Block-Sparse Reconstruction for Electrical Impedance Tomography

WANG Qi; ZHANG Pengcheng; WANG Jianming; LI Xiuyan; LIAN Zhijie; CHEN Qingliang; CHEN Tongyun; CHEN Xiaojing; HE Jing; DUAN Xiaojie; WANG Huaxiang

doi:10.11999/JEIT170425

Volume 40 Issue 3

Mar. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(3): 676-682

WANG Qi, ZHANG Pengcheng, WANG Jianming, LI Xiuyan, LIAN Zhijie, CHEN Qingliang, CHEN Tongyun, CHEN Xiaojing, HE Jing, DUAN Xiaojie, WANG Huaxiang. Block-Sparse Reconstruction for Electrical Impedance Tomography[J]. Journal of Electronics & Information Technology, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425

Citation:

WANG Qi, ZHANG Pengcheng, WANG Jianming, LI Xiuyan, LIAN Zhijie, CHEN Qingliang, CHEN Tongyun, CHEN Xiaojing, HE Jing, DUAN Xiaojie, WANG Huaxiang. Block-Sparse Reconstruction for Electrical Impedance Tomography[J]. Journal of Electronics & Information Technology, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425

Citation:

WANG Qi, ZHANG Pengcheng, WANG Jianming, LI Xiuyan, LIAN Zhijie, CHEN Qingliang, CHEN Tongyun, CHEN Xiaojing, HE Jing, DUAN Xiaojie, WANG Huaxiang. Block-Sparse Reconstruction for Electrical Impedance Tomography[J]. Journal of Electronics & Information Technology, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425

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Block-Sparse Reconstruction for Electrical Impedance Tomography

doi: 10.11999/JEIT170425

1.
2.
(Tianjin Chest Hospital, Tianjin 300000, China)
3.
(School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China)

Funds:

The Key Projects of National Science and Technology Support Program (2013BAF06B00), The National Natural Science Foundation of China (61601324, 61373104, 61402330, 61405143), The Natural Science Foundation of Tianjin Municipal Science and Technology Commission (15JCQNJC01500)

Received Date: 2017-05-09
Rev Recd Date: 2017-12-15
Publish Date: 2018-03-19

Abstract

Abstract

An electrical impedance image reconstruction algorithm based on adaptive block-sparse dictionary is proposed. A block-sparse dictionary is constructed creatively, which preferably preserves the details of reconstructed images. Meanwhile, the sparsifying dictionary optimization and image reconstruction are performed alternately, and the intermediate result of the iterative reconstruction is used as the training sample of the sparse dictionary, which can effectively improve the learning effect of the dictionary. The numerical simulation and experiment results show that the patch-based sparsity method for measure noise has excellent robustness and can accurately reconstruct the conductivity distribution image, especially the precise details of mutation.
- Electrical impedance tomography,
- Image reconstruction,
- Sparse representation,
- Dictionary learning

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References(15)

References

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