DocumentCode :
329933
Title :
Introduction of ordered subsets algorithm to maximum a posteriori expectation maximization method
Author :
Urabe, Hiroshi ; Ogawa, Koichi
Author_Institution :
Dept. of Electron. Inf., Hosei Univ., Tokyo, Japan
fYear :
1998
fDate :
4-7 Oct 1998
Firstpage :
394
Abstract :
Iterative reconstruction methods such as the maximum likelihood (ML)-expectation maximization (EM) method can be accelerated by introducing an ordered subsets (OS) algorithm, in which the projection data are grouped into subsets and a pixel in a reconstructed image is updated by using projections in each subset. In this paper we introduced the OS algorithm to the maximum a posteriori (MAP)-EM method and named this method OS-Bayesian reconstruction (BR). The performance of OS-BR was compared with ML-EM, MAP-EM and OS-EM on reconstructed images. The results showed that OS-BR with suitable parameters yielded higher quality images than the other methods at earlier iterations
Keywords :
Bayes methods; image reconstruction; iterative methods; maximum likelihood estimation; MAP EM method; OS-Bayesian reconstruction; iterative reconstruction methods; maximum a posteriori EM method; maximum a posteriori expectation maximization method; maximum likelihood expectation maximization; ordered subsets algorithm; projection data; quality; reconstructed image; reconstructed images; Acceleration; Bayesian methods; Computed tomography; Detectors; Image reconstruction; Iterative algorithms; Iterative methods; Pixel; Reconstruction algorithms; Single photon emission computed tomography;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-8186-8821-1
Type :
conf
DOI :
10.1109/ICIP.1998.727223
Filename :
727223
Link To Document :
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