Study on the inverse problem of electrical impedance tomography based on self-diagnosis regularization.

Xing LI; Fan YANG; Xiao YU; Hao TIAN; Jiayuan HU; Zhouhai QIAN

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Study on the inverse problem of electrical impedance tomography based on self-diagnosis regularization.

Author: Xing LI ^{1
,

2} ; Fan YANG ³ ; Xiao YU ³ ; Hao TIAN ⁴ ; Jiayuan HU ⁵ ; Zhouhai QIAN ⁴
Author Information

1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, P.R.China.lx918star@
2. com.
3. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, P.R.China.
4. Hangzhou Yi Neng Electric Technology Co., Ltd., Hangzhou 310014, P.R.China.
5. Electric Power Science Research Institute of Zhejiang Electric Power Corporation, Hangzhou 310007, P.R.China.
Publication Type:Journal Article
Keywords: anti-noise ability; electrical impedance tomography; inverse problem; mean square error; regularization
From: Journal of Biomedical Engineering 2018;35(3):460-467
CountryChina
Language:Chinese
Abstract: The inverse problem of electrical impedance tomography (EIT) is seriously ill-posed, which restricts the clinical application of EIT. Regularization is an important numerical method to improve the stability of the EIT inverse problem as well as the resolution of the imaging. This paper proposes a self-diagnosis regularization method based on Tikhonov regularization and diagonal weight regularization method (DWRM). Firstly, the ill-posedness of the inverse problem is analyzed by sensitivity. Then, the performance of the self-diagnosis regularization is analyzed through the singular value theory. Finally, some simulated experiments including simulations and flume experiment are carried out and verify that the self-diagnosis regularization has better image quality and anti-noise ability than those of traditional regularization methods. The self-diagnosis regularization method weakens the ill-posedness of inverse problem of EIT and can prompt the practical application of EIT.