Bong Seok Kim

An oppositional biogeography-based optimization technique to reconstruct organ boundaries in the human thorax using electrical impedance tomography

Electrical impedance tomography (EIT) is a non-invasive imaging modality which has been actively studied for its industrial as well as medical applications. However, the performance of the inverse algorithms to reconstruct the conductivity images using EIT is often sub-optimal. Several factors contribute to this poor performance, including high sensitivity of EIT to the measurement noise, the rounding-off errors, the inherent ill-posed nature of the problem and the convergence to a local minimum instead of the global minimum. Moreover, the performance of many of these inverse algorithms heavily relies on the selection of initial guess as well as the accurate calculation of a gradient matrix. Considering these facts, the need for an efficient optimization algorithm to reach the correct solution cannot be overstated. This paper presents an oppositional biogeography-based optimization (OBBO) algorithm to estimate the shape, size and location of organ boundaries in a human thorax using 2D EIT. The organ boundaries are expressed as coefficients of truncated Fourier series, while the conductivities of the tissues inside the thorax region are assumed to be known a priori. The proposed method is tested with the use of a realistic chest-shaped mesh structure. The robustness of the algorithm has been verified, first through repetitive numerical simulations by adding randomly generated measurement noise to the simulated voltage data, and then with the help of an experimental setup resembling the human chest. An extensive statistical analysis of the estimated parameters using OBBO and its comparison with the traditional modified Newton-Raphson (mNR) method are presented. The results demonstrate that OBBO has significantly better estimation performance compared to mNR. Furthermore, it has been found that OBBO is robust to the initial guess of the size and location of the boundaries as well as offering a reasonable solution when the a priori knowledge of the conductivity of the organs is not very accurate.

Image reconstruction with an adaptive threshold technique in electrical resistance tomography

In electrical resistance tomography, electrical currents are injected through the electrodes placed on the surface of a domain and the corresponding voltages are measured. Based on these currents and voltage data, the cross-sectional resistivity distribution is reconstructed. Electrical resistance tomography shows high temporal resolution for monitoring fast transient processes, but it still remains a challenging problem to improve the spatial resolution of the reconstructed images. In this paper, a novel image reconstruction technique is proposed to improve the spatial resolution by employing an adaptive threshold method to the iterative Gauss–Newton method. Numerical simulations and phantom experiments have been performed to illustrate the superior performance of the proposed scheme in the sense of spatial resolution.

ELECTRICAL RESISTANCE IMAGING OF TWO-PHASE FLOW WITH A MESH GROUPING TECHNIQUE BASED ON PARTICLE SWARM OPTIMIZATION

An electrical resistance tomography (ERT) technique combining the particle swarm optimization (PSO) algorithm with the Gauss-Newton method is applied to the visualization of two-phase flows. In the ERT, the electrical conductivity distribution, namely the conductivity values of pixels (numerical meshes) comprising the domain in the context of a numerical image reconstruction algorithm, is estimated with the known injected currents through the electrodes attached on the domain boundary and the measured potentials on those electrodes. In spite of many favorable characteristics of ERT such as no radiation, low cost, and high temporal resolution compared to other tomography techniques, one of the major drawbacks of ERT is low spatial resolution due to the inherent ill-posedness of conventional image reconstruction algorithms. In fact, the number of known data is much less than that of the unknowns (meshes). Recalling that binary mixtures like two-phase flows consist of only two substances with distinct electrical conductivities, this work adopts the PSO algorithm for mesh grouping to reduce the number of unknowns. In order to verify the enhanced performance of the proposed method, several numerical tests are performed. The comparison between the proposed algorithm and conventional Gauss-Newton method shows significant improvements in the quality of reconstructed images.

EM algorithm applied for estimating non-stationary region boundaries using electrical impedance tomography

EIT has been used for the dynamic estimation of organ boundaries. One specific application in this context is the estimation of lung boundaries during pulmonary circulation. This would help track the size and shape of lungs of the patients suffering from diseases like pulmonary edema and acute respiratory failure (ARF). The dynamic boundary estimation of the lungs can also be utilized to set and control the air volume and pressure delivered to the patients during artificial ventilation. In this paper, the expectation-maximization (EM) algorithm is used as an inverse algorithm to estimate the non-stationary lung boundary. The uncertainties caused in Kalman-type filters due to inaccurate selection of model parameters are overcome using EM algorithm. Numerical experiments using chest shaped geometry are carried out with proposed method and the performance is compared with extended Kalman filter (EKF). Results show superior performance of EM in estimation of the lung boundary.

A sub-domain based regularization method with prior information for human thorax imaging using electrical impedance tomography

Electrical impedance tomography (EIT) is a non-invasive imaging technique that can be used as a bed-side monitoring tool for human thorax imaging. EIT has high temporal resolution characteristics but at the same time it suffers from poor spatial resolution due to ill-posedness of the inverse problem. Often regularization methods are used as a penalty term in the cost function to stabilize the sudden changes in resistivity. In human thorax monitoring, with conventional regularization methods employing Tikhonov type regularization, the reconstructed image is smoothed between the heart and the lungs, that is, it makes it difficult to distinguish the exact boundaries of the lungs and the heart. Sometimes, obtaining structural information of the object prior to this can be incorporated into the regularization method to improve the spatial resolution along with helping create clear and distinct boundaries between the objects. However, the boundary of the heart is changed rapidly due to the cardiac cycle hence there is no information concerning the exact boundary of the heart. Therefore, to improve the spatial resolution for human thorax monitoring during the cardiac cycle, in this paper, a sub-domain based regularization method is proposed assuming the lungs and part of background region is known. In the proposed method, the regularization matrix is modified anisotropically to include sub-domains as prior information, and the regularization parameter is assigned with different weights to each sub-domain. Numerical simulations and phantom experiments for 2D human thorax monitoring are performed to evaluate the performance of the proposed regularization method. The results show a better reconstruction performance with the proposed regularization method.

A rare muscular variation in the superficial region of the popliteal fossa

AbstractWe found a rare muscular variation in the superficial region of the popliteal fossa in a 61-year-old Korean male cadaver whose cause of death was laryngeal carcinoma during routine dissection course for medical students. The muscle ran transversely between the medial head of the gastrocnemius muscle and the tendon of the long head of biceps femoris muscle, covering the neurovascular structures in the popliteal fossa. The muscle received its nerve supply from the tibial nerve. Based on its innervation, we speculated that the anomalous muscle might be a very specific type of variation related to the gastrocnemius tertius rather than another superficial muscle in the popliteal fossa.

A Differential Evolution Based Approach to Estimate the Shape and Size of Complex Shaped Anomalies Using EIT Measurements

EIT image reconstruction is an ill-posed problem, the spatial resolution of the estimated conductivity distribution is usually poor and the external voltage measurements are subject to variable noise. Therefore, EIT conductivity estimation cannot be used in the raw form to correctly estimate the shape and size of complex shaped regional anomalies. An efficient algorithm employing a shape based estimation scheme is needed. The performance of traditional inverse algorithms, such as the Newton Raphson method, used for this purpose is below par and depends upon the initial guess and the gradient of the cost functional. This paper presents the application of differential evolution (DE) algorithm to estimate complex shaped region boundaries, expressed as coefficients of truncated Fourier series, using EIT. DE is a simple yet powerful population-based, heuristic algorithm with the desired features to solve global optimization problems under realistic conditions. The performance of the algorithm has been tested through numerical simulations, comparing its results with that of the traditional modified Newton Raphson (mNR) method.

Tracking resistivity changes using suboptimal fading extended Kalman filter in electrical resistance tomography

This paper presents a suboptimal fading extended Kalman filter to track fast resistivity changes inside the heart as well as the lungs during the cardiac cycle. The performance of the proposed algorithm is evaluated through the numerical simulation of a cardiac cycle. The results demonstrate that the proposed method is more robust as compared to the extended Kalman filter in tracking fast changes in the resistivity distribution.