Anatomically constrained electrical impedance tomography for two- and three-dimensional anisotropic bodies

Electrical Impedance Tomography (EIT) extracts the electrical conductivity distribution within a body using electrical measurements (e.g., voltages and currents) along the surface of the body. This research investigates the inclusion of anatomical constraints and anisotropy in EIT using a two-step approach. In the first step, the boundaries between regions of different conductivities are anatomically constrained using other imaging systems such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), etc. The possibility of using a combinatorial optimization scheme in EIT to extract the conductivity boundaries is also investigated. In the second step, the conductivity values in different regions are determined. Anisotropic conductivity regions are included in realistic finite element torso models to allow better modeling of the muscle regions (e.g., skeletal muscle) which exhibit a greater conductivity in the direction parallel to the muscle fiber. This two-step approach is used to reconstruct the conductivity profile of a canine torso, illustrating its potential application in extracting conductivity values for bioelectric modeling. Results for both two- and three-dimensional models will be presented and evaluated.; This research represents an extensive numerical study on extracting precise conductivity values in vivo using EIT. The explicit two-step approach represents a novel addition to EIT. The investigation of a combinatorial optimization scheme isolates the spatial resolution problem in EIT and yields insight on the inherent spatial resolution limits of EIT. Our incorporation of anatomical constraints from other higher resolution imaging systems allows for the inclusion of anisotropy in realistic two- and three-dimensional torso models. Finally, we investigate the use of interior measurements within EIT to improve the reconstruction accuracy of interior conductivities. It is expected that the inclusion of in vivo conductivity measurement data in bioelectric simulations will improve the accuracy of these simulations and thus in turn reduce the number of costly experiments presently required to design and test biomedical devices.