Kalman Filter Application to Electrical Impedance Tomography
Speaker: Samarjit Das, ECE dept, ISU
Abstract:
We are going to discuss the following paper in the talk:
M. Vauhkonen, P.A. Karjalainen, and J.P. Kaipio, "A Kalman fillter approach to
track fast impedance changes in electrical impedance tomography," IEEE Trans
Biomed Eng, 1998.

In electrical impedance tomography (EIT), an estimate for the cross-sectional
impedance distribution is obtained from the body by using current and voltage
measurements made from the boundary. All well-known reconstruction algorithms
use a full set of independent current patterns for each reconstruction. In
some applications, the impedance changes may be so fast that information on
the time evolution of the impedance distribution is either lost or severely
blurred. A Kalman filter based approach of EIT reconstruction is able to track
fast changes in the impedance distribution. The method is based on the
formulation of EIT as a state-estimation problem and the recursive estimation
of the state with the aid of the Kalman filter.The impedance distribution
evolution with time is considered as the state-process and the voltage
measurement using 32 electrodes is considered as the measurement process.  
The linear Kalman filter is briefly discussed. It~Rs formulation as an optimal
filter from the state-space model is also focused along with its significance
pertaining to recursive state estimation problem. The performance of the KF
based EIT method is evaluated with a simulation of human thorax in a situation
in which the impedances of the ventricles change rapidly. Results show that KF
based reconstruction algorithm is much better able to track the time varying
impedances of the ventricles than the conventional method. Challenges and
problems associated with the model are discussed and a few modifications are
proposed for performance improvement.