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G. Wang, L. J. Schultz and J. Qi, “Bayesian image reconstruction for improving detection performance of Muon tomography,” IEEE Transaction on Image Processing, Vol. 18, No. 5, pp. 1080–1089, 2009.

has been cited by the following article:

• TITLE: Electrical Impedance Tomography Image Reconstruction Using Iterative Lavrentiev and L-Curve-Based Regularization Algorithm

  AUTHORS: Wenqin WANG, Jingye CAI, Lian YANG

  KEYWORDS: Electrical Impedance Tomography (EIT), Reconstruction Algorithm, Iterative Lavrentiev, Regularization Parameter, Inverse Problem.

  JOURNAL NAME: Journal of Electromagnetic Analysis and Applications, Vol.2 No.1, January 28, 2010

  ABSTRACT: Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution inside a medium from measurements made on its surface. The impedance distribution reconstruction in EIT is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods are often used in solving inverse problems. However, for EIT image reconstruction, the generalized Tikhonov regularization methods may lose the boundary information due to its smoothing operation. In this paper, we propose an iterative Lavrentiev regularization and L-curve-based algorithm to reconstruct EIT images. The regularization parameter should be carefully chosen, but it is often heuristically selected in the conventional regularization-based reconstruction algorithms. So, an L-curve-based optimization algorithm is used for selecting the Lavrentiev regularization parameter. Numerical analysis and simulation results are performed to illustrate EIT image reconstruction. It is shown that choosing the appropriate regularization parameter plays an important role in reconstructing EIT images.