Fast Adaptive 3-D Nonstationary Electrical Impedance Tomography Based on Reduced-Order Modeling

Abstract

Computational cost of image reconstruction in electrical impedance tomography (EIT) is generally very high. Time consumption of data processing can be prohibitive particularly in systems intended for continuous monitoring of time-varying targets in various applications. Recently, two promising approximate computational approaches have been proposed to reduce the computational cost of image reconstruction. These approaches are based on reduced-order approximation of the associated computational models. In this paper, we utilize these techniques to reduce the computational cost of 3-D nonstationary EIT imaging when high image reconstruction rate is required due to rapid changes or instabilities in the target of interest. The feasibility of the proposed reduced-order approach is evaluated in simulation and experimental studies. The results show that computational cost in nonstationary image reconstruction can be decreased significantly with reduced-order modeling, and in addition, with an appropriate reduced-order representation of the system state, the effects on the accuracy are very small.