Estimation of anomaly location and size using electrical impedance tomography

Estimating the Volume of Unknown Inclusions in an Electrically Conducting Body with Voltage Measurements

We propose a novel technique to estimate the total volume of unknown insulating inclusions in an electrically conducting body from voltage measurements. Unlike conventional Electrical Impedance Tomography (EIT) systems that usually exhibit low spatial resolution and accuracy, the proposed device is composed of a pair of driving electrodes which, supplied with a known sinusoidal voltage, create a current density field inside a region of interest. The electrodes are designed to generate a current density field in the region of interest that is uniform, to a good approximation, when the inclusions are not present. A set of electrodes with a polygonal geometry is used for four-wires resistance measurements. The proposed technique has been tested designing a low cost prototype, where all electrodes are on the bottom of the conducting body, showing good performances. Such a device may be used to monitor the volume of biological cells inside cell culture dishes or the volume of blood clots in micro-channels in lab-on-a-chip biosensors.

On the influence of spread constant in radial basis networks for electrical impedance tomography

Electrical impedance tomography (EIT) is a non-invasive imaging technique. The main task of this work is to solve a non-linear inverse problem, for which several techniques have been suggested, but none of which gives a very high degree of accuracy. This paper introduces a novel approach, based on radial basis function (RBF) artificial neural networks (ANNs), to solve this problem, and uses several ANNs to obtain the best solution to the EIT inverse problem. ANNs have the potential to directly estimate the solution of the inverse problem with a high degree of accuracy. While different radial basis neural networks do not always perform well on different problems, they usually give good results on some specific problems. This paper evidences a strong correlation between the area of the target and the spread constant of the RBF network that gives the best reconstruction. A solution to automatically estimate the size of the target and pick the best neural network directly from voltage measurements is presented, making the reconstruction process automatic. By automatically selecting the best ANN for each specific set of voltage measurements, the proposed solution gives a more accurate reconstruction of both small and large targets.

A Mathematical Model for Breast Cancer Lesion Estimation: Electrical Impedance Technique Using TS2000 Commercial System

We present a mathematical model to analyze transadmittance data for the detection of breast cancer using TransScan TS2000 commercial system. The model was constructed based on the assumption that a lesion exists near the surface of a breast region. The breast region that is considered as a background is assumed to be homogeneous at least near the surface where we attach a planar array of electrodes. Based on the model, we developed a lesion estimation algorithm utilizing single- or multifrequency transadmittance data. The approximate ratio of two conductivity values for the lesion and background needs to be known to estimate the size of the lesion even though the location estimate does not require this ratio. From the results of numerical simulations with added noise, we suggest better ways of interpreting TS2000 transadmittance images for the detection of breast cancer with improved accuracy. Since this study provides a rigorous mathematical modeling of TS2000 commercial system, it will be possible to apply the technique to lesion estimation problems based on more realistic models of breast regions in future studies.

Estimation of anomaly location and size using electrical impedance tomography

We developed a new algorithm that estimates locations and sizes of anomalies in electrically conducting medium based on electrical impedance tomography (EIT) technique. When only the boundary current and voltage measurements are available, it is not practically feasible to reconstruct accurate high-resolution cross-sectional conductivity or resistivity images of a subject. In this paper, we focus our attention on the estimation of locations and sizes of anomalies with different conductivity values compared with the background tissues. We showed the performance of the algorithm from experimental results using a 32-channel EIT system and saline phantom. With about 1.73% measurement error in boundary current-voltage data, we found that the minimal size (area) of the detectable anomaly is about 0.72% of the size (area) of the phantom. Potential applications include the monitoring of impedance related physiological events and bubble detection in two-phase flow. Since this new algorithm requires neither any forward solver nor time-consuming minimization process, it is fast enough for various real-time applications in medicine and nondestructive testing.