A Bayesian PET reconstruction method using segmented anatomical membrane as priors

TP391; In this paper a fully Bayesian PET reconstruction method is presented for combining a segmented anatomical membrane a priori. The prior distributions are based on the fact that the radiopharmaceutical activi- ty is similar throughout each region and the anatomical information is obtained from...

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Bibliographic Details
Published inJournal of Zhejiang University. A. Science Vol. 2; no. 4; pp. 406 - 410
Main Authors Tie-zhu, Gong, Yuan-mei, Wang
Format Journal Article
LanguageEnglish
Published The State Key Laboratory of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China 01.10.2001
Subjects
markov chain monte carlo
bayesian reconstruction
positron emission tomography
segimented anatomical membrane prior
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Summary:TP391; In this paper a fully Bayesian PET reconstruction method is presented for combining a segmented anatomical membrane a priori. The prior distributions are based on the fact that the radiopharmaceutical activi- ty is similar throughout each region and the anatomical information is obtained from other imaging modalities such as CT or MRI. The prior parameters in prior distribution are considered drawn from hyperpriors for fully Bayesian reconstruction. Dynamic Markov chain Monte Carlo methods are used on the Hoffman brain phantom to gain estimates of the posterior mean. The reconstruction result is compared to those obtained by ML, MAP. Our results showed that the segmented anatomical membrane a priori exhibit improved the noise and resolution properties.
ISSN:1673-565X
1862-1775
DOI:10.1631/BF02840556