Maximum entropy regularization method for electrical impedance tomography combined with a normalized sensitivity map

Electrical impedance tomography (EIT) aims to estimate the electrical properties at the interior of an object from current–voltage measurements on its boundary. To overcome ill-posedness, regularization techniques such as Tikhonov regularization as well as some iterative methods were developed. In difference imaging between two different conductivity distributions, a conductivity change can be seen relatively non-negative to the medium with lower conductivity through some safeguard techniques. Therefore, the concept of maximum entropy from information theory and statistic mechanics can be used for this purpose. Furthermore, because the sensing field is “soft-field” and non-uniform, the same anomaly may produce different reconstruction signatures depending on its location within the image plane. Therefore, in this paper, maximum entropy based on general Tikhonov regularization, combined with normalized sensitivity map, is proposed to solve the inverse problem of EIT. Image reconstruction was carried out by maximum entropy regularization (MER) with a normalized sensitivity map and compared with the results from conjugate gradient method (CG), Tikhonov regularization, and CG with a normalized sensitivity map accordingly. Simulation and experiment results indicate that reconstructed images with higher quality can be obtained by MER with a normalized sensitivity map.