Uncertainty estimation for Bayesian reconstructions from low-count spect data

Conference · Tue Dec 31 04:00:00 EST 1996

OSTI ID:459811

Cunningham, G S; Hanson, K M

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.

• Research Organization: Los Alamos National Lab., NM (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: W-7405-ENG-36
• OSTI ID: 459811
• Report Number(s): LA-UR--96-4073; CONF-961123--15; ON: DE97003124
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
99 GENERAL AND MISCELLANEOUS
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COMPUTERIZED TOMOGRAPHY
DATA ANALYSIS
DATA COVARIANCES
GAUSS FUNCTION
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MARKOV PROCESS
MONTE CARLO METHOD
NUCLEAR MEDICINE
THREE-DIMENSIONAL CALCULATIONS
TRACER TECHNIQUES
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