A trust region subproblem for 3D electrical impedance tomography inverse problem using experimental data

Image reconstruction in electrical impedance tomography (EIT) is an ill-posed nonlinear inverse problem. Regularization methods are needed to solve this problem. The results of the ill- posed EIT problem strongly depends on noise level in measured data as well as regularization parameter. In this paper, we present trust region subproblem (TRS), with the use of L-curve maximum curvature criteria to flnd a regularization parameter. Currently Krylov subspace methods especially conjugate gradient least squares (CGLS) are used for large scale 3D problem. CGLS is an e-cient technique when the norm of measured noise is exactly known. This paper demonstrates that CGLS and TRS converge to the same point on the L-curve with the same noise level. TRS can be implemented e-ciently for large scale inverse EIT problem as CGLS with no need a priori knowledge of the noise level.

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