Dynamic imaging in electrical impedance tomography of the human chest with online transition matrix identification.
One of the electrical impedance tomography objectives is to estimate the electrical resistivity distribution in a domain based only on electrical potential measurements at its boundary generated by an imposed electrical current distribution into the boundary. One of the methods used in dynamic estimation is the Kalman filter. In biomedical applications, the random walk model is frequently used as evolution model and, under this conditions, poor tracking ability of the extended Kalman filter (EKF) is achieved. An analytically developed evolution model is not feasible at this moment. The paper investigates the identification of the evolution model in parallel to the EKF and updating the evolution model with certain periodicity. The evolution model transition matrix is identified using the history of the estimated resistivity distribution obtained by a sensitivity matrix based algorithm and a Newton-Raphson algorithm. To numerically identify the linear evolution model, the Ibrahim time-domain method is used. The investigation is performed by numerical simulations of a domain with time-varying resistivity and by experimental data collected from the boundary of a human chest during normal breathing. The obtained dynamic resistivity values lie within the expected values for the tissues of a human chest. The EKF results suggest that the tracking ability is significantly improved with this approach.
Moura
Fernando Silva
FS
Department of Mechanical Engineering, Polytechnic School, University of São Paulo, São Paulo 05508-900, Brazil. fernandosmoura@gmail.com
Aya
Julio Cesar Ceballos
JC
Fleury
Agenor Toledo
AT
Amato
Marcelo Britto Passos
MB
Lima
Raul Gonzalez
RG
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D017097:Electric Impedance; D020342:Finite Element Analysis; D006801:Humans; D008168:Lung / Q000502:physiology; D008954:Models, Biological; D017711:Nonlinear Dynamics; D019047:Phantoms, Imaging; D012119:Respiration; D012680:Sensitivity and Specificity; D013909:Thorax / Q000502:physiology*; D014054:Tomography / Q000379:methods*