Advertisement

A Calibration Method of CBCT Geometric Parameters Based on the Visual Imaging Model

  • Yanli Wan
  • Quan Chen
  • Xingyun Lei
  • Yan Wang
  • Yongxin Chen
  • Hongpu HuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

In this paper, a framework for calibrating and correcting geometric parameters of CBCT based on visual imaging model is designed. Based on this framework, an automatic detection and recognition method for markers and a calibration and correction method for geometric parameters of CBCT under complex conditions are designed. Specifically, an algorithm for detecting and recognizing markers with high robustness and accuracy to rotation, illumination and marking imaging deformation is proposed. The geometric parameters of visual imaging model are estimated by using the corresponding relationship of high-precision markers, and the geometric parameters are optimized and corrected based on the minimum re-projection error.

Keywords

Visual imaging model CBCT Calibration Marker detection Marker recognition 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61703436), and the Fundamental Research Funds for the Central Universities (3332018103).

References

  1. 1.
    Zarepisheh, M., Long, T., Li, N., et al.: A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning. Med. Phys. 41(6), 061711 (2014)CrossRefGoogle Scholar
  2. 2.
    Yu, G., Liang, Y., Yang, G., et al.: Accelerated gradient-based free form deformable registration for online adaptive radiotherapy. Phys. Med. Biol. 60(7), 2765–2783 (2015)CrossRefGoogle Scholar
  3. 3.
    Usui, K., Ichimaru, Y., Okumura, Y., et al.: Dose calculation with a cone beam CT image in image-guided radiation therapy. Radiol. Phys. Technol. 6(1), 107–114 (2013)CrossRefGoogle Scholar
  4. 4.
    Meng, Y., Gong, H., Yang, X.: Online geometric calibration of cone-beam computed tomography for arbitrary imaging objects. IEEE Trans. Med. Imaging 32(2), 278–288 (2013)CrossRefGoogle Scholar
  5. 5.
    Xu, J., Tsui, B.M.W.: An analytical geometric calibration method for circular cone-beam geometry. IEEE Trans. Med. Imaging 32(9), 1731–1744 (2013)CrossRefGoogle Scholar
  6. 6.
    Zhang, F., Du, J., Jiang, H., et al.: Iterative geometric calibration in circular cone-beam computed tomography. Int. J. Light Electron Opt. 125(11), 2509–2514 (2014)CrossRefGoogle Scholar
  7. 7.
    Schneider, C.-T.: Mehrbildzuordnung. Dimension, Optische Formerfassung, pp. 61–67. DGZfP, Berlin (1991)Google Scholar
  8. 8.
    Xia, R.B., Zhao, J.B., Liu, W.J., Wu, J.H., Fu, S.P., Jiang, J., Li, J.Z.: A robust recognition algorithm for encoded targets in close-range photogrammetry. J. Inf. Sci. Eng. 27(6), 197–209 (2011)Google Scholar
  9. 9.
    Matei, B., Meer, P.: A general method for errors-in-variables problems in computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (2000)Google Scholar
  10. 10.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  11. 11.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yanli Wan
    • 1
  • Quan Chen
    • 1
  • Xingyun Lei
    • 1
  • Yan Wang
    • 1
  • Yongxin Chen
    • 1
  • Hongpu Hu
    • 1
    Email author
  1. 1.Institute of Medical InformationChinese Academy of Medical SciencesBeijingChina

Personalised recommendations