Nonlinear conjugate gradient method for spectral tomosynthesis

被引：3
|
作者：
Landi, G. [1 ]
Piccolomini, E. Loli [2 ]
Nagy, J. [3 ]
机构：
[1] Univ Bologna, Dept Math, Bologna, Italy
[2] Univ Bologna, Comp Sci & Engn Dept, Bologna, Italy
[3] Emory Univ, Dept Math, Atlanta, GA 30322 USA
来源：
INVERSE PROBLEMS | 2019年 / 35卷 / 09期
基金：
美国国家科学基金会;
关键词：
nonlinear conjugate gradient method; nonlinear least squares; spectral tomography; digital breast tomosynthesis; total variation regularization; DIGITAL BREAST TOMOSYNTHESIS; IMAGE-RECONSTRUCTION; MINIMIZATION; ALGORITHM; SYSTEM;
D O I：
10.1088/1361-6420/ab1c94
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Image reconstruction in spectral digital breast tomosynthesis (DBT) requires solving a large-scale nonlinear inverse problem. Most numerical approaches on real data used a simplified linear (and hence incorrect) mathematical model to reduce the computational costs. The aim of this paper is to consider the use of a nonlinear conjugate gradient method for very large-scale nonlinear least squares problems, and apply it to spectral DBT. Numerical experiments on 3-dimensional phantom images illustrate the effectiveness and efficiency of the proposed scheme.
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页数：16
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