Automatic detection of detached and erroneous electrodes in electrical impedance tomography

Abstract

Electrical Impedance Tomography (EIT) is an imaging technique which calculates the conductivity distribution within a medium from voltage measurements made at a series of electrodes on the medium's surface. Unfortunately, the electrodes can become detached or poorly connected, such that the measured data cannot be used. This thesis presents an automatic approach to detect such erroneous electrodes via the image reconstruction model. The method calculates an estimate of the data at an electrode, based on the measurements from all other electrodes. In order to detect an erroneous electrode amongst N electrodes, all sets of N-1 electrodes are tested, and the set with the best match between measurements and estimate is identified as the one which excludes the erroneous electrode. Tests performed on experimental data for 2D EIT showed similar classification to those made by a trained user. A detection parameter PER is developed, and a detection threshold of -22 +/- 2 dB is recommended based analysis of simulated erroneous data. Extension of the method into 3D EIT showed similar results as that of 2D EIT.