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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2001, Vol. 2 Issue (4): 406-410    DOI: 10.1631/jzus.2001.0406
Engineering Sciences     
A BAYESIAN PET RECONSTRUCTION METHOD USING SEGMENTED ANATOMICAL MEMBRANE AS PRIORS
GONG Tie-zhu, WANG Yuan-mei
The State Key Laboratory of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
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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.

Key words： positron emission tomography      bayesian reconstruction      markov chain monte carlo      segimented anatomical membrane prior     
Received: 16 October 2000     
CLC:  TP391  
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WANG Yuan-mei
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GONG Tie-zhu, WANG Yuan-mei. A BAYESIAN PET RECONSTRUCTION METHOD USING SEGMENTED ANATOMICAL MEMBRANE AS PRIORS. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2001, 2(4): 406-410.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2001.0406     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2001/V2/I4/406

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