ray Computed Tomography,” Proc. SPIE. 10033 (2016). DOI:  https://doi.org/10.1117/12.2245152.
  14. 14.
    G. T. Herman, “Correction for Beam Hardening in Computer Tomography,” Phys. Med. Biol. 24(1), 81–106 (1979).CrossRefGoogle Scholar
  15. 15.
    P. Hammersberg and M. Mengerd, “Correction for Beam Hardening Artefacts in Computerised Tomography,” J. X-Ray Sci. Technol. 8(1), 75–93 (1998).Google Scholar
  16. 16.
    L. Yu, S. Leng and C. H. McCollough, “Dual-Energy CT-Based Monochromatic Imaging,” Am. J. Roentgenol. 199, S9–S15 (2012).CrossRefGoogle Scholar
  17. 17.
    N. Menvill, Y. Goussard, D. Orban, and G. Soulez, “Reduction of Beam-Hardening Artifacts in X-ray CT,” in Proc. of the 27th Annu. Intern. Conf. IEEE EMBS, Shanghai, China, 1–4 Sept., 2005, Vol. 1, pp. 1865–1868.Google Scholar
  18. 18.
    I. A. Elbarki and J. A. Fessler, “Statistical Image Reconstruction for Polyenergetic X-ray Computed Tomography,” IEEE Trans. Med. Imag. 21(2), 89–99 (2002).CrossRefGoogle Scholar
  19. 19.
    P. Jin, C. A. Bouman, and K. D. Sauer, “A Model-Based Image Reconstruction Algorithm with Simultaneous Beam Hardening Correction for X-ray CT,” IEEE Trans. Computat. Imag. 1, 200–216 (2015).MathSciNetCrossRefGoogle Scholar
  20. 20.
    W. Van Aarle, W. J. Palenstijn, J. Cant, et al., “Fast and Flexible X-ray Tomography using the ASTRA Toolbox,” Opt. Express. 24(22), 25129–25147 (2016).CrossRefGoogle Scholar
  21. 21.
    W. Van Aarle, W. J. Palenstijn, J. De Beenhouwer, et al., “The ASTRA Toolbox: A Platform for Advanced Algorithm Development in Electron Tomography,” Ultramicroscopy 157, 35–47 (2015).  https://doi.org/10.1016/j.ultramic.2015.05.002.CrossRefGoogle Scholar
  22. 22.
    P. L. Salmon and X. Liu, “MicroCT Bone Densitometry: Context Sensitivity, Beam Hardening Correction and the Effect of Surrounding Media,” J. Sci. Technol. 2, 1–25 (2014).Google Scholar
  23. 23.
    M. Chukalina, A. Ingacheva, A. Buzmakov, et al., “Automatic Beam Hardening Correction for CT Reconstruction,” in Proc. of the 31th Europ. Conf. on Modeling and Simulation, Budapest, Hungary, 23–26 May, 2017, Vol. 1, pp. 270–275.Google Scholar
  24. 24.
    A. Buzmakov, M. Chukalina, D. Nikolaev, et al., “Monochromatic Computed Microtomography Using Laboratory and Synchrotron Sources and X-ray Fluorescence Analysis for Comprehensive Analysis of Structural Changes in Bones,” J. Appl. Cryst. 48, 693–701 (2015).CrossRefGoogle Scholar
  25. 25.
    K. Muller, “Fast and Accurate Three-Dimensional Reconstruction from Cone-Beam Projection Data Using Algebraic Methods,” PhD thesis (Ohio State University, Columbus, 1998).Google Scholar
  26. 26.
    A. Buzmakov, A. Ingacheva, V. Prun, et al., “Analysis of Computer Images in the Presence of Metals,” Proc. SPIE. 10696, 106961B (2015).Google Scholar

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© Allerton Press, Inc. 2019

Authors and Affiliations

  1. 1.Institute for Information Transmission ProblemsRussian Academy of SciencesMoscowRussia
  2. 2.Shubnikov Institute of Crystallography, Crystallography and Photonics Federal Scientific Research CenterRussian Academy of SciencesMoscowRussia

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