1D inverse problem in diffusion based optical tomography using iteratively regularized Gauss-Newton algorithm

doi:10.1016/j.amc.2003.12.019

1D inverse problem in diffusion based optical tomography using iteratively regularized Gauss-Newton algorithm

2005-02-01

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DOI：10.1016/j.amc.2003.12.019

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In this paper, we investigate an one-dimensional inverse problem in diffusion based optical tomography using iteratively regularized Gauss-Newton (IRGN) algorithm for ill-posed nonlinear problems. We devise a stable reconstruction algorithm for the inverse problem using iterative regularization with Armijo-Goldstein-Wolf (AGW) type line search strategy. We demonstrate the efficacy of the IRGN combined with AGW by reconstructing the scattering parameter relevant to the inverse problem in optical tomography. (C) 2003 Elsevier Inc. All rights reserved.

Keywords:

inverse problems

nonlinear ill-posed

iteratively regularized Gauss-Newton

biomedical imaging

reconstruction algorithms

1D inverse problem in diffusion based optical tomography using iteratively regularized Gauss-Newton algorithm

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