用于发射型计算机断层扫描统计迭代重建的一类通用预处理方法。

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

作者信息

Chinn G, Huang S C

机构信息

Department of Molecular and Medical Pharmacology, UCLA School of Medicine 90095-6948, USA.

出版信息

IEEE Trans Med Imaging. 1997 Feb;16(1):1-10. doi: 10.1109/42.552050.

DOI:10.1109/42.552050

PMID:9050403

Abstract

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.

摘要

发射型计算机断层成像中统计迭代图像重建的一个主要缺点是其计算成本高。断层成像的不适定性质导致基于标准梯度的迭代方法（如最速下降法或共轭梯度算法）收敛缓慢。本文针对一类预处理器开发了新的理论和方法，以加快迭代重建的收敛速度。为了证明这类预处理器的潜力，针对发射型断层成像，制定了一种用于加权最小二乘重建（WLS）的预处理共轭梯度（PCG）迭代算法。使用霍夫曼脑模型的模拟正电子发射断层成像（PET）数据表明，根据收敛准则，PCG的收敛速度可将标准共轭梯度算法的迭代次数减少2至8倍。