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Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction

  • Haofu LiaoEmail author
  • Wei-An Lin
  • Zhimin Huo
  • Levon Vogelsang
  • William J. Sehnert
  • S. Kevin Zhou
  • Jiebo Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

A conventional approach to computed tomography (CT) or cone beam CT (CBCT) metal artifact reduction is to replace the X-ray projection data within the metal trace with synthesized data. However, existing projection or sinogram completion methods cannot always produce anatomically consistent information to fill the metal trace, and thus, when the metallic implant is large, significant secondary artifacts are often introduced. In this work, we propose to replace metal artifact affected regions with anatomically consistent content through joint projection-sinogram correction as well as adversarial learning. To handle the metallic implants of diverse shapes and large sizes, we also propose a novel mask pyramid network that enforces the mask information across the network’s encoding layers and a mask fusion loss that reduces early saturation of adversarial training. Our experimental results show that the proposed projection-sinogram correction designs are effective and our method recovers information from the metal traces better than the state-of-the-art methods.

Notes

Acknowledgement

This work was supported in part by NSF award #1722847, the Morris K. Udall Center of Excellence in Parkinson’s Disease Research by NIH, and the corporate sponsor Carestream.

Supplementary material

490281_1_En_9_MOESM1_ESM.pdf (2.9 mb)
Supplementary material 1 (pdf 2920 KB)

References

  1. 1.
    Gjesteby, L., Yang, Q., Xi, Y., Zhou, Y., Zhang, J., Wang, G.: Deep learning methods to guide ct image reconstruction and reduce metal artifacts. In: SPIE Medical Imaging (2017)Google Scholar
  2. 2.
    Gjesteby, L., et al.: Reducing metal streak artifacts in CT images via deep learning: pilot results. In: The 14th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, pp. 611–614 (2017)Google Scholar
  3. 3.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of CVPR, pp. 1125–1134 (2017)Google Scholar
  4. 4.
    Kalender, W.A., Hebel, R., Ebersberger, J.: Reduction of CT artifacts caused by metallic implants. Radiology 164(2), 576–577 (1987)CrossRefGoogle Scholar
  5. 5.
    Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)Google Scholar
  6. 6.
    Meyer, E., Raupach, R., Lell, M., Schmidt, B., Kachelrieß, M.: Normalized metal artifact reduction in computed tomography. Med. Phys. 37(10), 5482–5493 (2010)CrossRefGoogle Scholar
  7. 7.
    Park, H.S., Chung, Y.E., Lee, S.M., Kim, H.P., Seo, J.K.: Sinogram-consistency learning in CT for metal artifact reduction. arXiv preprint arXiv:1708.00607 (2017)
  8. 8.
    Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. arXiv preprint arXiv:1604.07379 (2016)
  9. 9.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  10. 10.
    Verburg, J.M., Seco, J.: CT metal artifact reduction method correcting for beam hardening and missing projections. Phys. Med. Biol. 57(9), 2803 (2012)CrossRefGoogle Scholar
  11. 11.
    Xu, S., Dang, H.: Deep residual learning enabled metal artifact reduction in CT. In: Medical Imaging 2018: Physics of Medical Imaging, vol. 10573, p. 105733O. International Society for Optics and Photonics (2018)Google Scholar
  12. 12.
    Zhang, Y., Yu, H.: Convolutional neural network based metal artifact reduction in x-ray computed tomography. IEEE Trans. Med. Imaging 37, 1370–1381 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haofu Liao
    • 1
    Email author
  • Wei-An Lin
    • 2
  • Zhimin Huo
    • 4
  • Levon Vogelsang
    • 4
  • William J. Sehnert
    • 4
  • S. Kevin Zhou
    • 3
    • 5
  • Jiebo Luo
    • 1
  1. 1.Department of Computer ScienceUniversity of RochesterRochesterUSA
  2. 2.Department of ECEUniversity of MarylandCollege ParkUSA
  3. 3.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  4. 4.Carestream Health, Inc.RochesterUSA
  5. 5.Peng Cheng LaboratoryShenzhenChina

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