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Related Experiment Videos

Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data

D M Higdon1, J E Bowsher, V E Johnson

  • 1Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708-0251, USA. higdon@isds.duke.edu

IEEE Transactions on Medical Imaging
|November 22, 1997
PubMed
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This study introduces a fully Bayesian method for image reconstruction in emission computed tomography (ECT) using Markov chain Monte Carlo (MCMC) sampling. This approach propagates hyperparameter uncertainty for more accurate source intensity estimation.

Area of Science:

  • Medical Imaging
  • Computational Statistics
  • Image Reconstruction

Background:

  • Gibbs prior models are used to regularize emission computed tomography (ECT) image reconstruction.
  • Hyperparameter estimation for Gibbs priors is challenging due to unknown partition functions.
  • Existing methods often approximate joint posterior distributions.

Purpose of the Study:

  • To develop a fully Bayesian procedure for ECT image reconstruction.
  • To propagate uncertainty in hyperparameters to the estimated source intensities.
  • To utilize Markov chain Monte Carlo (MCMC) sampling for improved hyperparameter estimation.

Main Methods:

  • Employed MCMC sampling to estimate relative Gibbs partition function values.
  • Sampled from joint posterior distributions on image scenes using estimated partition functions.

Related Experiment Videos

  • Applied two Markov random field (MRF) models: power model and line-site model.
  • Utilized posterior distribution realizations to determine credible regions for source intensities.

    • Main Results:

    • Enabled a fully Bayesian approach by estimating Gibbs partition functions.
    • Successfully propagated hyperparameter uncertainty to emission source intensity estimates.
    • Demonstrated the method on simulated ECT data and a physical SPECT phantom.

    Conclusions:

    • The proposed MCMC-based method offers a robust fully Bayesian approach to ECT image reconstruction.
    • Accurate estimation of source intensities is achieved by accounting for hyperparameter uncertainty.
    • The approach is applicable to various MRF models and real-world ECT data.