Electromagnetic impedance tomography (EMIT): a new method for impedance imaging

A New Meshless Method for Solving 3D Inverse Conductivity Issues of Highly Nonlinear Elliptic Equations

In this research, the 3D inverse conductivity issues of highly nonlinear elliptic partial differential equations (PDEs) are investigated numerically. Even some researchers have utilized several schemes to overcome these multi-dimensional forward issues of those PDEs; nevertheless, an effective numerical algorithm to solve these 3D inverse conductivity issues of highly nonlinear elliptic PDEs is still not available. We apply two sets of single-parameter homogenization functions as the foundations for the answer and conductivity function to cope with the 3D inverse conductivity issue of highly nonlinear PDEs. The unknown conductivity function can be retrieved by working out another linear system produced from the governing equation by collocation skill, while the answer is acquired by dealing with a linear system to gratify over-specified Neumann boundary condition on a fractional border. As this new computational approach is based on a concrete theoretical foundation, it can result in a deeper understanding of 3D inverse conductivity issues with symmetry and asymmetry geometries. The homogenization functions method is rather stable, effective, and accurate in revealing the conductivity function when the over-specified Neumann data with a large level of noise of 28%.

WKB quasistatic reconstruction of layered biological tissues

A novel electrical impedance tomography method is introduced for reconstruction of layered biological tissues. The method utilizes a recently proposed image series expansion scheme in conjunction with the WKB approximation. This results in a locality feature, assigning analytically to each image term a local impedance associated with a unique layer, and thus leading to efficient and accurate reconstruction procedures.

Electrical impedance tomography method for reconstruction of biological tissues with continuous plane-stratification

A novel electrical impedance tomography method is introduced for reconstruction of layered biological tissues with continuous plane-stratification. The algorithm implements the recently proposed reconstruction scheme for piecewise constant conductivity profiles, based on an improved Prony method in conjunction with Legendre polynomial expansion (LPE). It is shown that the proposed algorithm is capable of successfully reconstructing continuous conductivity profiles with moderate (WKB) slop. Features of the presented reconstruction scheme include, an inherent linearity, achieved by the linear LPE transform, a locality feature, assigning analytically to each spectral component a local electrical impedance associated with a unique location, and effective performance even in the presence of noisy measurements.

Statistical estimation of resistance/conductance by electrical impedance tomography measurements

This paper is built upon the assumption that in electrical impedance tomography, vectors of voltages and currents are linearly dependent through a resistance matrix. This linear relationship was confirmed experimentally and may be derived analytically under certain assumptions regarding electrodes (Isaacson, 1991). Given measurement data consisting of voltages and currents, we treat this relationship as a linear statistical model. Thus, our goal is not to reconstruct the image but directly estimate its electromagnetic properties reflected in the resistance and/or conductance matrix using electrical impedance tomography (EIT) measurements of voltages and currents on the periphery of the body. Since no inverse problem is involved the algorithm for estimation merely reduces to one matrix inversion. We estimate the impedance resistance matrix using well established statistical inference techniques for linear regression models. We provide a comprehensive treatment for a two-dimensional homogeneous body of a circular shape, by which many concepts of electrical impedance tomography, such as width of electrodes, the difference between voltage-current and current-voltage systems are illustrated. Our theory may be applied to various tests including EIT hardware calibration and whether the medium is homogeneous. These tests are illustrated on phantom agar data.

Towards magnetic detection electrical impedance tomography: data acquisition and image reconstruction of current density in phantoms andin vivo

The objective of magnetic detection electrical impedance tomography (MD-EIT) is to reconstruct in vivo images of conductivity from magnetic field measurements taken around the body. MD-EIT is performed by applying an alternating current, at one of a range of frequencies, to a conducting object through a pair of electrodes fixed to the surface of the object. Magnetic field measurements recorded by search coils at a number of positions around the object are used to determine the current distribution that is generating the magnetic field. From this distribution, a conductivity map of a cross-section of the object can be reconstructed. This paper describes the development of an MD-EIT data acquisition system and discusses the related image reconstruction issues. The ill-conditioned nature of the inverse problem is examined and a number of image reconstruction methods are compared. The technical feasibility of MD-EIT data collection and image reconstruction is demonstrated with example images of current density from both phantom and human data.

Induced-current electrical impedance tomography: a 2-D theoretical simulation

A reconstruction algorithm, based on the modified Newton-Raphson algorithm, was developed for induced-current electrical impedance tomography and studied in theoretical two-dimensional geometry representing a human thorax. The finite-volume method was applied for the discretization of the physical domain, resulting in a symbolic representation of the Jacobian matrix, which is accurate and fast to construct. Several system configurations, differing in the number of excitation coils and electrodes, were simulated, and the performance in thoracic imaging was studied. It was found that a six-coil system shows a significant 40% improvement of conductivity values reconstruction over the three-coil system (an error of 2.06 omega(-1) compared with 3.44 omega(-1)). A number of 32 electrodes was found to be sufficient, being the smallest number of electrodes to still provide a reasonable performance (only 4.2% degradation in average conductivity error compared with the maximum possible 106-electrode system).