Implementation of Monte Carlo coincident aperture functions in image generation of a high-resolution animal positron tomograph

For imaging animals, small-diameter tomographs are designed for increased sensitivity. As a consequence, it is optimal to reconstruct a large fraction of the detector aperture. This is a challenge if resolution degradation, especially at the edge of the field-of-view, is to be minimized. Accounting for system-dependent resolution functions could therefore greatly enhance the image generation process in these tomographs. Monte Carlo techniques were used to simulate the high-resolution animal tomograph MADPET and to determine coincidence aperture functions (CAFs) as a function of position in field-of-view. Sinogram rebinning was applied with spatially varying CAFs followed by filtered backprojection. An analytical rebinning method based on the intrinsic resolution of the system was used for comparison. Furthermore, CAFs were implemented into iterative list-mode reconstruction without sinogram rebinning. Simulated and measured line sources at positions within 88% of the system diameter were analyzed to determine image resolution. Image quality was assessed with a simulated and measured Derenzo-style structure phantom. Since no deconvolution methods were used for sinogram rebinning with simulated CAFs, this method could not improve resolution degradation. Nevertheless, it provided enhanced image quality by removing sampling artifacts introduced by common rebinning techniques. The use of Monte Carlo derived probability matrices combined with iterative list-mode reconstruction proved to be adequate to improve image quality and restore spatial resolution within 88% of the detector ring aperture in a small animal positron tomograph