Time Sequence Learning for Electrical Impedance Tomography Using Bayesian Spatiotemporal Priors

Applications of Electrical Impedance Tomography in Neurology

Introduction: Electrical impedance tomography (EIT) is a non-invasive technique utilized in various medical applications, including brain imaging and other neurological diseases. Recognizing the physiological and anatomical characteristics of organs based on their electrical properties is one of the main applications of EIT, as each variety of tissue structure has its own electrical characteris...

A Bayesian Non-linear Approach for Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) of closed conductive media is an ill-posed inverse problem. As regards the corresponding direct problem, the choice of a Finite Elements Method preserves the non linear dependence of the observation set upon the conductivity distribution. In this paper, we show that the Bayesian approach presented in (1) for linear inverse imaging problems is also valid for...

Imaging UXO Using Electrical Impedance Tomography

This paper reports the results of tests where electrical impedance tomography (EIT) was evaluated as a tool for detecting and locating buried unexploded ordnance (UXO). The method relies on the electrolytic polarization induced at the boundary between soil and buried metal. This induced polarization (IP) produces a measurable phase delay between the electric current imposed on the subsurface an...

Breast Imaging using Electrical Impedance Tomography (EIT)

Application of Bayesian neural network in electrical impedance tomography

In this contribution we present a method for solving the inverse problem in electric impedance tomography with neural networks. The problem of reconstructing the conductivity distribution inside an object from potential measurements on the surface is known to be ill-posed, requiring efficient regularization techniques. We demonstrate that a statistical inverse solution, where the mean of the in...