Abstract
ObjectiveElectrical Impedance Tomography (EIT) is a powerful non-invasive technique for imaging applications. The goal is to estimate the electrical properties of living tissues by measuring the potential at the boundary of the domain. Being safe with respect to patient health, non-invasive, and having no known hazards, EIT is an attractive and promising technology. However, it suffers from a particular technical difficulty, which consists of solving a nonlinear inverse problem in real time. Several nonlinear approaches have been proposed as a replacement for the linear solver, but in practice very few are capable of stable, high-quality, and real-time EIT imaging because of their very low robustness to errors and inaccurate modeling, or because they require considerable computational effort.MethodsIn this paper, a post-processing technique based on an artificial neural network (ANN) is proposed to obtain a nonlinear solution to the inverse problem, starting from a linear solution. While common reconstruction methods based on ANNs estimate the solution directly from the measured data, the method proposed here enhances the solution obtained from a linear solver.ConclusionApplying a linear reconstruction algorithm before applying an ANN reduces the effects of noise and modeling errors. Hence, this approach significantly reduces the error associated with solving 2D inverse problems using machine-learning-based algorithms.SignificanceThis work presents radical enhancements in the stability of nonlinear methods for biomedical EIT applications.
Highlights
Electrical Impedance Tomography (EIT) is a method for biomedical imaging applications, and a very promising technology under active researches
Applying a linear reconstruction algorithm before applying an artificial neural network (ANN) reduces the effects of noise and modeling errors
An ANN can be represented as a system of interconnected neurons that compute outputs from inputs when information is fed through the network [15]
Summary
Electrical Impedance Tomography (EIT) is a method for biomedical imaging applications, and a very promising technology under active researches. There should be rough boundaries between two different tissues, and an accurate solution can only be obtained with a nonlinear method or with significant a priori knowledge of the system, which is usually not available in practice. The inverse problem is nonlinear, and severely ill-posed [2]. Due to such constraints, it is usually solved by making strong assumptions about the distribution from a prior probability function [3], and using linear inverse solvers [4]. The solution depends on an initial estimate of the conductivity distribution, potentially causing distortions in the resultant image, linear algorithms associated with a prior probability function offer strong robustness to noise and to modeling errors such as electrode displacement
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