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Biomedical Engineering Letters

, Volume 9, Issue 3, pp 375–385 | Cite as

U-net based metal segmentation on projection domain for metal artifact reduction in dental CT

  • Mohamed A. A. Hegazy
  • Myung Hye Cho
  • Min Hyoung Cho
  • Soo Yeol LeeEmail author
Original Article
  • 86 Downloads

Abstract

Unlike medical computed tomography (CT), dental CT often suffers from severe metal artifacts stemming from high-density materials employed for dental prostheses. Despite the many metal artifact reduction (MAR) methods available for medical CT, those methods do not sufficiently reduce metal artifacts in dental CT images because MAR performance is often compromised by the enamel layer of teeth, whose X-ray attenuation coefficient is not so different from that of prosthetic materials. We propose a deep learning-based metal segmentation method on the projection domain to improve MAR performance in dental CT. We adopted a simplified U-net for metal segmentation on the projection domain without using any information from the metal-artifacts-corrupted CT images. After training the network with the projection data of five patients, we segmented the metal objects on the projection data of other patients using the trained network parameters. With the segmentation results, we corrected the projection data by applying region filling inside the segmented region. We fused two CT images, one from the corrected projection data and the other from the original raw projection data, and then we forward-projected the fused CT image to get the fused projection data. To get the final corrected projection data, we replaced the metal regions in the original projection data with the ones in the fused projection data. To evaluate the efficacy of the proposed segmentation method on MAR, we compared the MAR performance of the proposed segmentation method with a conventional MAR method based on metal segmentation on the CT image domain. For the MAR performance evaluation, we considered the three primary MAR performance metrics: the relative error (REL), the sum of square difference (SSD), and the normalized absolute difference (NAD). The proposed segmentation method improved MAR performances by around 5.7% for REL, 6.8% for SSD, and 8.2% for NAD. The proposed metal segmentation method on the projection domain showed better MAR performance than the conventional segmentation on the CT image domain. We expect that the proposed segmentation method can improve the performance of the existing MAR methods that are based on metal segmentation on the CT image domain.

Keywords

Metal artifact reduction Metal segmentation Deep learning U-net Dental CT 

Notes

Acknowledgements

This work was supported by the National Research Foundation (NRF) of Korea funded by the Korean government (No: 2015M2A2A7A03043177).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors. (The IRB has reviewed that this work is not applicable to the IRB’ review).

References

  1. 1.
    Kataoka ML, Hochman MG, Rodriguez EK, Lin PJP, Kubo S, Raptopolous VD. A review of factors that affect artifact from metallic hardware on multi-row detector computed tomography. Curr Probl Diagn Radiol. 2010;39(4):125–36.CrossRefGoogle Scholar
  2. 2.
    De Mm B, Nuyts J, Dupont P, Marchai G, Suetttis P. Metal streak artifacts in x-ray computed tomography: a simulation study. IEEE Trans Nucl Sci. 1999;46(3 part 2):691–6.Google Scholar
  3. 3.
    Boas FE, Fleischmann D. CT artifacts: causes and reduction techniques. Imaging Med. 2012;4(2):229–40.CrossRefGoogle Scholar
  4. 4.
    De Man B, Nuyts J, Dupont P, Marchal G, Suetens P. Reduction of metal streak artifacts in x-ray computed tomography using a transmission maximum a posteriori algorithm. IEEE Trans Nucl Sci. 2000;47(3):977–81.CrossRefGoogle Scholar
  5. 5.
    Zhang X, Wang J, Xing L. Metal artifact reduction in x-ray computed tomography (CT) by constrained optimization. Med Phys. 2011;38(2):701–11.CrossRefGoogle Scholar
  6. 6.
    Williamson JF, Whiting BR, Benac J, Murphy RJ, Blaine GJ, O’Sullivan JA, et al. Prospects for quantitative computed tomography imaging in the presence of foreign metal bodies using statistical image reconstruction. Med Phys. 2002;29(10):2404–18.CrossRefGoogle Scholar
  7. 7.
    Wang G, Snyder DL, O’Sullivan JA, Vannier MW. Iterative deblurring for CT metal artifact reduction. IEEE Trans Med Imaging. 1996;15(5):657–64.CrossRefGoogle Scholar
  8. 8.
    Wei J, Sandison GA, Chen L, Liang Y, Xu LX. X-ray CT high-density artefact suppression in cryosurgery. Phys Med Biol. 2002;47(24):N319–26.CrossRefGoogle Scholar
  9. 9.
    Yazdia M, Gingras L, Beaulieu L. An adaptive approach to metal artifact reduction in helical computed tomography for radiation therapy treatment planning: experimental and clinical studies. Int J Radiat Oncol Biol Phys. 2005;62(4):1224–31.CrossRefGoogle Scholar
  10. 10.
    Jeong KY, Ra JB. Metal artifact reduction based on sinogram correction in CT. IEEE Nucl Sci Symp Conf Rec. 2009;4:3480–3.Google Scholar
  11. 11.
    Saint Olive C, Kaus MR, Pekar V, Eck K, Spies L. Segmentation-aided adaptive filtering for metal artifact reduction in radio-therapeutic CT images. Proc SPIE. 2004;5370:1991.CrossRefGoogle Scholar
  12. 12.
    Glover GH, Pelc NJ. An algorithm for the reduction of metal clip artifacts in CT reconstructions. Med Phys. 1981;8(6):799–807.CrossRefGoogle Scholar
  13. 13.
    Kalender WA, Hebel R, Ebersberger J. Reduction of CT artifacts caused by metallic implants. Radiology. 1987;164(2):576–7.CrossRefGoogle Scholar
  14. 14.
    Bal M, Spies L. Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering. Med Phys. 2006;33(8):2852–9.CrossRefGoogle Scholar
  15. 15.
    Hinderling T, Ruegsegger P, Anliker M, Dietschi C. Computed tomography reconstruction from hollow projections: an application to in vivo evaluation of artificial hip joints. J Comput Assist Tomogr. 1979;3(1):52–7.CrossRefGoogle Scholar
  16. 16.
    Wu J, Shih C-T, Chang S-J, Huang T-C, Sun J-Y, Wu T-H. Metal artifact reduction algorithm based on model images and spatial information. Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip. 2011;652(1):602–5.CrossRefGoogle Scholar
  17. 17.
    Prell D, Kyriakou Y, Beister M, Kalender WA. A novel forward projection-based metal artifact reduction method for flat-detector computed tomography. Phys Med Biol. 2009;54(21):6575–91.CrossRefGoogle Scholar
  18. 18.
    Prell D, Kyriakou Y, Struffert T, Dörfler A, Kalender WA. Metal artifact reduction for clipping and coiling in interventional C-Arm CT. Am J Neuroradiol. 2010;31(4):634–9.CrossRefGoogle Scholar
  19. 19.
    Tuy HK. A post-processing algorithm to reduce metallic clip artifacts in CT images. Eur Radiol. 1993;3(2):129–34.MathSciNetCrossRefGoogle Scholar
  20. 20.
    Crawford CR, Colsher JG, Pelc NJ, Lonn AHR. High speed reprojection and its applications. Med Imaging II. 1988;0914:311.CrossRefGoogle Scholar
  21. 21.
    Tohnak S, Mehnert AJH, Mahoney M, Crozier S. Dental CT metal artefact reduction based on sequential substitution. Dentomaxillofacial Radiol. 2011;40(3):184–90.CrossRefGoogle Scholar
  22. 22.
    Yazdi M, Lari MA, Bernier G, Beaulieu L. An opposite view data replacement approach for reducing artifacts due to metallic dental objects. Med Phys. 2011;38(4):2275–81.CrossRefGoogle Scholar
  23. 23.
    Karimi S, Cosman P, Wald C, Martz H. Segmentation of artifacts and anatomy in CT metal artifact reduction. Med Phys. 2012;39(10):5857–68.CrossRefGoogle Scholar
  24. 24.
    Kidoh M, Nakaura T, Nakamura S, Tokuyasu S, Osakabe H, Harada K, et al. Reduction of dental metallic artefacts in CT: value of a newly developed algorithm for metal artefact reduction (O-MAR). Clin Radiol. 2014;69(1):e11–6.CrossRefGoogle Scholar
  25. 25.
    Chen H, Zhang Y, Kalra MK, Lin F, Chen Y, Liao P, et al. Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN). IEEE Trans Med Imaging. 2017;36(12):2524–35.CrossRefGoogle Scholar
  26. 26.
    Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, et al. Low-dose CT denoising with convolutional neural network. In: IEEE 14th international symposium on biomedical imaging (ISBI 2017). Melbourne: IEEE; 2017, p. 143–6.Google Scholar
  27. 27.
    Xie S, Zheng X, Chen Y, Xie L, Liu J, Zhang Y, et al. Artifact removal using improved GoogLeNet for sparse-view CT reconstruction. Sci Rep. 2018;8(1):6700.CrossRefGoogle Scholar
  28. 28.
    Zhang C, Xing Y, Spie P. CT artifact reduction via U-net CNN. Proc SPIE. 2018;10574:62.Google Scholar
  29. 29.
    Gjesteby L, Yang Q, Xi Y, Claus B, Jin Y, Man B De, et al. Reducing metal streak artifacts in CT images via deep learning: pilot results. In: 14th International meeting on fully three-dimensional image reconstruction in radiology and nuclear medicine, vol. 14. 2017. p. 611–4.Google Scholar
  30. 30.
    Zhang H, Li L, Qiao K, Wang L, Yan B, Li L, et al. Image prediction for limited-angle tomography via deep learning with convolutional neural network. CoRR. 2016. abs/1607.0.Google Scholar
  31. 31.
    Yu H, Zhang Y, Chu Y. Reduction of metal artifacts in x-ray CT images using a convolutional neural network. Dev X-Ray Tomogr XI. 2017;10391:30.CrossRefGoogle Scholar
  32. 32.
    Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention (MICCAI 2016). 2016. p. 424–32.Google Scholar
  33. 33.
    Zhu Q, Du B, Turkbey B, Choyke PL, Yan P. Deeply-supervised CNN for prostate segmentation. In: International joint conference on neural networks. Anchorage; 2017. p. 178–84.Google Scholar
  34. 34.
    Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Search results lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), vol. 9351. 2015. p. 234–41.Google Scholar
  35. 35.
    Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297–302.CrossRefGoogle Scholar
  36. 36.
    Kingma DP, Ba J. ADAM: a method for stochastic optimization. In: International conference for learning representations (ICLR 2015). 2015.Google Scholar
  37. 37.
    Nasr G, Badr E, Joun C. Cross entropy error function in neural networks: forecasting gasoline demand. In: FLAIRS conference. 2002. p. 381–4.Google Scholar
  38. 38.
    Hegazy MAA, Eldib ME, Hernandez D, Cho MH, Cho MH, Lee SY. Dual-energy-based metal segmentation for metal artifact reduction in dental computed tomography. Med Phys. 2018;45(2):714–24.CrossRefGoogle Scholar
  39. 39.
    Jaccard P. Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bull Soc Vaud Des Sci Nat. 1901;37:241–72.Google Scholar
  40. 40.
    Feldkamp LA, Davis LC, Kress JW. Practical cone-beam algorithm. J Opt Soc Am A. 1984;1(6):612.CrossRefGoogle Scholar
  41. 41.
    Kratz B, Ens S, Müller J, Buzug TM. Reference-free ground truth metric for metal artifact evaluation in CT images. Med Phys. 2011;38(7):4321–8.CrossRefGoogle Scholar
  42. 42.
    Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. 2015.Google Scholar

Copyright information

© Korean Society of Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringKyung Hee UniversityYonginKorea

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