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This work introduces a computational framework for applying absoluteelectrical impedance tomography to head imaging without accurate information onthe head shape or the electrode positions. A library of fifty heads is employedto build a principal component model for the typical variations in the shape ofthe human head, which leads to a relatively accurate parametrization for headshapes with only a few free parameters. The estimation of these shapeparameters and the electrode positions is incorporated in a regularizedNewton-type output least squares reconstruction algorithm. The presentednumerical experiments demonstrate that strong enough variations in the internalconductivity of a human head can be detected by absolute electrical impedancetomography even if the geometric information on the measurement configurationis incomplete to an extent that is to be expected in practice.

Computational framework for applying electrical impedance tomography to head imaging