Artifact Removal Using Attention Guided Local-Global Dual-Stream Network for Sparse-View CT Reconstruction
Author:
Affiliation:
1. School of Information and Communication Enginnering, Beijing University of Posts and Telecommunications
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/8/E107.D_2023EDL8049/_pdf
Reference15 articles.
1. [1] Z. Zhang, X. Liang, X. Dong, Y. Xie, and G. Cao, “A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution,” IEEE Trans. Med. Imag., vol.37, no.6, pp.1407-1417, 2018. 10.1109/tmi.2018.2823338
2. [2] S. Xie, X. Zheng, Y. Chen, L. Xie, J. Liu, Y. Zhang, J. Yan, H. Zhu, and Y. Hu, “Artifact removal using improved GoogLeNet for sparse-view CT reconstruction,” Scientific reports, vol.8, p.6700, 2018. 10.1038/s41598-018-25153-w
3. [3] Y. Han and J.C. Ye, “Framing U-Net via deep convolutional framelets: Application to sparse-view CT,” IEEE Trans. Med. Imag., vol.37, no.6, pp.1418-1429, 2018. 10.1109/tmi.2018.2823768
4. [4] M. Lee, H. Kim, and H.-J. Kim, “Sparse-view CT reconstruction based on multi-level wavelet convolution neural network,” Physica Medica, vol.80, pp.352-362, 2020. 10.1016/j.ejmp.2020.11.021
5. [5] Z. Li, W. Zhang, L. Wang, A. Cai, N. Liang, B. Yan, and L. Li, “A sinogram inpainting method based on generative adversarial network for limited-angle computed tomography,” 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, vol.11072, SPIE, pp.345-349, 2019.
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