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Numerical realization of iteratively regularized Gauss-Newton method for electrical conductance imaging

A. D. Garnadi


The problem of conductance imaging (a.k.a impedance tomography) is to recover a spatially varying conductivity from boundary measurement, this problem is an exponentially nonlinear ill-posed problem. In this work, we investigate a two-dimensional inverse problem in conductance imaging using iteratively regularized Gauss-Newton (IRGN) algorithm for non-linear ill-posed problem.We demonstrate the efficacy of the IRGN algorithm by reconstructing
the conductivity parameter relevant to the inverse problem of conductivity imaging. The complete electrode model is used for the forward problem which is the common model in biomedical/biophysics applications


Conductance imaging, complete electrode model, iteratively regularized Gauss- Newton, finite element.

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