@article {Engle417P, author = {Jonathan Engle and Dan Kadrmas}, title = {Modeling the spatially-variant point spread function in a fast projector for improved fully-3D PET reconstruction}, volume = {48}, number = {supplement 2}, pages = {417P--417P}, year = {2007}, publisher = {Society of Nuclear Medicine}, abstract = {1742 Objectives: The point spread function (PSF) in PET is spatially-variant and becomes asymmetric away from the center of the tomograph. Modeling the PSF during iterative reconstruction has the potential to improve spatial resolution. In this work we characterize the spatially-variant PSF for ring PET tomographs, and model it in a fast projector capable of performing fully-3D iterative reconstruction almost as fast as methods using rebinning followed by 2D iterative reconstruction. Methods: The PSF was characterized using point source scans on a GE Advance scanner, then investigated further by a series of GATE Monte Carlo simulations including the effects of positron range, non-collinearity, and depth-of-interaction. The PSF was modeled to good approximation using an asymmetric gaussian function, where σleft and σright varied as functions of lateral distance from the central axis. The modeled PSF was implemented in the projector (and backprojector) using spatially-variant convolution in the transverse direction and spatially-invariant convolution in the axial direction. Since the fast projector exploits symmetries of the 3D Radon transform in order to conserve in-plane computations for oblique projection, only 1 set of convolutions was necessary to model the PSF for all segments (adding only 35 sec. to the total reconstruction time). Results: The effect of modeling the PSF during iterative fully-3D ordered-subsets expectation-maximization (OSEM) reconstruction was studied using NEMA and deluxe Jaszczak phantom experiments. Images were reconstructed from raw line-of-response (LOR) data in an {\textquotedblleft}ordinary Poisson{\textquotedblright} framework, and analyzed in terms of spatial resolution, contrast and noise measures. Modeling the PSF resulted in somewhat slower recovery of image features with iteration. Analysis of background noise versus resolution and contrast revealed improved resolution and contrast for the images reconstructed with PSF modeling. Conclusions: When implemented with the fast rotate-and-slant projector, addition of the spatially-variant PSF model added only 35 sec. to the total reconstruction time (4 iterations 3D-OSEM, 128x128x35 image). Modeling the spatially-variant PSF during iterative reconstruction provides improved spatial resolution and small-object contrast at comparable noise levels. Research Support (if any): NIH R01 CA107353}, issn = {0161-5505}, URL = {https://jnm.snmjournals.org/content/48/supplement_2/417P.1}, eprint = {https://jnm.snmjournals.org/content}, journal = {Journal of Nuclear Medicine} }