scholarly journals Automated filtering in the nonlinear Fourier domain of systematic artifacts in 2D electrical impedance tomography

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Melody Alsaker ◽  
Benjamin Bladow ◽  
Scott E. Campbell ◽  
Emma M. Kar
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Electrical Current ◽  
The Body ◽  
Fourier Domain ◽  
Reconstruction Method ◽  
Impedance Tomography ◽  
Conductivity Distribution ◽  
Frequency Regime ◽  
Internal Conductivity

<p style='text-indent:20px;'>For patients undergoing mechanical ventilation due to respiratory failure, 2D electrical impedance tomography (EIT) is emerging as a means to provide functional monitoring of pulmonary processes. In EIT, electrical current is applied to the body, and the internal conductivity distribution is reconstructed based on subsequent voltage measurements. However, EIT images are known to often suffer from large systematic artifacts arising from various limitations and exacerbated by the ill-posedness of the inverse problem. The direct D-bar reconstruction method admits a nonlinear Fourier analysis of the EIT problem, providing the ability to process and filter reconstructions in the nonphysical frequency regime. In this work, a technique is introduced for automated Fourier-domain filtering of known systematic artifacts in 2D D-bar reconstructions. The new method is validated using three numerically simulated static thoracic datasets with induced artifacts, plus two experimental dynamic human ventilation datasets containing systematic artifacts. Application of the method is shown to significantly reduce the appearance of artifacts and improve the shape of the lung regions in all datasets.</p>

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.

Electrical Impedance Tomography of Cell Viability in Tissue With Application to Cryosurgery

2004 ◽  
Vol 126 (2) ◽  
pp. 305-309 ◽  
Author(s):  
Rafael Davalos ◽  
Boris Rubinsky
Keyword(s):  
Electrical Properties ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Cell Damage ◽  
Membrane Integrity ◽  
The Body ◽  
Impedance Tomography ◽  
Cell Membrane Integrity ◽  
Minimally Invasive Surgical Procedure ◽  
Minimally Invasive Surgical

Tissue damage that is associated with the loss of cell membrane integrity should alter the bulk electrical properties of the tissue. This study shows that electrical impedance tomography (EIT) should be able to detect and image necrotic tissue inside the body due to the permeabilization of the membrane to ions. Cryosurgery, a minimally invasive surgical procedure that uses freezing to destroy undesirable tissue, was used to investigate the hypothesis. Experimental results with liver tissue demonstrate that cell damage during freezing results in substantial changes in tissue electrical properties. Two-dimensional EIT simulations of liver cryosurgery, which employ the experimental data, demonstrate the feasibility of this application.

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 A Krylov subspace type method for Electrical Impedance Tomography

2021 ◽  
Author(s):  
Mirjeta Pasha ◽  
Shyla Kupis ◽  
Sanwar Ahmad ◽  
Taufiquar Khan
Keyword(s):  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Krylov Subspace ◽  
Low Frequency ◽  
Small Dimension ◽  
Impedance Tomography ◽  
Type Method ◽  
Newton Algorithm ◽  
Ill Posed ◽  
Internal Conductivity

Electrical Impedance Tomography (EIT) is a well-known imaging technique for detecting the electrical properties of an object in order to detect anomalies, such as conductive or resistive targets. More specifically, EIT has many applications in medical imaging for the detection and location of bodily tumors since it is an affordable and non-invasive method, which aims to recover the internal conductivity of a body using voltage measurements resulting from applying low frequency current at electrodes placed at its surface. Mathematically, the reconstruction of the internal conductivity is a severely ill-posed inverse problem and yields a poor quality image reconstruction. To remedy this difficulty, at least in  part, we regularize and solve the nonlinear minimization problem by the aid of a Krylov subspace-type method for the linear sub problem during each iteration.  In EIT, a tumor or general anomaly can be modeled as a piecewise constant perturbation of a smooth background, hence, we solve the regularized problem on a subspace of relatively small dimension by the Flexible Golub-Kahan process that provides solutions that have sparse representation. For comparison, we use a well-known modified Gauss-Newton algorithm as a benchmark. Using simulations, we demonstrate the effectiveness of the proposed method. The obtained reconstructions indicate that the Krylov subspace method is better adapted to solve the ill-posed EIT problem and results in higher resolution images and faster convergence compared to reconstructions using the modified Gauss-Newton algorithm.

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.

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