Electrical Impedance Tomography (EIT) is a novel medical imaging technology which is expected to give valuable information for the treatment of mechanically ventilated patients as well as for patients with obstructive lung diseases. In lung-EIT electrodes are attached around the thorax to inject small alternating currents and to measure resulting voltages. These voltages depend on the internal conductivity distribution and thus on the amount of air in the lungs. Based on the measured voltages, image reconstruction algorithms are employed to generate tomographic images reflecting the regional ventilation of the lungs. However, the ill-posedness of the reconstruction problem leads to reconstructed images that are severely blurred compared to morphological imaging technologies, such as X-ray computed tomography or Magnetic Resonance Imaging. Thus, a correct identification of the particular ventilation in anatomically assignable units, e.g. lung-lobes, is often hindered. In this study a 3D-FEM model of a human thorax has been used to simulate electrode voltages at different lung conditions. Two electrode planes with 16 electrodes at each layer have been used and different amount of emphysema and mucus plugging was simulated with different severity in the lung lobes. Patient specific morphological information about the lung lobes is used in the image reconstruction process. It is shown that this kind of prior information leads to better reconstructions of the conductivity change in particular lung lobes than in classical image reconstruction approaches, where the anatomy of the patients’ lungs is not considered. Thus, the described approach has the potential to open new and promising applications for EIT. It might be used for diagnosis and disease monitoring for patients with obstructive lung diseases but also in other applications, e.g. during the placement of endobronchial valves in patients with severe emphysema.
©2017 Benjamin Schullcke et al., published by De Gruyter
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.