Journal
PHYSICS IN MEDICINE AND BIOLOGY
Volume 66, Issue 12, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/1361-6560/ac06e1
Keywords
PET image reconstruction; PET; MRI; posterior probability distribution; posterior bootstrap; uncertainty quantification; Bayesian inference; multimodal image reconstruction
Funding
- ITMO Cancer (France) [ANR-11-INBS-0006]
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This study introduces an easy-to-use methodology for assessing uncertainty in Bayesian models for PET image reconstruction and provides detailed analysis and interpretation of posterior image distributions. The coverage properties of posterior distributions are validated, offering more insight for incorporating uncertainty information into diagnostic and quantification tasks.
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.
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