Nonlinear forward problem solution for electrical capacitance tomography using feed-forward neural network

A new technique for solving the forward problem in electrical capacitance tomography sensor systems is introduced. The new technique is based on training a feed-forward neural network (NN) to predict capacitance data from permittivity distributions. The capacitance data used in training and testing the NN is obtained from preprocessed and filtered experimental measurements. The new technique has shown better results when compared to the commonly used linear forward projection (LFP) while maintaining fast prediction speed. The new technique has also been integrated into a modified iterative linear back projection (Landweber) reconstruction algorithm. Reconstruction results are found to be in favor of the NN forward solver when compared to the widely used Landweber reconstruction technique with LFP forward solver.

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

[2]  A. R. Borges,et al.  A quantitative algorithm for parameter estimation in magnetic induction tomography , 2004 .

[3]  I. L. Freeston,et al.  Analytic solution of the forward problem for induced current electrical impedance tomography systems , 1995 .

[4]  Weifu Fang,et al.  A nonlinear image reconstruction algorithm for electrical capacitance tomography , 2004 .

[5]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[6]  Richard A Williams,et al.  Process Tomography: Principles, Techniques and Applications , 1995 .

[7]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

[8]  V. Kvasnicka,et al.  Neural and Adaptive Systems: Fundamentals Through Simulations , 2001, IEEE Trans. Neural Networks.

[9]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[10]  Konrad Reif,et al.  Multilayer neural networks for solving a class of partial differential equations , 2000, Neural Networks.

[11]  F. Teixeira,et al.  Sensitivity matrix calculation for fast 3-D electrical capacitance tomography (ECT) of flow systems , 2004, IEEE Transactions on Magnetics.

[12]  J L Castro,et al.  Neural networks with a continuous squashing function in the output are universal approximators , 2000, Neural Networks.

[13]  H. Yan,et al.  Image reconstruction in electrical capacitance tomography using multiple linear regression and regularization , 2001 .

[14]  Gerald M. Maggiora,et al.  Computational neural networks as model-free mapping devices , 1992, J. Chem. Inf. Comput. Sci..

[15]  N. G. Gencer,et al.  Forward problem solution for electrical conductivity imaging via contactless measurements. , 1999, Physics in medicine and biology.

[16]  Jose C. Principe,et al.  Neural and adaptive systems , 2000 .

[17]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[18]  Richard A. Williams,et al.  Application of conjugate harmonics to electrical process tomography , 1996 .

[19]  A Tizzard,et al.  Solving the forward problem in electrical impedance tomography for the human head using IDEAS (integrated design engineering analysis software), a finite element modelling tool , 2001, Physiological measurement.

[20]  Richard A Williams,et al.  Process tomography: a European innovation and its applications , 1996 .

[21]  Chih-Chen Chang,et al.  Adaptive neural networks for model updating of structures , 2000 .

[22]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[23]  Maurice Beck,et al.  Tomographic imaging of two-component flow using capacitance sensors , 1989 .

[24]  Jan C. de Munck,et al.  The boundary element method in the forward and inverse problem of electrical impedance tomography , 2000, IEEE Transactions on Biomedical Engineering.

[25]  Ø. Isaksen,et al.  A review of reconstruction techniques for capacitance tomography , 1996 .