The sensitivity of reconstructed images and process engineering metrics to key choices in practical electrical impedance tomography

This paper explores, from an experimental perspective, the dramatic effect that measurement strategy, reconstruction algorithm and reconstruction parameters can have on electrical impedance tomography images. Measurement data, from a stirred tank and jet mixer, have been acquired using two tomographs from the University of Cape Town and Industrial Tomography Systems Ltd respectively. Simulations consider conductively contrasting objects that are placed strategically in the vessel. The adjacent and opposite measurement strategies are employed to interrogate an unbaffled mixing tank. The superior sensitivity of the opposite strategy in the centre of the vessel is verified. For a central inclusion, simulated results suggest 4% 'image error' compared to 6% for the adjacent strategy. Five reconstruction algorithms: Linear Back Projection, Landweber, Conjugate Gradients, Generalized Singular Value Decomposition (GSVD) and Nonlinear Gauss Newton, have been considered. A measure of 'image error' is typically below 10%, but values as high as 30% are not unusual with algorithms such as Linear Back Projection. For a homogeneous step change in conductivity 'image error' is seen to vary from 3% for Nonlinear Gauss Newton to 150% for Linear Back Projection. Corresponding measures of the coefficient of variation range from 25% to 44%. Overall it is suggested that the GSVD algorithm provides the best balance of attributes for identifying discrete objects and for homogeneous step changes. The dramatic effect of regularization parameters is illustrated by considering the GSVD. The use of the discrete Picard condition to determine optimum values is demonstrated. Mixing lengths have been calculated from the reconstructed image data and this is seen to vary dramatically with the regularization parameter.