AUTHOR=Zhang Muyang , Aykroyd Robert G. , Tsoumpas Charalampos TITLE=Mixture prior distributions and Bayesian models for robust radionuclide image processing JOURNAL=Frontiers in Nuclear Medicine VOLUME=4 YEAR=2024 URL=https://www.frontiersin.org/journals/nuclear-medicine/articles/10.3389/fnume.2024.1380518 DOI=10.3389/fnume.2024.1380518 ISSN=2673-8880 ABSTRACT=
The diagnosis of medical conditions and subsequent treatment often involves radionuclide imaging techniques. To refine localisation accuracy and improve diagnostic confidence, compared with the use of a single scanning technique, a combination of two (or more) techniques can be used but with a higher risk of misalignment. For this to be reliable and accurate, recorded data undergo processing to suppress noise and enhance resolution. A step in image processing techniques for such inverse problems is the inclusion of smoothing. Standard approaches, however, are usually limited to applying identical models globally. In this study, we propose a novel Laplace and Gaussian mixture prior distribution that incorporates different smoothing strategies with the automatic model-based estimation of mixture component weightings creating a locally adaptive model. A fully Bayesian approach is presented using multi-level hierarchical modelling and Markov chain Monte Carlo (MCMC) estimation methods to sample from the posterior distribution and hence perform estimation. The proposed methods are assessed using simulated