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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.
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 modals 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.