A novel data calibration scheme for electrical impedance tomography.

In electrical impedance tomography surface measurements of voltages and currents are recorded and the image reconstruction algorithm uses this set of boundary data to estimate internal electrical properties of the region under investigation. Therefore correct and accurate modelling of the current and voltage distributions (forward model) is an essential part of any reconstruction method. In this paper, we explored the root cause of a boundary layer effect in the reconstructed conductivity map and found it to be an artefact arising from 2D to 3D data-model mismatch within the imaging algorithm. We propose a data calibration scheme that improves the reconstruction results by removing these boundary or edge effects. We present both two-dimensional and three-dimensional images for agar phantoms using this data calibration scheme which are markedly better than their counterparts recovered when the measurement data are not calibrated with the procedure outlined herein.