Principles of Image Reconstruction Using Positron Emission Tomography and Maximum Likelihood Estimation Algorithms

被引：0
|
作者：
Bezbradica, Milan [1 ]
Trpovski, Zeljen [2 ]
机构：
[1] Energotehnika Juzna Backa, Dept Informat, Put Novosadskog Partizanskog Odreda 1, Novi Sad 21000, Serbia
[2] Univ Novi Sad, Fac Tech Sci, Novi Sad, Serbia
来源：
NEUREL 2008: NINTH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS | 2008年
关键词：
Image reconstruction; PET; EM; SAGE; Algorithm;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Positron Emission Tomography (PET) is used extensively in image acquisition for medical purposes. It is necessary to perform an evaluation of parameters that are required for image reconstruction based on the measured data. The most commonly used method for estimation of PET parameters is Expectation Maximization algorithm. About a decade ago a new SAGE algorithm was developed and soon it began to be used in image reconstruction. Principles and examples of these algorithms as well as of PET are described in this paper.
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页码：133 / +
页数：2
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