Dynamic boundary estimation of human heart within a complete cardiac cycle using electrical impedance tomography

Rashid, Ahmar; Kim, B S; Khambampati, Anil Kumar; Liu, Dong; Kim, S; Kim, Kyung Youn

doi:10.1088/1742-6596/224/1/012042

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…High correlation with the reference was found despite some limitations: the use of ICG as reference and the dependency of their approach on anatomical and conductivity a priori assumptions. A similar approach was used by Rashid et al (2010) who expressed the boundary of the left ventricle as truncated Fourier series coefficients and used a first-order kinematic model as state evolution model. A recent publication (Pikkemaat et al 2014) showed the feasibility of assessing SV from a cardiac impedance change derived from principal component analysis.…”

Section: Previous Workmentioning

confidence: 99%

Influence of heart motion on cardiac output estimation by means of electrical impedance tomography: a case study

et al. 2015

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Electrical impedance tomography (EIT) is a non-invasive imaging technique that can measure cardiac-related intra-thoracic impedance changes. EIT-based cardiac output estimation relies on the assumption that the amplitude of the impedance change in the ventricular region is representative of stroke volume (SV). However, other factors such as heart motion can significantly affect this ventricular impedance change. In the present case study, a magnetic resonance imaging-based dynamic bio-impedance model fitting the morphology of a single male subject was built. Simulations were performed to evaluate the contribution of heart motion and its influence on EIT-based SV estimation. Myocardial deformation was found to be the main contributor to the ventricular impedance change (56%). However, motion-induced impedance changes showed a strong correlation (r = 0.978) with left ventricular volume. We explained this by the quasi-incompressibility of blood and myocardium. As a result, EIT achieved excellent accuracy in estimating a wide range of simulated SV values (error distribution of 0.57 ± 2.19 ml (1.02 ± 2.62%) and correlation of r = 0.996 after a two-point calibration was applied to convert impedance values to millilitres). As the model was based on one single subject, the strong correlation found between motion-induced changes and ventricular volume remains to be verified in larger datasets.

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Section: Previous Workmentioning

confidence: 99%

Influence of heart motion on cardiac output estimation by means of electrical impedance tomography: a case study

et al. 2015

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“…EIT is an inherently ill-posed problem and its voltage measurements are prone to high measurement noise, leading to a poorly reconstructed conductivity image in the absence of any a priori information of the object domain. Using a priori knowledge of the lung boundaries, Vauhkonen et al (1998) and Rashid et al (2010c) reconstructed elliptic ventricle boundaries. Khambampati et al (2010), on the other hand, estimated the elliptic lung boundaries using the expectation minimization algorithm while assuming that the heart boundary is approximately known.…”

Section: Introductionmentioning

confidence: 99%

A dynamic oppositional biogeography-based optimization approach for time-varying electrical impedance tomography

et al. 2016

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Dynamic electrical impedance tomography-based image reconstruction using conventional algorithms such as the extended Kalman filter often exhibits inferior performance due to the presence of measurement noise, the inherent ill-posed nature of the problem and its critical dependence on the selection of the initial guess as well as the state evolution model. Moreover, many of these conventional algorithms require the calculation of a Jacobian matrix. This paper proposes a dynamic oppositional biogeography-based optimization (OBBO) technique to estimate the shape, size and location of the non-stationary region boundaries, expressed as coefficients of truncated Fourier series, inside an object domain using electrical impedance tomography. The conductivity of the object domain is assumed to be known a priori. Dynamic OBBO is a novel addition to the family of dynamic evolutionary algorithms. Moreover, it is the first such study on the application of dynamic evolutionary algorithms for dynamic electrical impedance tomography-based image reconstruction. The performance of the algorithm is tested through numerical simulations and experimental study and is compared with state-of-the-art gradient-based extended Kalman filter. The dynamic OBBO is shown to be far superior compared to the extended Kalman filter. It is found to be robust to measurement noise as well as the initial guess, and does not rely on a priori knowledge of the state evolution model.

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“…In medical imaging, EIT can be used to detect certain anomalies such as breast cancer cells (Kim et al 2007) and monitor several physiological phenomena, such as cardiac, pulmonary and respiratory functions (Harris 1991, Brown et al 1994, Kim et al 2006, Deibele et al 2008. The applications of EIT in the process industry include monitoring multi-phase flow in process pipelines, monitoring of the mixing phenomenon, sedimentation monitoring, etc (Mann et al 1997, Kim et al 2005, Khambampati et al 2009, Rashid et al 2010c. The conductivity estimation using EIT is a nonlinear ill-posed problem.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2011

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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.

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Dynamic boundary estimation of human heart within a complete cardiac cycle using electrical impedance tomography

Cited by 3 publications

References 6 publications

Influence of heart motion on cardiac output estimation by means of electrical impedance tomography: a case study

Influence of heart motion on cardiac output estimation by means of electrical impedance tomography: a case study

A dynamic oppositional biogeography-based optimization approach for time-varying electrical impedance tomography

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

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