Abstract
Metal artifact reduction (MAR) is important to alleviate the impacts of metal implants on clinical diagnosis with CT images. However, enhancing the quality of metal-corrupted image remains a challenge. Although the deep learning-based MAR methods have achieved impressive success, their interpretability and generalizability need further improvement. It is found that metal artifacts mainly concentrate in high frequency, and their distributions in the wavelet domain are significantly different from those in the image domain. Decomposing metal artifacts into different frequency bands is conducive for us to characterize them. Based on these observations, a model is constructed with dual-domain constraints to encode artifacts by utilizing wavelet transform. To facilitate the optimization of the model and improve its interpretability, a novel multi-perspective adaptive iteration network (MAIN) is proposed. Our MAIN is constructed under the guidance of the proximal gradient technique. Moreover, with the usage of the adaptive wavelet module, the network gains better generalization performance. Compared with the representative state-of-the-art deep learning-based MAR methods, the results show that our MAIN significantly outperforms other methods on both of a synthetic and a clinical datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beck, A., Teboulle, M.: A fast iterative Shrinkage-Thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)
Diwakar, M., Kumar, M.: A review on CT image noise and its denoising. Biomed. Signal Process. Control 42, 73–88 (2018)
Ghani, M.U., Karl, W.: Deep learning based sinogram correction for metal artifact reduction. Electron. Imaging 2018, 4721–4728 (2018)
Ghose, S., Singh, N., Singh, P.: Image denoising using deep learning: convolutional neural network. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 511–517 (2020)
Gjesteby, L., et al.: Metal artifact reduction in CT: where are we after four decades? IEEE Access 4, 5826–5849 (2016)
Kalender, W.A., Hebel, R., Ebersberger, J.: Reduction of CT artifacts caused by metallic implants. Radiology 164(2), 576–577 (1987)
Katsura, M., Sato, J., Akahane, M., Kunimatsu, A., Abe, O.: Current and novel techniques for metal artifact reduction at CT: practical guide for radiologists. Radiographics 38(2), 450–461 (2018)
Lin, W.A., et al.: DuDoNet: dual domain network for CT metal artifact reduction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Liu, P., et al.: Deep learning to segment pelvic bones: large-scale CT datasets and baseline models. Int. J. Comput. Assist. Radiol. Surg. 16(5), 749–756 (2021). https://doi.org/10.1007/s11548-021-02363-8
Luo, Y., et al.: Adaptive rectification based adversarial network with spectrum constraint for high-quality PET image synthesis. Med. Image Anal. 77, 102335 (2022)
Lyu, Y., Lin, W.-A., Liao, H., Lu, J., Zhou, S.K.: Encoding metal mask projection for metal artifact reduction in computed tomography. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 147–157. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59713-9_15
Medoff, B.P., Brody, W.R., Nassi, M., Macovski, A.: Iterative convolution backprojection algorithms for image reconstruction from limited data. J. Opt. Soc. Am. 73(11), 1493–1500 (1983)
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)
Pan, J., Zhang, H., Wu, W., Gao, Z., Wu, W.: Multi-domain integrative swin transformer network for sparse-view tomographic reconstruction. Patterns 3(6), 100498 (2022)
Park, H.S., Lee, S.M., Kim, H.P., Seo, J.K., Chung, Y.E.: CT sinogram-consistency learning for metal-induced beam hardening correction. Med. Phys. 45(12), 5376–5384 (2018)
Paszke, A., et al.: Automatic differentiation in PyTorch (2017)
Peng, C., et al.: An irregular metal trace inpainting network for X-ray CT metal artifact reduction. Med. Phys. 47(9), 4087–4100 (2020)
Rodriguez, M.X.B., et al.: Deep adaptive wavelet network. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2020)
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
Rousselle, A., et al.: Metallic implants and CT artefacts in the CTV area: where are we in 2020? Cancer/Radiothérapie 24(6), 658–666 (2020). 31e Congrès national de la Société française de radiothérapie oncologique
Verburg, J.M., Seco, J.: CT metal artifact reduction method correcting for beam hardening and missing projections. Phys. Med. Biol. 57(9), 2803 (2012)
Wang, H., Li, Y., Meng, D., Zheng, Y.: Adaptive convolutional dictionary network for CT metal artifact reduction. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, pp. 1401–1407. International Joint Conferences on Artificial Intelligence Organization (2022)
Wang, H., et al.: InDuDoNet: an interpretable dual domain network for CT metal artifact reduction. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 107–118. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_11
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
Wang, Y., et al.: 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Trans. Med. Imaging 38(6), 1328–1339 (2019)
Wellenberg, R., Hakvoort, E., Slump, C., Boomsma, M., Maas, M., Streekstra, G.: Metal artifact reduction techniques in musculoskeletal CT-imaging. Eur. J. Radiol. 107, 60–69 (2018)
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 (CVPR) (2018)
Yu, L., Zhang, Z., Li, X., Xing, L.: Deep sinogram completion with image prior for metal artifact reduction in CT images. IEEE Trans. Med. Imaging 40(1), 228–238 (2021)
Zhan, B., et al.: Multi-constraint generative adversarial network for dose prediction in radiotherapy. Med. Image Anal. 77, 102339 (2022)
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)
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, B., Chen, X., Zhou, S.K., Duncan, J.S., Liu, C.: DuDoDR-Net: dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography. Med. Image Anal. 75, 102289 (2022)
Acknowledgements
This work was supported in part by National Natural Science Foundation of China (grant numbers 62101611 and 62201628), National Key Research and Development Program of China (2022YFA1204200), Guangdong Basic and Applied Basic Research Foundation (grant number 2022A1515011375,2023A1515012278,2023A1515011780) and Shenzhen Science and Technology Program (grant number JCYJ20220530145411027, JCYJ20220818102414031).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mao, H., Wang, Y., Yu, H., Wu, W., Zhang, J. (2023). Multi-perspective Adaptive Iteration Network for Metal Artifact Reduction. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-43999-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43998-8
Online ISBN: 978-3-031-43999-5
eBook Packages: Computer ScienceComputer Science (R0)