Quantitative comparison of FBP, EM, and Bayesian reconstruction algorithms, including the impact of accurate system modeling, for the IndyPET scanner

Abstract

We quantitatively compare filtered backprojection (FBP), expectation maximization (EM), and Bayesian reconstruction algorithms as applied to the IndyPET scanner, a small to intermediate field of view dedicated research scanner. A key feature of our investigation is the use of an empirical system kernel determined from scans of line source phantoms. This kernel is incorporated into the forward operator of EM and the Bayesian reconstruction algorithms. Our results indicate that, particularly when an accurate system kernel is used, Bayesian methods can significantly improve reconstruction quality over FBP and EM.