Improving Electrical Impedance Tomography Imaging of the Lung with Patient-specific 3D Models

Abstract

Electrical Impedance Tomography (EIT) visualizes conductivity changes inside the thorax, which correlate with breathing and cardiac activity. While featuring high temporal resolution, no patient risks and bedside application, EIT has a very low spatial resolution, and its anatomical correspondence depends crucially on the choice of body model for image reconstruction. In contrast to the state of the art simplified or averaged 2D body models, we propose a workflow to generate patient-specific 3D models from Computed Tomography (CT) segmentations. This method acknowledges the 3D characteristics of EIT-induced currents in the body, while measurements are only performed in 2D. The workflow was designed in collaboration with medical experts such that its applicability in the clinical context becomes feasible. This is in contrast to most other works that only consider isolated algorithms and neglect the clinical demands and tasks. Our approach generates CT segmentations using another novel workflow based on interactive sketching, computes a tetrahedral multi-material mesh and creates a forward model with these results. The GREIT reconstruction algorithm is used to generate EIT images using the 3D model, while its parameters are tuned to the 3D properties of the mesh. We present results from two pigs, with three EIT datasets each, including mechanical ventilation, ventilation under the influence of lung injury, and ventilationfree regional perfusion analysis. We discovered three anatomical phenomena in the improved EIT images that could be visualized and explained using our workflow, while they caused some confusion in image interpretation using the state of the art techniques. These results, though not yet quantitatively measured, show the improved image quality and better anatomical significance, and stress the importance of accurate body models for EIT application in clinical research and patient treatment.