Improved Configurations for 3D Acoustoelectric Tomography With a Minimal Number of Electrodes.

Abstract

Acoustoelectric tomography (AET) is a hybrid imaging technique combining ultrasound and electrical impedance tomography (EIT). It exploits the acoustoelectric effect (AAE): an US wave propagating through the medium induces a local change in conductivity, depending on the acoustoelectric properties of the medium. Typically, AET image reconstruction is limited to 2D and most cases employ a large number of surface electrodes. This article investigates the detectability of contrasts in AET. We characterize the AEE signal as a function of the medium conductivity and electrode placement, using a novel 3D analytical model of the AET forward problem. The proposed model is compared to a finite element method simulation. In a cylindrical geometry with an inclusion contrast of 5 times the background and two pairs of electrodes, the maximum, minimum, and mean suppression of the AEE signal are 68.5%, 3.12%, and 49.0%, respectively, over a random scan of electrode positions. The proposed model is compared to a finite element method simulation and the minimum mesh sizes required successfully model the signal is estimated. We show that the coupling of AAE and EIT leads to a suppressed signal and the magnitude of the reduction is a function of geometry of the medium, contrast and electrode locations. This model can aid in the reconstruction of AET images involving a minimum number of electrodes to determine the optimal electrode placement.