EIT image interpretation based on a 3D finite difference human thorax model

Electrical Impedance Tomography (EIT) can produce a cross-sectional image that allows non-invasive assessment of resistivity distribution in a measured object. Since its introduction more than two decades ago, EIT has attracted considerable interests. However, its clinical application has been constrained, to some extent, by the difficulties in interpreting the reconstructed images, due to their low resolution. To facilitate the application of Err to modeling human physiological functions, in this study, we proposed a method to quantify the changes of E IT images relative to anatomical structure. Based on ECG-gated MRI images, a 3D human thorax model was developed. EIT measurements of the thorax model were simulated by finite difference method and images were reconstructed using the filtered back projection algorithm. By varying the resistivities of the lungs, the ventricles and the atria in the thorax model, changes in the images were derived by computing the average resistivity change in the regions of interest. The results show there is a very strong relationship between organ volume and the magnitude of the observed resistivity change in the image. All the organs show a nearly linear change in the observed resistivity as a function of the resistivity change in the model.