Bayesian Modelling with Locally Adaptive Prior Parameters in Small Animal Imaging

Muyang Zhang ¹ Robert G Aykroyd ^1* Charalampos Tsoumpas ^1,2

• ¹ University of Leeds, Leeds, United Kingdom
• ² Department of Nuclear Medicine and Molecular Imaging, Faculty of Medical Sciences, University of Groningen, Groningen, Netherlands

Medical images suffer from noise and relatively low resolution which create a bottleneck in obtaining accurate and precise measurements of living organisms. Noise suppression and resolution enhancement are two examples of inverse problems. The aim of this study is to develop novel and robust estimation approaches rooted in fundamental statistical concepts, which could be utilized in solving several inverse problems in image processing and potentially in image reconstruction. In this study we have implemented Bayesian methods which have been identified to be particularly useful when there is only limited data but a large number of unknowns.Specifically, we implemented a locally adaptive Markov chain Monte Carlo algorithm and analyzed its robustness by varying its parameters and exposing it to different experimental setups. As application area we selected radionuclide imaging using a prototype gamma camera. The results using simulated data compare estimates using the proposed method over the current non-locally adaptive approach in terms of edge recovery, uncertainty and bias. The locally adaptive Markov chain Monte Carlo algorithm is more flexible which allows better edge recovery whilst reducing estimation uncertainty and bias. This results in more robust and reliable outputs for medical imaging applications, leading to improved interpretation and quantification. We have shown the use of locally-adaptive smoothing improves estimation accuracy compared to the homogeneous Bayesian model.