• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.23-32
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• 2001

Electrical impedance tomography (EIT) is a relatively new imaging modality in which the resistivity (conductivity) distribution of the unknown object is estimated based on the known sets of injected currents and measured voltages on the surface of the object. In this paper, we propose a dynamical EIT reconstruction algorithm based on the regularized extended Kalman filter(EKF). The EIT inverse problem is formulated as dynamic equation which consists of the slate equation and the observation equation, and the unknown state(resistivity) is estimated recursively with the aid of the EKF. In doing so, the generalized Tikhonov regularization technique is employed in the cost functional to mitigate the ill-posedness characteristics of the inverse problem. Computer simulations for the 16-channel synthetic data are provided to illustrate the reconstruction performance of the proposed algorithm.

• Journal of IKEEE
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• v.5 no.2 s.9
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• pp.153-163
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• 2001

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. We describe a dynamic EIT imaging technique for the case where the resistivity distribution inside the object changes rapidly within the time taken to acquire a full set of independent measurement data. In doing so, the inverse problem is treated as the state estimation problem and the unknown state (resistivity) is estimated with the aid of extended Kalman filter in a minimum mean square error sense. In particular, additional electrodes are attached to the known internal structure of the object to enhance the reconstruction performance and modified Tikhonov regularization technique is employed to mitigate the ill-posedness of the inverse problem. Computer simulations are provided to illustrate the reconstruction performance of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.45.4-45
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• 2002

In EIT, an array of disjoint electrodes is attached on the boundary of the object and a set of small alternating electrical currents is injected into the object through these electrodes, and then the corresponding set of voltages is measured on the same array of the electrodes. The objective in EIT is to estimate the resistivity distribution inside the object based on the set of measured voltages and injected currents. In this paper, we proposed a new dynamic EIT reconstruction scheme based on the interacting multiple model (IMM) algorithm. The main contribution of the proposed scheme is that multiple models are employed for the state evolution to get around the modeling uncertainty. Extensi...

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.613-619
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• 2008

A method to produce a desired current pattern in a multiple-source EIT system using voltage sources is presented. Application of current patterns to a body is known to be superior to the application of voltage patterns in terms of high spatial frequency noise suppression, resulting in high accuracy in conductivity and permittivity images. Since current sources are difficult and expensive to build, the use of voltage sources to apply the current pattern is desirable. An iterative algorithm presented in this paper generates the necessary voltage pattern that will produce the desired current pattern. The convergence of the algorithm is shown under the condition that the estimation error of the linear mapping matrix from voltage to current is small. Simulation results are presented to illustrate the convergence of the output current.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.32-35
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• 2004

In boundary estimation in Electrical Impedance Tomography (EIT), conventional method is the modified Newton Raphson (mNR) method .The mNR is famous for good method since has good convergence and robustness against noisy data. But the mNR is low efficiency to get and update Jacobian matrix. So, the mNR become very slow algorithm. We propose the Quasi Newton (QN) method to improve efficiency which will lead to speed up in boundary estimation. The QN can improve a low efficiency by using estimated Jacobian matrix contrary to using exactly calculated Jacobian matrix, this used by the mNR. And finally, we propose the modified Quasi Newton (mQN) method because the QN has some problems such as bad early convergence rate and instability of 'divided by zero'. For the verification of the propose method, numerical experiments are conducted and the results show a good performance.

• Journal of IKEEE
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• v.25 no.4
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• pp.650-663
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• 2021

Subsurface topology estimation is an important factor in the geophysical survey. Electrical impedance tomography is one of the popular methods used for subsurface imaging. The EIT inverse problem is highly nonlinear and ill-posed; therefore, reconstructed conductivity distribution suffers from low spatial resolution. The subsurface region can be approximated as piece-wise separate regions with constant conductivity in each region; therefore, the conductivity estimation problem is transformed to estimate the shape and location of the layer boundary interface. Each layer interface boundary is treated as an open boundary that is described using front points. The subsurface domain contains multi-layers with very complex configurations, and, in such situations, conventional methods such as the modified Newton Raphson method fail to provide the desired solution. Therefore, in this work, we have implemented a 7-layer artificial neural network (ANN) as an inverse problem algorithm to estimate the front points that describe the multi-layer interface boundaries. An ANN model consisting of input, output, and five fully connected hidden layers are trained for interlayer boundary reconstruction using training data that consists of pairs of voltage measurements of the subsurface domain with three-layer configuration and the corresponding front points of interface boundaries. The results from the proposed ANN model are compared with the gravitational search algorithm (GSA) for interlayer boundary estimation, and the results show that ANN is successful in estimating the layer boundaries with good accuracy.

• Journal of Biomedical Engineering Research
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• v.25 no.4
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• pp.261-268
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• 2004

Osteoporosis is a clinical condition in which the amount of bone tissue is reduced and the likelihood of fracture is increased. It is known that the electrical property of the bone is related to its density, and, in particular, the electrical resistance of the bone decreases as the bone loss increases. This implies that the electrical property of bone may be an useful parameter to diagnose osteoporosis, provided that it can be readily measured. The study attempted to evaluate the electrical conductivity of bone using a technique of electrical impedance tomography (EIT). It nay not be easy in general to get an EIT for the bone due to the big difference (an order of 2) of electrical properties between the bone and the surrounding soft tissue. In the present study, we took an adaptive mesh regeneration technique originally developed for the detection of two phase boundaries and modified it to be able to reconstruct the electrical conductivity inside the boundary provided that the geometry of the boundary was given. Numerical simulation was carried out for a tibia phantom, circular cylindrical phantom (radius of 40 mm) inside of which there is an ellipsoidal homeogenous tibia bone (short and long radius are 17 mm and 15 mm, respectively) surrounded by the soft tissue. The bone was located in the 15 mm above from the center of the circular cross section of the phantom. The electrical conductivity of the soft tissue was set to be 4 mS/cm and varies from 0.01 to 1 ms/cm for the bone. The simulation considered measurement errors in order to look into its effects. The simulated results showed that, if the measurement error was maintained less than 5 %, the reconstructed electrical conductivity of the bone was within 10 % errors. The accuracy increased with the electrical conductivity of the bone, as expected. This indicates that the present technique provides more accurate information for osteoporotic bones. It should be noted that tile simulation is based on a simple two phase image for the bone and the surrounding soft tissue when its anatomical information is provided. Nevertheless, the study indicates the possibility that the EIT technique may be used as a new means to detect the bone loss leading to osteoporotic fractures.

• Proceedings of the KSMRM Conference
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• 2003.10a
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• pp.17-17
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• 2003

목적： 생체 조직에서의 전기임피던스 분포는 생리적 기능에 대하여 풍부한 정보를 가지고 있다. 이러한 전기임피던스 분포는 전기임피던스단층촬영법(EIT)으로 구할 수 있으나 공간해상도가 열악하여 그 사용이 보편화되지 못하고 있다. 기존의 EIT의 한계점을 극복하기 위하여 EIT와 MRI 기술을 결합한 자기공명임피던스단층촬영법(MREIT： Magnetic Resonance Electrical Impedance Tomography)이 최근 제안되었다. MREIT는 영상복원 과정에서 x, y, z 3방향의 자속밀도 벡터를 필요로 하므로 MRI용 자석 내에서 물체를 3차원으로 회전하여 자속밀도 벡터를 구해야 한다. 이러한 3차원 회전은 MREIT가 실제 임상에 적용되는데 있어서 한계점으로 지적되고 있다. 본 논문에서는 물체 회전을 하지 않고 전기임피던스 분포를 얻을 수 있는 새로운 MREIT 방법을 제안하였다. 새로운 MREIT 방법의 원리에 대해서 소개하고 0.3T의 주자장세기를 갖는 연구용 MRI 시스템에서 얻은 MREIT영상을 소개하고자 한다.

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.249-256
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• 2000

In EIT(electrical impedance tomography), the internal resistivity(or conductivity) distribution of the unknown object is estimated using the boundary voltage data induced by different current patterns using various reconstruction algorithms. In this paper, we present a regularized modified Newton-Raphson(mNR) scheme which employs additional a priori information in the cost functional as soft constraint and the weighting matrices in the cost functional are selected based on the exponentially weighted least square criterion. The computer simulation for the 32 channels synthetic data shows that the reconstruction performance of the proposed scheme is improved compared to that of the conventional regularized mNR at the expense of slightly increased computational burden.

• Journal of IKEEE
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• v.18 no.2
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• pp.272-281
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• 2014

In electrical impedance tomography (EIT), modified Newton Raphson (mNR) method is widely used inverse algorithm for static image reconstruction due to its convergence speed and estimation accuracy. The unknown conductivity distribution is estimated iteratively by minimizing a cost functional such that the residual error namely the difference in measured and calculated voltages is reduced. Although, mNR method has good estimation performance, EIT inverse problem still suffers from ill-conditioned and ill-posedness nature. To mitigate the ill-posedness, generally, regularization methods are adopted. The inverse solution is highly dependent on the choice of regularization parameter. In most cases, the regularization parameter has a constant value and is chosen based on experience or trail and error approach. In situations, when the internal distribution changes or with high measurement noise, the solution does not get converged with the use of constant regularization parameter. Therefore, in this paper, in order to improve the image reconstruction performance, we propose a new scheme to determine the regularization parameter. The regularization parameter is computed based on residual error and updated every iteration. The proposed scheme is tested with numerical simulations and laboratory phantom experiments. The results show an improved reconstruction performance when using the proposed regularization scheme as compared to constant regularization scheme.