Nonlinear Difference Imaging Approach to Three-Dimensional Electrical Impedance Tomography in the Presence of Geometric Modeling Errors

Abstract

Physiological processes that produce changes in the electrical conductivity of the body can be monitored with difference imaging based on EIT. The wide popularity of linearized difference imaging in EIT is mainly based on its good tolerance for the ubiquitous modeling errors, which are predominantly caused by inexact knowledge of the body geometry. However, the linearized difference imaging produces only qualitative information on the conductivity change, and the feasibility of the estimates also depends on the selection of the linearization point which ideally should be equal to the conductivity of the initial state. Based on the findings of this paper, these problems can be avoided by nonlinear difference imaging, and potentially the approach can enable quantitative imaging of conductivity change in medical applications.

B-Spline-Based Sharp Feature Preserving Shape Reconstruction Approach for Electrical Impedance Tomography

“…Framing the reconstruction approach in the context of linear difference imaging or nonlinear difference imaging would be another potential solution. This is because difference imaging tends to be quite robust to these sources of modeling errors, which are largely canceled out in the measurement subtraction and absorbed by the background estimation using the nonlinear approach [44], [45].…”

Artificial Sensitive Skin for Robotics Based on Electrical Impedance Tomography

“…Electrical impedance tomography (EIT) is a noninvasive measurement technique that reconstructs tissue images in vivo with the resistivity distribution of the human body . The essence of EIT is to inject the known current and measure the voltage value to reconstruct the impedance distribution according to a certain method .…”

An overview of 38 least squares–based frameworks for structural damage tomography

“…Moreover, models such as Eq. 43 offer advantages over non-stacked models, since much of the noise and modeling errors may be absorbed in the (often) uninteresting distribution of θ 1 (Liu et al 2016(Liu et al , 2015a. Incorporation of mixed regularization in two-state least squares regimes may be done in many ways.…”

“…* This model has previously been referred to as nonlinear difference imaging(Smyl et al 2018d;Liu et al 2016Liu et al , 2015a Prepared using sagej.cls…”