Open AccessReconstruction, analysis and interpretation of posterior probability distributions of PET images, using the posterior bootstrap
Author(s) -
Marina Filipović,
Thomas Dautremer,
Claude Comtat,
Simon Stute,
Éric Barat
Publication year - 2021
Publication title -
physics in medicine and biology/physics in medicine and biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.312
H-Index - 191
eISSN - 1361-6560
pISSN - 0031-9155
DOI - 10.1088/1361-6560/ac06e1
Subject(s) - posterior probability , bayesian probability , maximum a posteriori estimation , computer science , prior probability , uncertainty quantification , artificial intelligence , a priori and a posteriori , algorithm , pattern recognition (psychology) , mathematics , statistics , machine learning , maximum likelihood , philosophy , epistemology
The uncertainty of reconstructed PET images remains difficult to assess and to interpret for the use in diagnostic and quantification tasks. Here we provide (1) an easy-to-use methodology for uncertainty assessment for almost any Bayesian model in PET reconstruction from single datasets and (2) a detailed analysis and interpretation of produced posterior image distributions. We apply a recent posterior bootstrap framework to the PET image reconstruction inverse problem and obtain simple parallelizable algorithms based on random weights and on existing maximum a posteriori (MAP) (posterior maximum) optimization-based algorithms. Posterior distributions are produced, analyzed and interpreted for several common Bayesian models. Their relationship with the distribution of the MAP image estimate over multiple dataset realizations is exposed. The coverage properties of posterior distributions are validated. More insight is obtained for the interpretation of posterior distributions in order to open the way for including uncertainty information into diagnostic and quantification tasks.