Performance Prediction, Sensitivity Analysis and Parametric Optimization of Electrical Impedance Tomography Using A Bioelectrical Tissue Simulation Platform

There is an urgent need to bring forth portable, low-cost, point-of-care diagnostic instruments to monitor patient health and wellbeing. This is elevated by the COVID-19 global pandemic in which the availability of proper lung imaging equipment has proven to be pivotal in the timely treatment of patients. Electrical impedance tomography (EIT) has long been studied and utilized as such a critical imaging device in hospitals especially for lung ventilation. Despite decades of research and development, many challenges remain with EIT in terms of 1) optimal image reconstruction algorithms, 2) simulation and measurement protocols, 3) hardware imperfections, and 4) uncompensated tissue bioelectrical physiology. Due to the inter-connectivity of these challenges, singular solutions to improve EIT performance continue to fall short of the desired sensitivity and accuracy. Motivated to gain a better understanding and optimization of the EIT system, we report the development of a bioelectric facsimile simulator demonstrating the dynamic operations, sensitivity analysis, and reconstruction outcome prediction of the EIT sensor with stepwise visualization. By building a sandbox platform to incorporate full anatomical and bioelectrical properties of the tissue under study into the simulation, we created a tissue-mimicking phantom with adjustable EIT parameters to interpret bioelectrical interactions and to optimize image reconstruction accuracy through improved hardware setup and sensing protocol selections.