Detector response models for statistical iterative image reconstruction in high resolution PET

One limitation in a practical implementation of an interative image reconstruction is to build a transition matrix accurately modeling the relationship between projection and image spaces. Detector response functions (DRF) in PET are broad and spatially-variant, leading to large transition matrices taking too much space to store. In this work, we investigate the effect of simpler DRF models on image quality in ML-EM reconstruction. We studied 6 cases of increasing complexity: simple tube/pixel geometric overlap; geometric overlap with tubes centered on DRF maximum; two different fixed-width Gaussians centered on DRF maximum; same as previous with a Gaussian of same spectral resolution as DRF; simulated DRF based on linear attenuation of γ-rays in detector arrays. Oversimplification of DRFs may affect image quality dramatically. Simulated DRFs generate a transition matrix that can be easily fitted into the memory of current stand-alone computers and yield images of excellent quality for a small animal PET system with long narrow detectors