Uncertainty estimation for Bayesian reconstructions from low-count spect data

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Bayesian analysis is especially useful to apply to low-count medical imaging data, such as gated cardiac SPECT, because it allows one to solve the nonlinear, ill-posed, inverse problems associated with such data. One advantage of the Bayesian approach is that it quantifies the uncertainty in estimated parameters through the posterior probability. We compare various approaches to exploring the uncertainty in Bayesian reconstructions from SPECT data including: the standard estimation of the covariance of an estimator using a frequentist approach; a new technique called the `hard truth` in which one applies `forces` to the parameters and observes their displacements; and Markov-chain ... continued below

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6 p.

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Cunningham, G.S. & Hanson, K.M. December 31, 1996.

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Bayesian analysis is especially useful to apply to low-count medical imaging data, such as gated cardiac SPECT, because it allows one to solve the nonlinear, ill-posed, inverse problems associated with such data. One advantage of the Bayesian approach is that it quantifies the uncertainty in estimated parameters through the posterior probability. We compare various approaches to exploring the uncertainty in Bayesian reconstructions from SPECT data including: the standard estimation of the covariance of an estimator using a frequentist approach; a new technique called the `hard truth` in which one applies `forces` to the parameters and observes their displacements; and Markov-chain Monte Carlo sampling of the posterior probability distribution, which in principle provides a complete uncertainty characterization.

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6 p.

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OSTI as DE97003124

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  • Institute of Electrical and Electronic Engineers (IEEE) nuclear science symposium and medical imaging conference, Anaheim, CA (United States), 2-9 Nov 1996

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  • Other: DE97003124
  • Report No.: LA-UR--96-4073
  • Report No.: CONF-961123--15
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 459811
  • Archival Resource Key: ark:/67531/metadc686075

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Office of Scientific & Technical Information Technical Reports

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  • December 31, 1996

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  • July 25, 2015, 2:21 a.m.

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  • March 10, 2016, 1:18 p.m.

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Cunningham, G.S. & Hanson, K.M. Uncertainty estimation for Bayesian reconstructions from low-count spect data, article, December 31, 1996; New Mexico. (digital.library.unt.edu/ark:/67531/metadc686075/: accessed October 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.