Image reconstruction of EIT using differential evolution algorithm

Differential evolution (DE) algorithm is used in this paper to solve the inverse problem of EIT, where the cost function is determined by solving the forward problem using finite element method (FEM). This method is applied to the 2D impedance reconstruction of brain section based on real head model. Our simulations demonstrate that DE algorithm is robust in obtaining high quality reconstruction for EIT problems studied in this paper.

[1]  P. Berg,et al.  A fast method for forward computation of multiple-shell spherical head models. , 1994, Electroencephalography and clinical neurophysiology.

[2]  S. Priori,et al.  A genetic algorithm approach to image reconstruction in electrical impedance tomography , 2000, IEEE Trans. Evol. Comput..

[3]  R H Bayford,et al.  A multi-shell algorithm to reconstruct EIT images of brain function. , 2002, Physiological measurement.

[4]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[5]  Jing Bai,et al.  An efficient improvement of modified Newton-Raphson algorithm for electrical impedance tomography , 1999 .

[6]  Eung Je Woo,et al.  A robust image reconstruction algorithm and its parallel implementation in electrical impedance tomography , 1993, IEEE Trans. Medical Imaging.

[7]  David Isaacson,et al.  Electrical Impedance Tomography , 1999, SIAM Rev..

[8]  J. Lampinen.  A constraint handling approach for the differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  M. E. Glidewell,et al.  Anatomically constrained electrical impedance tomography for three-dimensional anisotropic bodies , 1997, IEEE Transactions on Medical Imaging.

[10]  Wei Wang,et al.  Effects of incompatible boundary information in EIT on the convergence behavior of an iterative algorithm , 2002, IEEE Transactions on Medical Imaging.

[11]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..