Abstract
Nowadays computed tomography (CT) is widely used for medical diagnosis and treatment. However, CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which could affect the quality of CT images and increase the possibility of false diagnosis and analysis. Recently, Convolutional Neural Network (CNN) was applied for metal artifact reduction (MAR) with synthesized paired images, which is not accurate enough to simulate the mechanism of imaging. With unpaired images, the first unsupervised model ADN appeared. But it is complicated in architecture and has distance to reach the level of existing supervised methods. To narrow the gap between unsupervised methods with supervised methods, this paper introduced a simpler multi-phase deep learning method extracting features recurrently to generate both metal artifacts and non-artifact images. Artifact Generative Network and Image Generative Network are presented jointly to remove metal artifacts. Extensive experiments show a better performance than ADN on synthesized data and clinical data.
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References
Gjesteby, L., et al.: Deep neural network for ct metal artifact reduction with a perceptual loss function. In: Proceedings of The Fifth International Conference on Image Formation in X-ray Computed Tomography (2018)
Sakamoto, M., et al.: Automated segmentation of hip and thigh muscles in metal artifact contaminated CT using CNN. In: International Forum on Medical Imaging in Asia 2019 (2019)
Shen, T., Li, X., Zhong, Z., Wu, J., Lin, Z.: R\(^{2}\)-Net: recurrent and recursive network for sparse-view CT artifacts removal. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 319–327. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_36
Liao, H., Lin, W.-A., Yuan, J., Zhou, S.K., Luo, J.: Artifact disentanglement network for unsupervised metal artifact reduction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 203–211. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_23
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, Z., et al.: Deep learning based metal artifacts reduction in post-operative cochlear implant CT imaging. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 121–129. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_14
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)
Goodfellow, I.J., et al.: Generative adversarial networks. arXiv:1406.2661 (2014)
Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging. 5(3), 036501 (2018)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8026–8037 (2019)
Ghani, M.-U., Karl, W.-C.: Fast enhanced CT metal artifact reduction using data domain deep learning. IEEE Trans. Comput. Imaging 6, 181–193 (2020)
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
Acknowledgement
This work was supported in part by Heilongjiang Province Natural Science Foundation key project of China under Grant No. ZD2019F003, the Natural Science Foundation of Heilongjiang Province under Grant No. F2018028.
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Zhao, B., Li, J., Ren, Q., Zhong, Y. (2020). Unsupervised Reused Convolutional Network for Metal Artifact Reduction. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_67
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DOI: https://doi.org/10.1007/978-3-030-63820-7_67
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