Abstract
Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain. However, they are inherently local in the sinogram domain. Thus, one possible approach to MAR is to exploit the latter characteristic by learning to reduce artifacts in the sinogram. However, if we directly treat the metal-affected regions in sinogram as missing and replace them with the surrogate data generated by a neural network, the artifact-reduced CT images tend to be over-smoothed and distorted since fine-grained details within the metal-affected regions are completely ignored. In this work, we provide analytical investigation to the issue and propose to address the problem by (1) retaining the metal-affected regions in sinogram and (2) replacing the binarized metal trace with the metal mask projection such that the geometry information of metal implants is encoded. Extensive experiments on simulated datasets and expert evaluations on clinical images demonstrate that our novel network yields anatomically more precise artifact-reduced images than the state-of-the-art approaches, especially when metallic objects are large.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chang, Z., Ye, D.H., Srivastava, S., Thibault, J.B., Sauer, K., Bouman, C.: Prior-guided metal artifact reduction for iterative x-ray computed tomography. IEEE Trans. Med. Imaging 38(6), 1532–1542 (2018)
Ghani, M.U., Karl, W.C.: Fast enhanced CT metal artifact reduction using data domain deep learning. IEEE Trans. Comput. Imaging 6, 181–193 (2019)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Jin, P., Bouman, C.A., Sauer, K.D.: A model-based image reconstruction algorithm with simultaneous beam hardening correction for x-ray CT. IEEE Trans. Comput. Imaging 1(3), 200–216 (2015)
Kalender, W.A., Hebel, R., Ebersberger, J.: Reduction of CT artifacts caused by metallic implants. Radiology 164(2), 576–577 (1987)
Karimi, S., Martz, H., Cosman, P.: Metal artifact reduction for CT-based luggage screening. J. X-ray Sci. Technol. 23(4), 435–451 (2015)
Liao, H., et al.: Generative mask pyramid network for CT/CBCT metal artifact reduction with joint projection-sinogram correction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 77–85. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_9
Lin, W.A., et al.: DudoNet: dual domain network for CT metal artifact reduction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10512–10521 (2019)
Meyer, E., Raupach, R., Lell, M., Schmidt, B., Kachelrieß, M.: Normalized metal artifact reduction (NMAR) in computed tomography. Med. phys. 37(10), 5482–5493 (2010)
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_28
Soltanian-Zadeh, H., Windham, J.P., Soltanianzadeh, J.: CT artifact correction: an image-processing approach. In: Medical Imaging 1996: Image Processing, vol. 2710, pp. 477–485. International Society for Optics and Photonics (1996)
Wang, J., Zhao, Y., Noble, J.H., Dawant, B.M.: Conditional generative adversarial networks for metal artifact reduction in CT images of the ear. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_1
Yan, K., et al.: Deep lesion graphs in the wild: relationship learning and organization of significant radiology image findings in a diverse large-scale lesion database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9261–9270 (2018)
Zhang, Y., Yu, H.: Convolutional neural network based metal artifact reduction in x-ray computed tomography. IEEE Trans. Med. Imaging 37(6), 1370–1381 (2018)
Zhou, S.K.: Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches. Academic Press (2015)
Zhou, S.K., Greenspan, H., Shen, D.: Deep Learning for Medical Image Analysis. Academic Press (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Lyu, Y., Lin, WA., Liao, H., Lu, J., Zhou, S.K. (2020). Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-59713-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59712-2
Online ISBN: 978-3-030-59713-9
eBook Packages: Computer ScienceComputer Science (R0)
