Three dimensional EIT models for human lung reconstruction based on Schur CG algorithm

It is urgent to define the nature and severity of persisting lung disease and to identify risk factors for developing chronic lung problems with objective, non-invasive measurements of lung oxygen requirements and lung function, suitable for use in medical treatment. At present, there is no system for continuous monitoring lung function for lung cancer sufferers to reduce the risk of syndrome after radial treatment. As electrical impedance tomography (EIT) aims to estimate the electrical properties inside the body from electrical signal measurements on the boundary, we are in the process of developing a new integrated EIT system based on portable technology to integrate three dimensional (3D) body structure from CT scan into the reconstruction algorithm. In principle, this approach, incorporated with prior information of anatomy, could provide a reduction of image artifacts in the reconstructed image associated with incorrect boundary model assumptions. In this paper, we investigate the suitable boundary model to minimize artifacts in the reconstruction for lung function. Three different 3D models with monolayer electrode are generated to represent a full range of geometric integrity and corresponding reconstructed images are compared, using conjugate gradient (CG) method. In order to improve the disadvantage of ill-posed for EIT problem, we also investigate three different multilayer electrode structures for 3D models. An advanced version of the conjugate gradient (CG) method, i.e. the Schur CG method, is used to solve the inverse problem. The result indicates that the quality of the reconstructed image depends on the structures of electrode.