• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5595-5597
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• 2015

Electrical impedance tomography (EIT) is a new non-invasive, mobile screening method which does not use ionizing radiation to the human breast. It is based on the theory that cancer cells display altered local dielectric properties, thus demonstrating measurably higher conductivity values. This article reviews the utilisation of EIT in breast cancer detection. It could be used as an adjunct to mammography and ultrasonography for breast cancer screening.

• 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 the Korean Mathematical Society
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• v.45 no.1
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• pp.97-119
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• 2008

Electrical impedance tomography (EIT) problem with anisotropic anomalous region is formulated in a few different ways using boundary integral operators. The Frechet derivative of Neumann-to-Dirichlet map is computed also by using boundary integral operators and the boundary of the anomalous region is approximated by trigonometric expansion with Lagrangian basis. The numerical reconstruction is done in case that the conductivity of the anomalous region is isotropic.

• 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.

• KIEE International Transactions on Electrophysics and Applications
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• v.4C no.3
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• pp.123-128
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• 2004

In electrical impedance tomography (EIT), the internal resistivity distribution of the unknown object is computed using the boundary voltage data induced by different current patterns using various reconstruction algorithms. This paper presents a new image reconstruction algorithm based on the genetic algorithm (GA) via a two-step approach for the solution of the EIT inverse problem, in particular for the reconstruction of "static" images. The computer simulation for the 32 channels synthetic data shows that the spatial resolution of reconstructed images in the proposed scheme is improved compared to that of the modified Newton-Raphson algorithm at the expense of an increased computational burden.rden.

• 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.15 no.4
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• pp.413-418
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• 1994

In electrical impedance tomography (EIT), we use boundary current and voltage measurements to provide the information about the cross-sectional distribution of electrical impedance or resistivity One of the major problems in EIT has been the inaccessibility of internal voltage or current data in finding the internal impedance values. We propose a new image reconstruction method using internal current density data measured by NMR. We obtained a two-dimensional current density distribution within a phantom by processing the real and imaginary MR images from a 4.7T NMR machine. We implemented a resistivity image reconstruction algorithm using the finite element method and sensitivity matrix. We presented computer simulation results of the image reconstruction algorithm and furture direction of the research.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.118-121
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• 2003

In the conventional boundary estimation in EIT (Electrical Impedance Tomography), the interface between anomalies and background is expressed in usual as Fourier series and the boundary is reconstructed by obtaining the Fourier coefficients. This paper proposes a method for the boundary estimation, where the boundary of anomaly is approximated as the interpolation of front points located discretely along the boundary and is imaged by tracking the points in the region of interest. In the solution to the inverse problem to estimate the front points, the multi-layer neural network is introduced. For the verification of the proposed method, numerical experiments are conducted and the results indicate a good performance.

• Journal of IKEEE
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• v.24 no.4
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• pp.982-990
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• 2020

Groundwater is essential source of the freshwater. Groundwater is stored in the body of the rocks or sediments, called aquifer. Finding an aquifer is a very important part of the geophysical survey. The best method to find the aquifer is to make a borehole. Single borehole is not a suitable method if the aquifer is not located in the borehole drilled area. To overcome this problem, a cross borehole method is used. Using a cross borehole method, we can estimate aquifer location more precisely. Electrical impedance tomography is use to estimate the aquifer location inside the subsurface using the cross borehole method. Electrodes are placed inside each boreholes and area between these boreholes are analysed. An aquifer is a non-uniform structure with complex shape which can represented by the truncated Fourier series. Deep neural network is evaluated as an inverse problem solver for estimating the aquifer boundary coefficients.