Constrained modeling for image reconstruction in the application of Electrical Impedance Tomography to the head

Abstract

Electrical Impedance Tomography (EIT) is an alternative way to image brain functions, in the form of conductivity distribution image, by using the boundary voltage information while a small current is injected. In head applications, due to the lack of accurate head models and the high-degree nonlinearity, the image reconstruction tends to fail. Recently, a nonlinear difference imaging approach has been proposed to mitigate modeling error. This approach, however, is based on unconstrained modeling that allows tissue conductivity values to be unrealistically negative. Consequently, substantial image artifacts are possibly conducted. In this work, two methods of constrained modeling were demonstrated they are able to substantially reduce artifacts and improve localization performance. New images of conductivity distribution of the mapped constraint domains, derived from the use of constrained modeling, are also exhibited here. The simulation result shows that the new images achieve better localization performance than those of using unconstrained modeling.