Advanced search

Patch-based sparse reconstruction for electrical impedance tomography

Qi Wang (Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin Polytechnic University, Tianjin, China)
Pengcheng Zhang (Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin Polytechnic University, Tianjin, China)
Jianming Wang (Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin Polytechnic University, Tianjin, China)
Qingliang Chen (Tianjin Chest Hospital, Tianjin, China)
Zhijie Lian (Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin Polytechnic University, Tianjin, China)
Xiuyan Li (Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin Polytechnic University, Tianjin, China)
Yukuan Sun (Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin Polytechnic University, Tianjin, China)
Xiaojie Duan (Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin Polytechnic University, Tianjin, China)
Ziqiang Cui (Tianjin University, Tianjin, China)
Benyuan Sun (Tianjin University, Tianjin, China)
Huaxiang Wang (Tianjin University, Tianjin, China)

Sensor Review

ISSN: 0260-2288

Publication date: 19 June 2017

Abstract

Purpose

Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise.

Design/methodology/approach

This paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary optimization and image reconstruction are performed alternately. Two patch-based sparsity, namely, square-patch sparsity and column-patch sparsity, are discussed and compared with the global sparsity.

Findings

Both simulation and experimental results indicate that the patch based sparsity method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages.

Originality/value

EIT image is reconstructed based on patch-based sparse representation. Square-patch sparsity and column-patch sparsity are proposed and compared. Sparse dictionary optimization and image reconstruction are performed alternately. The new method tolerates a relatively high level of noise in measured voltages.

Keywords

Acknowledgements

The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (61402330, 61301246, 61301244 and 61601324), the PhD Programs Foundation of Ministry of Education of China (20131201120002) and the Natural Science Foundation of Tianjin Municipal Science and Technology Commission (15JCQNJC01500).

Citation

Wang, Q., Zhang, P., Wang, J., Chen, Q., Lian, Z., Li, X., Sun, Y., Duan, X., Cui, Z., Sun, B. and Wang, H. (2017), "Patch-based sparse reconstruction for electrical impedance tomography", Sensor Review, Vol. 37 No. 3, pp. 257-269. https://doi.org/10.1108/SR-07-2016-0126

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

Please note you might not have access to this content

You may be able to access this content by login via Shibboleth, Open Athens or with your Emerald account.
To rent this content from Deepdyve, please click the button.
Rent from Deepdyve
If you think you should have access to this content, click the button to contact our support team.
Contact us

We’re listening — tell us what you think

Join us on our journey