Dynamic imaging in electrical capacitance tomography and electromagnetic induction tomography using a Kalman filter

Electrical capacitance tomography (ECT) and electromagnetic induction tomography (EMT) attempt to visualize the distributions of materials with different permittivity and conductivity/permeability, aiming to reveal electrical and magnetic characteristics of an object, by measuring electrical capacitance and electromagnetic inductance on the periphery of the object. In ECT, capacitances of pairs of electrodes placed around the periphery are measured and in EMT, mutual induction of pairs of coils is measured. In this paper, a dynamic imaging technique is developed for ECT and EMT with a linearized Kalman filter to improve the temporal resolution of images. The inverse problem is treated as a state estimate. A Kalman estimator is used to obtain the material distribution. Experimental results demonstrate that the dynamic imaging technique can improve the spatio-temporal resolution of both ECT and EMT.

[1]  Wuqiang Yang,et al.  Hardware design of electrical capacitance tomography systems , 1996 .

[2]  Jari P. Kaipio,et al.  An experimental evaluation of state estimation with fluid dynamical models in process tomography , 2007 .

[3]  Daniel Watzenig,et al.  A particle filter approach for tomographic imaging based on different state-space representations , 2006 .

[4]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[5]  E. Somersalo,et al.  State space models in process tomography — approximation of state noise covariance , 2001 .

[6]  Jari P. Kaipio,et al.  Tikhonov regularization and prior information in electrical impedance tomography , 1998, IEEE Transactions on Medical Imaging.

[7]  H. Griffiths.  Magnetic induction tomography , 2001 .

[8]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[9]  Manuchehr Soleimani,et al.  Nonlinear image reconstruction for electrical capacitance tomography using experimental data , 2005 .

[10]  Manuchehr Soleimani,et al.  Absolute Conductivity Reconstruction in Magnetic Induction Tomography Using a Nonlinear Method , 2006, IEEE Transactions on Medical Imaging.

[11]  Xiandong Ma,et al.  Hardware and software design for an electromagnetic induction tomography (EMT) system for high contrast metal process applications , 2005 .

[12]  O. Bíró.  Edge element formulations of eddy current problems , 1999 .

[13]  S. Kim,et al.  Dynamic inverse obstacle problems with electrical impedance tomography , 2004, Math. Comput. Simul..

[14]  P.A. Karjalainen,et al.  A Kalman filter approach to track fast impedance changes in electrical impedance tomography , 1998, IEEE Transactions on Biomedical Engineering.

[15]  Lihui Peng,et al.  Image reconstruction algorithms for electrical capacitance tomography , 2003 .

[16]  Hermann Scharfetter,et al.  Sensitivity maps for low-contrast perturbations within conducting background in magnetic induction tomography. , 2002, Physiological measurement.

[17]  Jari P. Kaipio,et al.  Dynamic electrical impedance tomography - phantom studies , 2000 .

[18]  Patricia Brunner,et al.  Solution of the inverse problem of magnetic induction tomography (MIT) , 2005, Physiological measurement.

[19]  M. Soleimani,et al.  A three-dimensional inverse finite-element method applied to experimental eddy-current imaging data , 2006, IEEE Transactions on Magnetics.

[20]  David Isaacson,et al.  Dynamic electrical impedance imaging of a chest phantom using the Kalman filter , 2006, Physiological measurement.

[21]  Manuchehr Soleimani,et al.  Sensitivity maps in three-dimensional magnetic induction tomography , 2006 .