Using genetic algorithm for electrode movement problem in Electrical Impedance Tomography

Electrical impedance tomography (EIT) attempts to reconstruct the internal impedance distribution in a medium according to electrical measurements with electrodes on the medium surface. Main problem of EIT is the drift of electrodes during the medical applications, in which the body surface moves during breathing and posture change. In this paper, a new approach which can distinguish electrode movements from measurement data of EIT for eliminating such effects in static reconstructed image is presented. To achieve these objectives, this paper proposed a linear model to describe affection of boundary voltages caused by electrode movements. A genetic algorithm for the problems is introduced, which attempted to find the optimization of electrode movements to match the measurement voltages. To get least number of iterations possible, we introduce cultivation process in mutation operator. Simulation experiments show that the genetic algorithm is efficient and effective for electrode movement problem.