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This work considers the optimization of electrode positions in head imagingby electrical impedance tomography. The study is motivated by maximizing thesensitivity of electrode measurements to conductivity changes when monitoringthe condition of a stroke patient, which justifies adopting a linearizedversion of the complete electrode model as the forward model. The algorithm isbased on finding a (locally) A-optimal measurement configuration via gradientdescent with respect to the electrode positions. The efficient computation ofthe needed derivatives of the complete electrode model is one of the focalpoints. Two algorithms are introduced and numerically tested on a three-layerhead model. The first one assumes a region of interest and a Gaussian prior forthe conductivity in the brain, and it can be run offline, i.e., prior to takingany measurements. The second algorithm first computes a reconstruction of theconductivity anomaly caused by the stroke with an initial electrodeconfiguration by combining lagged diffusivity iteration with sequentiallinearizations, which can be interpreted to produce an approximate Gaussianprobability density for the conductivity perturbation. It then resorts to thefirst algorithm to find new, more informative positions for the availableelectrodes with the constructed density as the prior.

Bayesian experimental design for head imaging by electrical impedance tomography