A Modified Landweber Iteration Algorithm using Updated Sensitivity Matrix for Electrical Impedance Tomography

2013 ◽  
Vol 5 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Lifeng Zhang
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Reconstructed Image ◽  
Iteration Algorithm ◽  
Sensitivity Matrix ◽  
Initial Image ◽  
Impedance Tomography ◽  
Static Test ◽  
Conductivity Distribution

Electrical impedance tomography (EIT) is a technique to reconstruct the conductivity distribution of an inhomogeneous medium by injecting currents at the boundary of an object and measuring the resulting changes in voltage. The sensitivity matrix of EIT is calculated with a selected reference conductivity distribution, which is time-consuming. However, the sensitivity matrix will change with the media distribution, which results in the quality of the reconstructed image reduction. A modified Landweber iterative algorithm based on updated sensitivity matrix was presented in this paper. The reconstructed image based on conventional Landweber iteration was selected as the initial image for sensitivity matrix update, and the reconstructed images after sensitivity matrix update using different initial images were compared. The effect on the quality of reconstructed images for different times of sensitivity matrix update was also analyzed. Simulation and static test were carried out. Experimental results showed that reconstructed images with higher quality can be obtained.

Enhanced imaging of piezoresistive nanocomposites through the incorporation of nonlocal conductivity changes in electrical impedance tomography

2018 ◽  
Vol 29 (9) ◽  
pp. 1850-1861 ◽  
Author(s):  
Hashim Hassan ◽  
Fabio Semperlotti ◽  
Kon-Well Wang ◽  
Tyler N Tallman
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Low Cost ◽  
Electrode Arrays ◽  
Sensitivity Matrix ◽  
Impedance Tomography ◽  
Ill Posed ◽  
Internal Conductivity ◽  
Tomography Inversion

Electrical impedance tomography is a method of noninvasively imaging the internal conductivity distribution of a domain. Because many materials exhibit piezoresistivity, electrical impedance tomography has considerable potential for application in structural health monitoring. Despite its numerous benefits such as being low cost, providing continuous sensing, and having the ability to be employed in real time, electrical impedance tomography is limited by several important factors such as the ill-posed nature of the inverse problem and the requirement for large electrode arrays to produce quality images. Unfortunately, current methods of mitigating these limitations impose upon the benefits of electrical impedance tomography. Herein, we propose a multi-physics approach of enhancing electrical impedance tomography without sacrificing any of its benefits. This approach is predicated on coupling global conductivity changes with the electrical impedance tomography inversion process thereby adding additional constraints and rendering the problem less ill-posed. Additionally, we leverage physically motivated global conductivity changes in the context of piezoresistive nanocomposites. We demonstrate this proof of concept with numerical simulations and demonstrate that by incorporating multiple conductivity changes, the rank of the sensitivity matrix can be improved and the quality of electrical impedance tomography reconstructions can be enhanced. The proposed method, therefore, has the potential of easing the implementation burden of electrical impedance tomography while concurrently enabling high-quality images to be produced without imposing on the major advantages of electrical impedance tomography.

scholarly journals Numerical determination of anomalies in multifrequency electrical impedance tomography

2018 ◽  
Vol 30 (3) ◽  
pp. 481-504 ◽  
Author(s):  
HABIB AMMARI ◽  
FAOUZI TRIKI ◽  
CHUN-HSIANG TSOU
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Extreme Case ◽  
Cauchy Data ◽  
Perfect Conductor ◽  
Impedance Tomography ◽  
Piecewise Constant ◽  
Conductivity Distribution ◽  
Conductivity Profile

The multifrequency electrical impedance tomography consists in retrieving the conductivity distribution of a sample by injecting a finite number of currents with multiple frequencies. In this paper, we consider the case where the conductivity distribution is piecewise constant, takes a constant value outside a single smooth anomaly, and a frequency dependent function inside the anomaly itself. Using an original spectral decomposition of the solution of the forward conductivity problem in terms of Poincaré variational eigenelements, we retrieve the Cauchy data corresponding to the extreme case of a perfect conductor, and the conductivity profile. We then reconstruct the anomaly from the Cauchy data. The numerical experiments are conducted using gradient descent optimization algorithms.

scholarly journals Electrical Impedance Tomography might be a Practical Tool to Provide Information about COVID-19 Pneumonia Progression

2021 ◽  
Vol 7 (2) ◽  
pp. 276-278
Author(s):  
Rongqing Chen ◽  
András Lovas ◽  
Balázs Benyó ◽  
Knut Moeller
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Distress Syndrome ◽  
Impedance Tomography ◽  
Conductivity Distribution ◽  
Difference Image ◽  
Practical Tool ◽  
Different Response ◽  
Difference Image Analysis ◽  
Phenotype Identification

Abstract COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which might have different response and outcome to the traditional ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the PEEP recruitment can help improve the patients’ outcome. In this contribution we reported a COVID-19 patient with seven-day electrical impedance tomography monitoring. From the conductivity distribution difference image analysis of the data, a clear course developing trend can be observed in addition to the phenotype identification. This case might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia.

A novel deep neural network method for electrical impedance tomography

2019 ◽  
Vol 41 (14) ◽  
pp. 4035-4049 ◽  
Author(s):  
Xiuyan Li ◽  
Yong Zhou ◽  
Jianming Wang ◽  
Qi Wang ◽  
Yang Lu ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Simulation Software ◽  
Voltage Measurement ◽  
Finite Elements Analysis ◽  
Impedance Tomography ◽  
Conductivity Distribution ◽  
Highly Nonlinear ◽  
Internal Conductivity

Image reconstruction for Electrical Impedance Tomography (EIT) is a highly nonlinear and ill-posed inverse problem. It requires the design and employment of feasible reconstruction methods capable to guarantee trustworthy image generation. Deep Neural Networks (DNN) have a powerful ability to express complex nonlinear functions. This research paper introduces a novel framework based on DNN aiming to achieve EIT image reconstruction. The proposed DNN model, comprises of the following two layers, namely: The Stacked Autoencoder (SAE) and the Logistic Regression (LR). It is trained using the large lab samples which are obtained by the COMSOL simulation software (a cross platform finite elements analysis solver). The relationship between the voltage measurement and the internal conductivity distribution is determined. The untrained voltage measurement samples are used as input to the trained DNN, and the output is an estimate for image reconstruction of the internal conductivity distribution. The results show that the proposed model can achieve reliable shape and size reconstruction. When white Gaussian noise with a signal-to-noise ratio of 30, 40 and 50 were added to test set, the proposed DNN structure still has good imaging results, which proved the anti-noise capability of the network. Furthermore, the network that was trained using simulation data sets, would be applied for the EIT image reconstruction based on the experimental data that were produced after preprocessing.

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