Tracking Conductivity Variations in The Absence of Accurate State Evolution Models in Electrical Impedance Tomography
Paper in proceeding, 2010

We present results on both linear and non-linear approaches in tracking conductivity variations in electrical impedance tomography. Throughout this study, we use both synthetic and measured data. The true system dynamics is considered as unknown and modelled as a random walk. In the linear reconstructions, the time evolution model is augmented with a Gaussian smoothness prior and results are shown using two different models for the covariance matrix of the process noise. Furthermore, we compare the reconstructions of the one step Gauss-Newton method to the Kalman filter on measured data from an adult human subject. In the non-linear study, we compare the performance of the extended Kalman filter against the particle filter on a simple test case. It is observed that the particle filter shows superior performance in tracking nonlinear/non-Gaussian conductivity variations.

Author

Parham Hashemzadeh

V Sahota

M.F. Callaghan

H.E. Dib

Lennart Svensson

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Richard Bayford

Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering

Subject Categories

Bioinformatics and Systems Biology

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

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Created

10/7/2017