Lung ventilation imaging based on structural model by electrical impedance tomography

Electrical impedance tomography (EIT), as a relatively new medical imaging method, has desirable advantages of being inexpensive, safe, non-invasive and portable. Complementary to existing methods, it is expected to reconstruct accurate images of impedance changes from the sensor arrays surrounding the boundary. One of the approaches to producing an accurately reconstructed image of the object in question is the inclusion of prior information combined with the structure property and the conductivity contribution information of the object. In this research, structural model is researched, the mesh of the human thorax is automatically refined and a sensitivity matrix incorporating the structural information is calculated. On accomplishment of the conjugate gradient (CG) algorithm, the lung ventilation images are reconstructed, the visualization results and the sensitivity distributions of the structural model make significant improvements in image quality. In addition to the imaging of the overall resistivity distribution within the thorax, the relative resistivity changes traced with the ventilation are analyzed, the acidic activities can be figured out in the ventilation periods.