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

被引：15
|
作者：
Khan, T
Smirnova, A
机构：
[1] Dept Math Sci, Clemson, SC 29634 USA
[2] Georgia State Univ, Dept Math & Stat, Atlanta, GA 30303 USA
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2005年 / 161卷 / 01期
基金：
美国国家科学基金会;
关键词：
inverse problems; nonlinear ill-posed; iteratively regularized Gauss-Newton; biomedical imaging; reconstruction algorithms;
D O I：
10.1016/j.amc.2003.12.019
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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.
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页码：149 / 170
页数：22
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