Application of Markov Chain Monte Carlo for Uncertainty Quantification in Quantitative Imaging Problems
Abstract
The Differential Evolution Adaptive Metropolis (DREAM) method, a Markov chain Monte Carlo approach, is used for optimization and uncertainty analysis of a radioactive source/shield system using pixelated measured data generated by radiation imagers, where each pixel in the image contains a gammaray spectrum with statistical uncertainty. DREAM uses this spectral information to determine source thickness and source strength while also propagating uncertainty from the measured data to the solution. Using measurements simulated with GEANT4, successful parameter reconstruction is demonstrated on a numerical test case in a slab/shield geometry. In the presence of statistical noise of 59%, parameters are calculated to better than 95% with 2σ error bars that generally encompass the actual values. In a test problem with realworld measurements, source strength per energy line is calculated to within 7897% of the actual value.
 Authors:

 ORNL
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1570128
 DOE Contract Number:
 AC0500OR22725
 Resource Type:
 Conference
 Resource Relation:
 Conference: International Conference on Mathematics and Computational Methods applied to Nuclear Science and Engineering (M&C 2019)  Portland, Oregon, United States of America  8/25/2019 8:00:00 AM8/29/2019 8:00:00 AM
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Bledsoe, Keith C., Jessee, Matthew Anderson, Blackston, Matthew A., Knowles, Justin R., Ziock, KlausPeter, and Lefebvre, Jordan P. Application of Markov Chain Monte Carlo for Uncertainty Quantification in Quantitative Imaging Problems. United States: N. p., 2019.
Web.
Bledsoe, Keith C., Jessee, Matthew Anderson, Blackston, Matthew A., Knowles, Justin R., Ziock, KlausPeter, & Lefebvre, Jordan P. Application of Markov Chain Monte Carlo for Uncertainty Quantification in Quantitative Imaging Problems. United States.
Bledsoe, Keith C., Jessee, Matthew Anderson, Blackston, Matthew A., Knowles, Justin R., Ziock, KlausPeter, and Lefebvre, Jordan P. Thu .
"Application of Markov Chain Monte Carlo for Uncertainty Quantification in Quantitative Imaging Problems". United States. https://www.osti.gov/servlets/purl/1570128.
@article{osti_1570128,
title = {Application of Markov Chain Monte Carlo for Uncertainty Quantification in Quantitative Imaging Problems},
author = {Bledsoe, Keith C. and Jessee, Matthew Anderson and Blackston, Matthew A. and Knowles, Justin R. and Ziock, KlausPeter and Lefebvre, Jordan P.},
abstractNote = {The Differential Evolution Adaptive Metropolis (DREAM) method, a Markov chain Monte Carlo approach, is used for optimization and uncertainty analysis of a radioactive source/shield system using pixelated measured data generated by radiation imagers, where each pixel in the image contains a gammaray spectrum with statistical uncertainty. DREAM uses this spectral information to determine source thickness and source strength while also propagating uncertainty from the measured data to the solution. Using measurements simulated with GEANT4, successful parameter reconstruction is demonstrated on a numerical test case in a slab/shield geometry. In the presence of statistical noise of 59%, parameters are calculated to better than 95% with 2σ error bars that generally encompass the actual values. In a test problem with realworld measurements, source strength per energy line is calculated to within 7897% of the actual value.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}