A general class of preconditioners for statistical iterative reconstruction of emission computed tomography

被引：28

作者：

Chinn, G ^{[1
]}

Huang, SC ^{[1
]}

机构：

[1] UNIV CALIF LOS ANGELES,SCH MED,DEPT BIOMATH,LOS ANGELES,CA 90095

来源：

IEEE TRANSACTIONS ON MEDICAL IMAGING | 1997年 / 16卷 / 01期

关键词：

emission computed tomography; image reconstruction; statistical iterative reconstruction;

D O I：

10.1109/42.552050

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A major drawback of statistical iterative image reconstruction for emission computed tomography is its high computational cost, The ill-posed nature of tomography leads to slow convergence for standard gradient-based iterative approaches such as the steepest descent or the conjugate gradient algorithm, In this paper new theory and methods for a class of preconditioners are developed for accelerating the convergence rate of iterative reconstruction. To demonstrate the potential of this class of preconditioners, a preconditioned conjugate gradient (PCG) iterative algorithm for weighted least squares reconstruction (WLS) was formulated for emission tomography. Using simulated positron emission tomography (PET) data of the Hoffman brain phantom, it was shown that the convergence rate of the PCG can reduce the number of iterations of the standard conjugate gradient algorithm by a factor of 2-8 times depending on the convergence criterion.

引用

页码：1 / 10

页数：10

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