Bayesian inference based reconstruction for poisson statistics
A new reconstruction method is explored using Bayesian inference for Poisson Statistics for emission tomography. The Gamma density function is chosen as the natural choice for the activity distribution at each voxel, being the conjugate-prior of Poisson distribution. The update equations of the shape and rate parameters for Gamma distribution are derived and tested on a simple 2D example using Metropolis-Hastings algorithm. The results show promise with quick convergence within ∼20 iterations and stable noise properties with iteration. A 3D algorithm and comparison with OSEM is underway.
Publication Source (Journal or Book title)
2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015
Dey, J., Xu, J., Bhusal, N., & Shumilov, D. (2016). Bayesian inference based reconstruction for poisson statistics. 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015 https://doi.org/10.1109/NSSMIC.2015.7582216