Estimation of locating the multiconductivity distribution in electrical impedance tomography using ICA

Abstract

Electrical impedance tomography (EIT) is a non-invasive imaging technique in medicine and industry. It can be used for determining the distribution of electrical impedance inside a body based upon current and voltage measurements made at the body’s surface. EIT is a non-linear inverse problem and the reconstruction problem is more complex and difficult. Estimation of the location and distribution of multi-conductivity distribution sources within the body, based on voltage recording from the source localization, is one of the fundamental problems in EIT. Independent component analysis (ICA) is a way to resolve signals into independent components based on the statistical characteristics of the signals. It is a method for factoring probability densities of measured signals into a set of densities that are as statistically independent as possible under the assumptions of a linear model. Under the approximate condition the independent component analysis is used to pre-process the acquired voltage measurements for EIT reconstruction in this paper. By using ICA the measured EIT voltage data can be separated into several independent component activation maps, in which the reconstruction algorithm is performed in order to obtain individual conductivity distributions. In our experiment the modified iterative reconstruction algorithm with an exponentially weighted least square criteria can be used for improving the performance of the reconstruction algorithm. Computer simulations show that this method is valid for locating the multi-conductivity distribution in electrical impedance tomography.