• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.475-478
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• 2006

In electrical impedance tomography (EIT) an array of electrodes is attached on the boundary of an object and small alternating currents are injected through these electrodes, and then the resulting voltages are measured. An estimation for the cross-sectional resistivity distribution in the object is obtained by using these current and voltage data in a nondestructive manner. In this paper, the electrical impedance imaging of two-phase flows undergoing rapid transient is considered with a special emphasis on the effect of the current pattern on the image reconstruction. The trigonometric current pattern, which is commonly used in the conventional static electrical impedance imaging, shows poor performance in case of the dynamic imaging considered in this work. Extensive numerical experiments are conducted with various kinds of current patterns and their effects on the image reconstruction performance are examined.

• Communications of the Korean Mathematical Society
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• v.16 no.3
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• pp.519-541
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• 2001

Magnetic Resonance Electrical Impedance Tomography(MREIT) is a new medical imaging technique for the cross-sectional conductivity distribution of a human body using both EIT(Electrical Impedance Tomography) and MRI(Magnetic Resonance Imaging) system. MREIT system was designed to enhance EIT imaging system which has inherent low sensitivity of boundary measurements to any changes of internal tissue conductivity values. MREIT utilizes a recent CDI (Current Density Imaging) technique of measuring the internal current density by means of MRI technique. In this paper, a mathematical modeling for MREIT and image reconstruction method called the alternating J-substitution algorithm are presented. Computer simulations show that the alternating J-substitution algorithm provides accurate high-resolution conductivity images.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1327-1331
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• 2014

Background: Electrical impedance tomography (EIT) is a new non-invasive, mobile screening method which does not use ionizing radiation to the human breast; allows conducting quantitative assessment of the images besides the visual interpretation. The aim of this study was to correlate the quantitative assessment and visual interpretation of breast electrical impedance tomographs and associated factors. Materials and Methods: One hundred and fifty mammography patients above 40 years and undergoing EIT were chosen using convenient sampling. Visual interpretation of the images was carried out by a radiologist with minimum of three years experience using the breast imaging - electrical impedance (BI-EIM) classification for detection of abnormalities. A set of thirty blinded EIT images were reinterpreted to determine the intra-rater reliability using kappa. Quantitative assessment was by comparison of the breast average electric conductivity with the norm and correlations with visual interpretation of the images were determined using Chi-square. One-way ANOVA was used to compare the mean electrical conductivity between groups and t-test was used for comparisons with pre-existing Caucasians statistics. Independent t-tests were applied to compare the mean electrical conductivity of women with factors like exogenous hormone use and family history of breast cancer. Results: The mean electrical conductivity of Malaysian women was significantly lower than that of Caucasians (p<0.05). Quantitative assessment of electrical impedance tomography was significantly related with visual interpretation of images of the breast (p<0.05). Conclusions: Quantitative assessment of electrical impedance tomography images was significantly related with visual interpretation.

• Journal of Industrial Technology
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• v.26 no.B
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• pp.221-226
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• 2006

Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution within the interior of a body from measurements made on its surface. One recent application area of the EIT is the detection of breast cancer by imaging the conductivity and permittivity distribution inside the breast. The present "gold standard" for breast cancer detection is X-ray mammography, and it is desirable that EIT and X-ray mammography use the same geometry. This paper presents a forward model of a simplified mammography geometry for EIT imaging. The mammography geometry is modeled as a rectangular box with electrode arrays on the top and bottom planes. A forward model for the electrical impedance imaging problem is derived for a homogeneous conductivity distribution and Validated by experiment using a phantom tank.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.1-6
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• 2007

Two impedance imaging systems of multi-frequency electrical impedance tomography (MFEIT) and magnetic resonance electrical impedance tomography (MREIT) are described. MFEIT utilizes boundary measurements of current-voltage data at multiple frequencies to reconstruct cross-sectional images of a complex conductivity distribution (${\sigma}+i{\omega}{\varepsilon}$) inside the human body. The inverse problem in MFEIT is ill-posed due to the nonlinearity and low sensitivity between the boundary measurement and the complex conductivity. In MFEIT, we therefore focus on time- and frequency-difference imaging with a low spatial resolution and high temporal resolution. Multi-frequency time- and frequency-difference images in the frequency range of 10 Hz to 500 kHz are presented. In MREIT, we use an MRI scanner to measure an internal distribution of induced magnetic flux density subject to an injection current. This internal information enables us to reconstruct cross-sectional images of an internal conductivity distribution with a high spatial resolution. Conductivity image of a postmortem canine brain is presented and it shows a clear contrast between gray and white matters. Clinical applications for imaging the brain, breast, thorax, abdomen, and others are briefly discussed.

• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.39-46
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• 2012

Electrical impedance tomography(EIT) can produce functional images with conductivity distributions associated with physiological events such as cardiac and respiratory cycles. EIT has been proposed as a clinical imaging tool for the detection of stroke and breast cancer, pulmonary function monitoring, cardiac imaging and other clinical applications. However EIT still suffers from technical challenges such as the electrode interface, hardware limitations, lack of animal or human trials, and interpretation of conductivity variations in reconstructed images. We improved the KHU Mark2 EIT system by introducing an EIT electrode interface consisting of nano-web fabric electrodes and by adding a synchronized biosignal measurement system for gated conductivity imaging. ECG and respiration signals are collected to analyze the relationship between the changes in conductivity images and cardiac activity or respiration. The biosignal measurement system provides a trigger to the EIT system to commence imaging and the EIT system produces an output trigger. This EIT acquisition time trigger signal will also allow us to operate the EIT system synchronously with other clinical devices. This type of biosignal gated conductivity imaging enables capture of fast cardiac events and may also improve images and the signal-to-noise ratio (SNR) by using signal averaging methods at the same point in cardiac or respiration cycles. As an example we monitored the beat by beat cardiac-related change of conductivity in the EIT images obtained at a common state over multiple respiration cycles. We showed that the gated conductivity imaging method reveals cardiac perfusion changes in the heart region of the EIT images on a canine animal model. These changes appear to have the expected timing relationship to the ECG and ventilator settings that were used to control respiration. As EIT is radiation free and displays high timing resolution its ability to reveal perfusion changes may be of use in intensive care units for continuous monitoring of cardiopulmonary function.

• Proceedings of the KSMRM Conference
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• 2008.04a
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• pp.82-128
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• 2008

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.396-401
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• 2003

Electrical impedance tomography(EIT) is a relatively new imaging modality in which the internal impedivity distribution is reconstructed based on the known sets of injected currents and measured voltages on the surface of the object. In this paper, an effective dynamic EIT imaging scheme is presented based on the projection filtering to estimate the unknown resistivity distribution. In particular, pre-integration (pre-grouping) technique is employed to stabilize the inverse algorithm. We carried out computer simulations with synthetic data to illustrate the reconstruction performance of the proposed algorithm.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.91-96
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• 2007

Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution within the interior of a body from measurements made on its surface. One recent application area of the EIT is the detection of breast cancer by imaging the conductivity and permittivity distribution inside the breast. The present standard for breast cancer detection is X-ray mammography, and it is desirable that EIT and X-ray mammography use the same geometry. A forward model of a simplified mammography geometry for EIT imaging was proposed earlier. In this paper, we propose an iterative algorithm for computing the current pattern that will be applied to the electrodes. The current pattern applied to the electrodes influences the voltages measured on the electrodes. Since the measured voltage data is going to be used in the impedance imaging computation, it is desirable to apply currents that result in the largest possible voltage signal. We compute the eigenfunctions for a homogenous medium that will be applied as current patterns to the electrodes. The algorithm for the computation of the eigenfunctions is presented. The convergence of the algorithm is shown by computing the eigencurrent of the simplified mammography geometry.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.40-45
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• 2004

This work presents a boundary estimation approach in electrical impedance imaging for binary-mixture fields based on a parallel structured multi-layer neural network. The interfacial boundaries are expressed with the truncated Fourier series and the unknown Fourier coefficients are estimated with the parallel structure of multi-layer neural network. Results from numerical experiments shows that the proposed approach is insensitive to the measurement noise and has a strong possibility in the visualization of binary mixtures for a real time monitoring.