• Journal of Biomedical Engineering Research
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• v.25 no.4
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• pp.269-275
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• 2004

Different biological tissues have different values of electrical resistivity. In EIT (electrical impedance tomography), we try to provide cross-sectional images of a resistivity distribution inside an electrically conducting subject such as the human body mainly for functional imaging. However, it is well known that the image reconstruction problem in EIT is ill-posed and the quality of a reconstructed image highly depends on the measurement error. This requires us to develop a high-performance EIT system. In this paper, we describe the development of a 16-channel digital EIT system including a single constant current source, 16 voltmeters, main controller, and PC. The system was designed and implemented using the FPGA-based digital technology. The current source injects 50KHz sinusoidal current with the THD (total harmonic distortion) of 0.0029% and amplitude stability of 0.022%. The single current source and switching circuit reduce the measurement error associated with imperfect matching of multiple current sources at the expense of a reduced data acquisition time. The digital voltmeter measuring the induced boundary voltage consists of a differential amplifier, ADC, and FPGA (field programmable gate array). The digital phase-sensitive demodulation technique was implemented in the voltmeter to maximize the SNR (signal-to-noise ratio). Experimental results of 16-channel digital voltmeters showed the SNR of 90dB. We used the developed EIT system to reconstruct resistivity images of a saline phantom containing banana objects. Based on the results, we suggest future improvements for a 64-channel muff-frequency EIT system for three-dimensional dynamic imaging of bio-impedance distributions inside the human body.

• 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.

• Communications of the Korean Mathematical Society
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• v.16 no.3
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• pp.459-485
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• 2001

The aim of EIT (electrical impedance tomography) system is to image cross-section conductivity distribution of a human body by means of both generating and sensing electrodes attached on to the surface of the body, where currents are injected and voltages are measured. EIT has been suffered from the severe ill-posedness which is caused by the inherent low sensitivity of boundary measurements to any changes of internal tissue conductivity values. With a limited set of current-to-voltage data, figuring out full structure of the conductivity distribution could be extremely difficult at present time, so it could be worthwhile to extract some necessary partial information of the internal conductivity. We try to extract some key patterns of current-to-voltage data that furnish some core information on the conductivity distribution such s location and size. This overview provides our recent observation on the location search and the size estimation.

• 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.

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.256-264
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• 2012

The directional algebraic reconstruction technique (DART) using the trigonometric current pattern is one of the image reconstruction algorithms in electrical impedance tomography (EIT). This method needs to compute resistances between electrode pairs as using relation between the injected currents and measured voltages for the reconstruction of the inner image. The delay time is incurred in this process. Therefore this paper proposes modified directional algebraic reconstruction technique (mDART) using the adjacent current pattern instead of the trigonometric current pattern to solve the delay time for initial resistance values. The proposed method uses measured voltages instead of computed resistances in the reconstruction algorithm. Hence this method can eliminate the delay time because it does not use the resistances. In conclusion, the proposed method improves image quality and image reconstruction time by using the adjacent current pattern. To prove performance of the proposed method, we carried on computer simulation of various cases.

• Journal of IKEEE
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• v.23 no.1
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• pp.164-172
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• 2019

Current clinical methods for diagnosing urination disorder are invasive, expensive, and very inconvenient to perform continuous monitoring. EIT is a non-invasive technique that injects electrical current through an external electrodes and measures the induced voltage to visualize the internal electrical (impedance) characteristics, which makes it possible to monitor bladder conditions with low cost. The signal characteristics of the measured voltage data changes according to the current pattern injected through the electrode and affects reconstruction performance. In this paper, image reconstruction performance is compared and analyzed according to the injected current patterns to maximize the sensitivity to the variation of bladder size.

• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.94-98
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• 2010

In magnetic resonance electrical impedance tomography (MREIT), a current-injection MR imaging method is adopted to produce a cross-sectional image of an electrical conductivity distribution in addition to MR images. The purpose of this study was to test the feasibility of MREIT for differentiating the canine lung parenchyma without and with pneumonia. Three normal healthy beagles and two mixed breed dogs with pneumonia were used. After attaching electrodes around the chest, we placed the dog inside our MR scanner. We injected as much as 30 mA current in a form of short pulses into the chest region. Reconstructed conductivity images of normal canine lungs exhibit a peculiar pattern of a relatively coarse salt and pepper noise. On the contrary, conductivity images of pneumonic canine lungs show significantly enhanced contrast of the lesions while the corresponding MR images show a little bit of contrast in the middle and caudal lung parenchyma due to the accumulation of pleural fluid. This preliminary study indicates that MREIT imaging of the chest may deliver unique new diagnostic information.

• Journal of IKEEE
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• v.19 no.1
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• pp.33-40
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• 2015

Inverse problem in electrical impedance tomography (EIT) is highly ill-posed therefore prior information is used to mitigate the ill-posedness. Regularization methods are often adopted in solving EIT inverse problem to have satisfactory reconstruction performance. In solving the EIT inverse problem, iterative Gauss-Newton method is generally used due to its accuracy and fast convergence. However, its performance is still suboptimal and mainly depends on the selection of regularization parameter. Although, there are few methods available to determine the regularization parameter such as L-curve method they are sometimes not applicable for all cases. Moreover, regularization parameter is a scalar and it is fixed during iteration process. Therefore, in this paper, a novel method is used to determine the regularization parameter to improve reconstruction performance. Conductivity norm is calculated at each iteration step and it used to obtain the regularization parameter which is a diagonal matrix in this case. The proposed method is applied to human thorax imaging and the reconstruction performance is compared with traditional methods. From numerical results, improved performance of proposed method is seen as compared to conventional methods.