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Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data.
IEEE Trans Med Imaging. 1997 Oct; 16(5):516-26.IT

Abstract

In recent years, many investigators have proposed Gibbs prior models to regularize images reconstructed from emission computed tomography data. Unfortunately, hyperparameters used to specify Gibbs priors can greatly influence the degree of regularity imposed by such priors and, as a result, numerous procedures have been proposed to estimate hyperparameter values from observed image data. Many of these procedures attempt to maximize the joint posterior distribution on the image scene. To implement these methods, approximations to the joint posterior densities are required, because the dependence of the Gibbs partition function on the hyperparameter values is unknown. In this paper, we use recent results in Markov chain Monte Carlo (MCMC) sampling to estimate the relative values of Gibbs partition functions and using these values, sample from joint posterior distributions on image scenes. This allows for a fully Bayesian procedure which does not fix the hyperparameters at some estimated or specified value, but enables uncertainty about these values to be propagated through to the estimated intensities. We utilize realizations from the posterior distribution for determining credible regions for the intensity of the emission source. We consider two different Markov random field (MRF) models-the power model and a line-site model. As applications we estimate the posterior distribution of source intensities from computer simulated data as well as data collected from a physical single photon emission computed tomography (SPECT) phantom.

Authors+Show Affiliations

Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708-0251, USA. higdon@isds.duke.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

9368107

Citation

Higdon, D M., et al. "Fully Bayesian Estimation of Gibbs Hyperparameters for Emission Computed Tomography Data." IEEE Transactions On Medical Imaging, vol. 16, no. 5, 1997, pp. 516-26.
Higdon DM, Bowsher JE, Johnson VE, et al. Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data. IEEE Trans Med Imaging. 1997;16(5):516-26.
Higdon, D. M., Bowsher, J. E., Johnson, V. E., Turkington, T. G., Gilland, D. R., & Jaszczak, R. J. (1997). Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data. IEEE Transactions On Medical Imaging, 16(5), 516-26.
Higdon DM, et al. Fully Bayesian Estimation of Gibbs Hyperparameters for Emission Computed Tomography Data. IEEE Trans Med Imaging. 1997;16(5):516-26. PubMed PMID: 9368107.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data. AU - Higdon,D M, AU - Bowsher,J E, AU - Johnson,V E, AU - Turkington,T G, AU - Gilland,D R, AU - Jaszczak,R J, PY - 1997/11/22/pubmed PY - 1997/11/22/medline PY - 1997/11/22/entrez SP - 516 EP - 26 JF - IEEE transactions on medical imaging JO - IEEE Trans Med Imaging VL - 16 IS - 5 N2 - In recent years, many investigators have proposed Gibbs prior models to regularize images reconstructed from emission computed tomography data. Unfortunately, hyperparameters used to specify Gibbs priors can greatly influence the degree of regularity imposed by such priors and, as a result, numerous procedures have been proposed to estimate hyperparameter values from observed image data. Many of these procedures attempt to maximize the joint posterior distribution on the image scene. To implement these methods, approximations to the joint posterior densities are required, because the dependence of the Gibbs partition function on the hyperparameter values is unknown. In this paper, we use recent results in Markov chain Monte Carlo (MCMC) sampling to estimate the relative values of Gibbs partition functions and using these values, sample from joint posterior distributions on image scenes. This allows for a fully Bayesian procedure which does not fix the hyperparameters at some estimated or specified value, but enables uncertainty about these values to be propagated through to the estimated intensities. We utilize realizations from the posterior distribution for determining credible regions for the intensity of the emission source. We consider two different Markov random field (MRF) models-the power model and a line-site model. As applications we estimate the posterior distribution of source intensities from computer simulated data as well as data collected from a physical single photon emission computed tomography (SPECT) phantom. SN - 0278-0062 UR - https://www.unboundmedicine.com/medline/citation/9368107/Fully_Bayesian_estimation_of_Gibbs_hyperparameters_for_emission_computed_tomography_data_ L2 - https://dx.doi.org/10.1109/42.640741 DB - PRIME DP - Unbound Medicine ER -