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Journal of Zhejiang University SCIENCE A 2001 Vol.2 No.4 P.406-410

http://doi.org/10.1631/jzus.2001.0406


A BAYESIAN PET RECONSTRUCTION METHOD USING SEGMENTED ANATOMICAL MEMBRANE AS PRIORS


Author(s):  GONG Tie-zhu, WANG Yuan-mei

Affiliation(s):  The State Key Laboratory of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s): 

Key Words:  positron emission tomography, bayesian reconstruction, markov chain monte carlo, segimented anatomical membrane prior


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GONG Tie-zhu, WANG Yuan-mei. A BAYESIAN PET RECONSTRUCTION METHOD USING SEGMENTED ANATOMICAL MEMBRANE AS PRIORS[J]. Journal of Zhejiang University Science A, 2001, 2(4): 406-410.

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Abstract: 
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 activity 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.

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Reference

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[5] Smith, A. F. M., Roberts, G. O., 1993. Bayesian computation via Gibbs sampler and related Markov chain Monte Carlo methods. J. R. Statist. Soc. B, 55:3-23.

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[7] Weir, I. S., 1997. Fully Bayesian reconstructions from single-photon emission computed tomography data. J. Amer. Statistic. Ass., 92:49-60.

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