An Image Reconstruction Algorithm for Electrical Impedance Tomography Using Measurement Estimation of Virtual Electrodes

Yang, Lu; Wu, Hongtao; Liu, Kai; Bai, Chen; Yang, Yunjie; Zhu, Chengjun; Yao, Jiafeng

doi:10.1109/jsen.2021.3120190

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…Currently, there are two ways to obtain reference voltages from the homogeneous medium. One is to obtain the reference voltages from the numerical calculation, which requires the shape of the phantom in simulation to be identical to that of the actual situation [12]. The other method is to measure the boundary voltages in the homogenous mediumfilled container, which is usually used in pipeline fluid flow velocity detection or volume fraction measurement in industry.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Reference Voltages for Time-Difference Electrical Impedance Tomography

Wan

Dong

et al. 2022

IEEE Trans. Instrum. Meas.

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Electrical impedance tomography (EIT), as a merging technology, has been widely used in the industrial and clinical fields. However, the causes of the uncertainty of measuring reference voltages, which are affected by medium temperature, measurement errors, and reference conductivity distribution that varies with the patient's posture, have brought obstacles to applying EIT in industry and medicine. In the paper, two methods: the Multiple Measurements (MM) method and the deep learning method, convolutional neural network (CNN) are proposed to establish the nonlinear mapping between measurement voltages and reference voltages. The novelty of the article is firstly adopting the deep learning method to estimate the reference voltages from measurement voltages for the timedifference EIT. Both static experiments-water tank experiments and dynamic experiments-two-phase flow experiments were carried out. Compared with the two existing estimation methods: best homogeneous (BH) approximation and measurement-scale feature (MSF), and the proposed MM method, the deep learning method shows excellent results in quantitative analysis of the relative errors of reference voltages and ground truth. In addition, the CNN method also displays a better performance in qualitative analysis in terms of the reconstructed tomographic images. The study shows the potential to realtime estimate the reference voltages for time-difference EIT in the industrial and medical fields.

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Section: Introductionmentioning

confidence: 99%

Estimation of Reference Voltages for Time-Difference Electrical Impedance Tomography

Wan

Dong

et al. 2022

IEEE Trans. Instrum. Meas.

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A shallow convolutional neural network-based method for enhanced conductivity distribution reconstruction under limited measurement

Shi,

Su,

Wang

et al. 2024

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Purpose In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement. Design/methodology/approach A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted. Findings The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise. Originality/value To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

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An Eigenvalue Decomposition Based Amplitude Extraction Method for EIT Demodulation System

Hao,

Wang,

2024

IEEE Access

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An Image Reconstruction Algorithm for Electrical Impedance Tomography Using Measurement Estimation of Virtual Electrodes

Cited by 5 publications

References 36 publications

Estimation of Reference Voltages for Time-Difference Electrical Impedance Tomography

Estimation of Reference Voltages for Time-Difference Electrical Impedance Tomography

A shallow convolutional neural network-based method for enhanced conductivity distribution reconstruction under limited measurement

An Eigenvalue Decomposition Based Amplitude Extraction Method for EIT Demodulation System

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