An Image Reconstruction Algorithm for Electrical Impedance Tomography Using Measurement Estimation of Virtual Electrodes

For biomedical imaging in mini-scale, the inverse problem of electrical impedance tomography (EIT) is severely ill-conditioned due to the number of electrodes is very limited. In this paper, a novel difference imaging algorithm for 2D-EIT using measurement estimation of virtual electrodes is proposed. The proposed reconstruction algorithm breaks though the limitation of micro EIT sensor’s structure and tackles the problem of low spatial resolution in mini-scale by introducing virtual electrodes. The inverse problem of EIT is decomposed into two separately tasks: estimation potentials of virtual electrodes with proper priors, determination of the conductivity distribution using both virtual and real electrodes. It is formulated as a possibility model using virtual electrodes’ potentials as latent variables. Real electrodes’ potentials are regarded as the observable variables. Conductivity distribution is predicted by a maximum likelihood of the possibility model by using EM algorithm. The proposed algorithm is verified by both simulation and experiment. In experiment, medaka fish embryo which is about 1mm in diameter is reconstructed. Comparing with Tikhonov and NOSER regularization method, average ICC is improved 11.54% from 0.7620 to 0.8499 in simulation and improved 5.79% from 0.6978 to 0.7382 in experiment. The algorithm not only performs well in conductivity reconstruction, but also in shape reconstruction. Average position error is decreased 47.57%, average shape error is decreased 24.44% in experiment. It is very suitable for image reconstruction in mini-scale.

I. Introduction

Electrical impedance tomography (EIT) is a kind of tomographic impedance imaging technology. Due to its non-invasive, non-radiative and high-speed characteristics, it is commonly used in pulmonary, cardiac, brain functional imaging [1]–[4], cell culture imaging [5], [6], as well as industrial applications [7]. It reconstructs conductivity distribution or conductivity change of the sensing domain with a set of electrodes through boundary current injection and the induced potential measurements [8], [9]. Image reconstruction is still a key issue of EIT due to the inverse problem is nonlinearity and ill-posedness [10], especially for biomedical imaging in mini-scale. Many efforts have been devoted to improve the quality of reconstruction image, such as driven patterns optimization [11], [12], electrode position optimization [13], novel image reconstruction algorithms development [14]–[19] and so on. Due to reconstruction algorithm of EIT affects image quality severely, it is very worth further studying.