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[The selection of Tikhonov regularization parameter in dynamic electrical impedance imaging].

Yuan Peng, Yulong Mo

Open publisher page 3 citations

Abstract

Dynamic electrical impedance imaging is a non-linear inverse problem which is always discretized by finite element method and solved by linear model. Since the coefficient matrix of the linear model is ill-conditioned, the regularization method is used to get stable solution. However Tikhonov regularization is easy to realize, the selection of the regularization parameter is bothering. In this paper, according to the results of a great number of experiments, a new method for determining the regularization parameter is presented which uses the characteristic of the product of the norm of the error and the regularization solution. The reconstructed image using the regularization parameter selected by the proposed method has good spatial resolution.

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What this paper is about

Dynamic electrical impedance imaging is a non-linear inverse problem which is always discretized by finite element method and solved by linear model. Since the coefficient matrix of the linear model is ill-conditioned, the regularization method is used to get stable solution. However Tikhonov regularization is easy to realize, the selection of the regularization parameter is bothering. In this paper, according to the results of a great number of experiments, a new method for determining the regularization parameter is presented which uses the characteristic of the product of the norm of the error and the regularization solution. The reconstructed image using the regularization parameter selected by the proposed method has good spatial resolution.

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Available abstract

Dynamic electrical impedance imaging is a non-linear inverse problem which is always discretized by finite element method and solved by linear model. Since the coefficient matrix of the linear model is ill-conditioned, the regularization method is used to get stable solution. However Tikhonov regularization is easy to realize, the selection of the regularization parameter is bothering. In this paper, according to the results of a great number of experiments, a new method for determining the regularization parameter is presented which uses the characteristic of the product of the norm of the error and the regularization solution. The reconstructed image using the regularization parameter selected by the proposed method has good spatial resolution.

Key concepts: Tikhonov regularization, Regularization (linguistics), Regularization perspectives on support vector machines, Discretization, Inverse problem, Backus–Gilbert method, Applied mathematics, Coefficient matrix

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