Reconstruction, analysis and interpretation of posterior probability distributions of PET images, using the posterior bootstrap. (17th June 2021)

• Record Type: Journal Article
• Authors: Filipović, Marina; Dautremer, Thomas; Comtat, Claude; Stute, Simon; Barat, Éric
• Is Part Of: Physics in medicine & biology. Volume 66:Number 12(2021)
• Publication Date: 2021-06-17
• Subjects: PET image reconstruction -- PET/MRI -- posterior probability distribution -- posterior bootstrap -- uncertainty quantification -- Bayesian inference -- multimodal image reconstruction; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac06e1
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: 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.