Journal of the Korean Physical Society

, Volume 67, Issue 1, pp 180–188 | Cite as

Development of a new metal artifact reduction algorithm by using an edge preserving method for CBCT imaging

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Abstract

CT (computed tomography) images, metal materials such as tooth supplements or surgical clips can cause metal artifact and degrade image quality. In severe cases, this may lead to misdiagnosis. In this research, we developed a new MAR (metal artifact reduction) algorithm by using an edge preserving filter and the MATLAB program (Mathworks, version R2012a). The proposed algorithm consists of 6 steps: image reconstruction from projection data, metal segmentation, forward projection, interpolation, applied edge preserving smoothing filter, and new image reconstruction. For an evaluation of the proposed algorithm, we obtained both numerical simulation data and data for a Rando phantom. In the numerical simulation data, four metal regions were added into the Shepp Logan phantom for metal artifacts. The projection data of the metal-inserted Rando phantom were obtained by using a prototype CBCT scanner manufactured by medical engineering and medical physics (MEMP) laboratory research group in medical science at Ewha Womans University. After these had been adopted the proposed algorithm was performed, and the result were compared with the original image (with metal artifact without correction) and with a corrected image based on linear interpolation. Both visual and quantitative evaluations were done. Compared with the original image with metal artifacts and with the image corrected by using linear interpolation, both the numerical and the experimental phantom data demonstrated that the proposed algorithm reduced the metal artifact. In conclusion, the evaluation in this research showed that the proposed algorithm outperformed the interpolation based MAR algorithm. If an optimization and a stability evaluation of the proposed algorithm can be performed, the developed algorithm is expected to be an effective tool for eliminating metal artifacts even in commercial CT systems.

Keywords

MAR algorithm Metal artifact reduction Image reconstruction 

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References

  1. [1]
    J. Hsieh, Computed tomography: principles, design, artifacts and recent advances, 2nd edition, (SPIE Press, Bellingham, Wash, USA, 2009).Google Scholar
  2. [2]
    T. M. Buzug, Computed Tomography from Photon Statistics to Modern Cone-Beam CT (Springer, Germany, 2008).Google Scholar
  3. [3]
    J. F. Barrett and N. Keat, RadioGraphics 24, 1679 (2004).CrossRefGoogle Scholar
  4. [4]
    W. A. Kalender, R. Hebele and J. Ebersberger, Radiology 164, 576 (1987).CrossRefGoogle Scholar
  5. [5]
    S. Zhao et al., IEEE Trans. Medical Imaging 19, 1238 (2000).CrossRefGoogle Scholar
  6. [6]
    M. Yazdia, L. Gingras and L. Beaulieu, Int. J. Radiat. Oncol. Biol. Phys. 64, 1224 (2005).CrossRefGoogle Scholar
  7. [7]
    M. Bal and L. Spies, Med. Phys. 33, 2852 (2006).CrossRefGoogle Scholar
  8. [8]
    A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE Press, 1988).Google Scholar
  9. [9]
    T. G. Feeman, The Mathematics of Medical Imaging; A Beginner’s Guide (Springer, Germany, 2010).CrossRefMATHGoogle Scholar
  10. [10]
    F. O’Sullivan, Annals Stat. 23, 1267 (1995).MathSciNetCrossRefGoogle Scholar
  11. [11]
    E. Levitan and G. T. Herman, IEEE Trans. Medical Imaging MI-6, 185 (1987).CrossRefGoogle Scholar
  12. [12]
    L. Shepp and Y. Vardi, IEEE Trans. Medical Imaging 1, 113 (1982).CrossRefGoogle Scholar
  13. [13]
    G. Wang et al., IEEE Trans. Medical Imaging 15, 657 (1996).CrossRefGoogle Scholar
  14. [14]
    B. De Man et al., IEEE Transactions on Nuc. Sci. 47, 977 (2000).ADSCrossRefGoogle Scholar
  15. [15]
    B. De Man et al., IEEE Trans. Med, Imaging 20, 999 (2001).CrossRefGoogle Scholar
  16. [16]
    S. Larry and B. F. Logan, IEEE Trans. on Nuc. Sci. NS-21, 21 (1974).Google Scholar
  17. [17]
    Y. Zhang et al., Comp. and Math. Meth. In Med. 2011, 1 (2011).CrossRefGoogle Scholar
  18. [18]
    Online: http://www.phantomlab.com/products/Rando.php.Google Scholar
  19. [19]
    J. Wei et al., Phys. Med. Biol. 49, 5407 (2004).CrossRefGoogle Scholar
  20. [20]
    J. Weickert and H. Scharr, Visual Comm. and Image Rep. 13, 103 (2002).CrossRefGoogle Scholar
  21. [21]
    D. Kroon and C. H. Slump, IEEE-EMBS Benelux Chapter Symposium, Enschede (2009).Google Scholar
  22. [22]
    D.-J. Kroon et al., MICCAI 13, 221 (2010).Google Scholar
  23. [23]
    F. E. Boas and D. Fleischmann, Radiology 256 894 (2011).CrossRefGoogle Scholar
  24. [24]
    Y. Zhang et al., Int. J. Radiation Oncology Biol.Phys. 67, 924 (2007).CrossRefGoogle Scholar
  25. [25]
    W. J. H Veldkamp et al., Med. Phys. 37, 620 (2010).CrossRefGoogle Scholar
  26. [26]
    H. Yu et al., Acad. Radiol. 14, 495 (2007).CrossRefGoogle Scholar
  27. [27]
    S. H. Ahn et al., Korean Phys. Soc. 64, 1220 (2014).ADSCrossRefGoogle Scholar

Copyright information

© The Korean Physical Society 2015

Authors and Affiliations

  1. 1.Department of Medical ScienceEwha Womans UniversitySeoulKorea
  2. 2.Yonsei Institute of Convergence TechnologyYonsei UniversityIncheonKorea
  3. 3.Global Top 5 Research ProgramEwha Womans UniversitySeoulKorea

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