期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 98, 期 3, 页码 261-270出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2009.09.003
关键词
Iterative reconstruction; Computed tomography; Commodity graphics hardware; GPU
类别
资金
- Howard Hughes Medical Institute Funding Source: Medline
- NIBIB NIH HHS [R21 EB004099-01A1, R21 EB004099-02, R21 EB004099-01] Funding Source: Medline
- NIGMS NIH HHS [GM31627] Funding Source: Medline
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R21EB004099] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM031627] Funding Source: NIH RePORTER
Expectation Maximization (EM) and the Simultaneous Iterative Reconstruction Technique (SIRT) are two iterative computed tomography reconstruction algorithms often used when the data contain a high amount of statistical noise, have been acquired from a limited angular range, or have a limited number of views. A popular mechanism to increase the rate of convergence of these types of algorithms has been to perform the correctional updates within subsets of the projection data. This has given rise to the method of Ordered Subsets EM (OS-EM) and the Simultaneous Algebraic Reconstruction Technique (SART). Commodity graphics hardware (GPUs) has shown great promise to combat the high computational demands incurred by iterative reconstruction algorithms. However, we find that the special architecture and programming model of GPUs add extra constraints on the real-time performance of ordered subsets algorithms, counteracting the speedup benefits of smaller subsets observed on GPUs. This gives rise to new relationships governing the optimal number of subsets as well as relaxation factor settings for obtaining the smallest wall-clock time for reconstruction a factor that is likely application-dependent. In this paper we study the generalization of SIRT into Ordered Subsets SIRT and show that this allows one to optimize the computational performance of GPU-accelerated iterative algebraic reconstruction methods. (C) 2009 Elsevier Ireland Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据