Fine-tuning electrode information in electrical impedance tomography

Electrical impedance tomography is a noninvasive imaging technique forrecovering the admittivity distribution inside a body from boundarymeasurements of current and voltage.In practice, impedance tomography suffers from inaccurate modellingof the measurement setting: The exact electrode locations and theshape of the imaged object are not necessarily known precisely.In this work, we tackle the problem with imperfect electrode informationby introducing the Fréchet derivative of the boundary measurementmap of impedance tomography with respect to the electrode shapes andlocations. This enables us to include the fine-tuning of the information onthe electrode positions as a part of a Newton-type output least squaresreconstruction algorithm; we demonstrate that this approach isfeasible via a two-dimensional numerical example based on simulated data.The impedance tomographymeasurements are modelled by the complete electrode model,which is in good agreement with real-life electrode measurements.