Artificial Intelligence Based Electrical Impedance Tomography for Local Tissue

This research aims at proposing the use of Electrical Impedance Tomography (EIT), a
non-invasive technique that makes it possible to measure two or three dimensional
impedance of living local tissue in a human body which is applied for medical
diagnosis of diseases. In order to achieve this, electrodes are attached to the part of
human body and an image of the conductivity or permittivity of living tissue is
deduced from surface electrodes. In this thesis we have worked towards alleviating
drawbacks of EIT such as estimating parameters by incorporating it in an electrode
structure and determining a solution to spatial distribution of bio-impedance to a close
proximity. We address the issue of initial parameter estimation and spatial resolution
accuracy of an electrode structure by using an arrangement called "divided electrode"
for measurement of bio-impedance in a cross section of a local tissue. Its capability is
examined by computer simulations, where a distributed equivalent circuit is utilized
as a model for the cross section tissue. Further, a novel hybrid model is derived which
is a combination of artificial intelligence based gradient free optimization technique
and numerical integration in order to estimate parameters. This arne! iorates the
achievement of spatial resolution of equivalent circuit model to the closest accuracy.