Reconstruction of conductivity distribution with a compound variational strategy in electrical impedance tomography

2021 ◽  
Author(s):  
Yanyan Shi ◽  
Zhiwei Tian ◽  
Meng Wang ◽  
Zuguang Rao ◽  
Feng Fu
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Impedance Tomography ◽  
Conductivity Distribution

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.

scholarly journals NUMERICAL SIMULATION OF ELECTRICAL IMPEDANCE TOMOGRAPHY PROBLEM AND STUDY OF APPROACH BASED ON FINITE VOLUME METHOD

2014 ◽  
Vol 13 (4) ◽  
pp. 156-164
Author(s):  
Ye. S. Sherina ◽  
A. V. Starchenko
Keyword(s):  
Numerical Simulation ◽  
Image Reconstruction ◽  
Finite Volume Method ◽  
Electrical Impedance Tomography ◽  
Finite Volume ◽  
Electrical Impedance ◽  
Approximation Error ◽  
Impedance Tomography ◽  
Conductivity Distribution ◽  
Volume Method

This research has been aimed to carry out a study of peculiarities that arise in a numerical simulation of the electrical impedance tomography (EIT) problem. Static EIT image reconstruction is sensitive to a measurement noise and approximation error. A special consideration has been given to reducing of the approximation error, which originates from numerical implementation drawbacks. This paper presents in detail two numerical approaches for solving EIT forward problem. The finite volume method (FVM) on unstructured triangular mesh is introduced. In order to compare this approach, the finite element (FEM) based forward solver was implemented, which has gained the most popularity among researchers. The calculated potential distribution with the assumed initial conductivity distribution has been compared to the analytical solution of a test Neumann boundary problem and to the results of problem simulation by means of ANSYS FLUENT commercial software. Two approaches to linearized EIT image reconstruction are discussed. Reconstruction of the conductivity distribution is an ill-posed problem, typically requiring a large amount of computation and resolved by minimization techniques. The objective function to be minimized is constructed of measured voltage and calculated boundary voltage on the electrodes. A classical modified Newton type iterative method and the stochastic differential evolution method are employed. A software package has been developed for the problem under investigation. Numerical tests were conducted on simulated data. The obtained results could be helpful to researches tackling the hardware and software issues for medical applications of EIT.

scholarly journals Imaging Conductivity Changes in Monolayer Graphene Using Electrical Impedance Tomography

Micromachines ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1074
Author(s):  
Anil Kumar Khambampati ◽  
Sheik Abdur Rahman ◽  
Sunam Kumar Sharma ◽  
Woo Young Kim ◽  
Kyung Youn Kim
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Monolayer Graphene ◽  
Impedance Tomography ◽  
Graphene Surface ◽  
Conductivity Distribution ◽  
Conductivity Change ◽  
Modeling Errors ◽  
Imaging Approach ◽  
Difference Imaging

Recently, graphene has gained a lot of attention in the electronic industry due to its unique properties and has paved the way for realizing novel devices in the field of electronics. For the development of new device applications, it is necessary to grow large wafer-sized monolayer graphene samples. Among the methods to synthesize large graphene films, chemical vapor deposition (CVD) is one of the promising and common techniques. However, during the growth and transfer of the CVD graphene monolayer, defects such as wrinkles, cracks, and holes appear on the graphene surface. These defects can influence the electrical properties and it is of interest to know the quality of graphene samples non-destructively. Electrical impedance tomography (EIT) can be applied as an alternate method to determine conductivity distribution non-destructively. The EIT inverse problem of reconstructing conductivity is highly non-linear and is heavily dependent on measurement accuracy and modeling errors related to an accurate knowledge of electrode location, contact resistances, the exact outer boundary of the graphene wafer, etc. In practical situations, it is difficult to eliminate these modeling errors as complete knowledge of the electrode contact impedance and outer domain boundary is not fully available, and this leads to an undesirable solution. In this paper, a difference imaging approach is proposed to estimate the conductivity change of graphene with respect to the reference distribution from the data sets collected before and after the change. The estimated conductivity change can be used to locate the defects on the graphene surface caused due to the CVD transfer process or environment interaction. Numerical and experimental results with graphene sample of size 2.5 × 2.5 cm are performed to determine the change in conductivity distribution and the results show that the proposed difference imaging approach handles the modeling errors and estimates the conductivity distribution with good accuracy.

Statistical inversion in electrical impedance tomography using mixed total variation and non-convex ℓp regularization prior

2015 ◽  
Vol 23 (5) ◽  
Author(s):  
Thilo Strauss ◽  
Taufiquar Khan
Keyword(s):  
Inverse Problem ◽  
Total Variation ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Bayesian Framework ◽  
Electromagnetic Properties ◽  
Impedance Tomography ◽  
Conductivity Distribution ◽  
Ill Posed

AbstractElectrical impedance tomography (EIT) is a well-known technique to estimate the conductivity distribution γ of a body Ω with unknown electromagnetic properties. EIT is a severely ill-posed inverse problem. In this paper, we formulate the EIT problem in the Bayesian framework using mixed total variation (TV) and non-convex ℓ

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