In this work, a small animal PET scanner named SIAT aPET was developed using dual-ended readout depth encoding detectors to simultaneously achieve high spatial resolution and high sensitivity. The scanner consists of four detector rings with 12 detector modules per ring; the ring diameter is 111 mm and the axial field of view (FOV) is 105.6 mm. The images are reconstructed using an ordered subset expectation maximization (OSEM) algorithm. The spatial resolution of the scanner was measured by using a 22Na point source at the center axial FOV with different radial offsets. The sensitivity of the scanner was measured at center axis of the scanner with different axial positions. The count rate performance of the system was evaluated by scanning mouse-sized and rat-sized phantoms. An ultra-micro hot-rods phantom and two mice injected with 18F-NaF and 18F-FDG were scanned on the scanner. An average depth of interaction (DOI) resolution of 1.96 mm, energy resolution of 19.1% and timing resolution of 1.20 ns were obtained for the detector. Average spatial resolutions of 0.82 mm and 1.16 mm were obtained up to a distance of 30 mm radially from the center of the FOV when reconstructing a point source in 1% and 10% warm backgrounds, respectively, using OSEM reconstruction with 16 subsets and 10 iterations. Sensitivities of 16.0% and 11.9% were achieved at center of the scanner for energy windows of 250–750 keV and 350–750 keV respectively. Peak noise equivalent count rates (NECRs) of 324 kcps and 144 kcps were obtained at an activity of 26.4 MBq for the mouse-sized and rat-sized phantoms. Rods of 1.0 mm diameter can be visually resolved from the image of the ultra-micro hot-rods phantom. The capability of the scanner was demonstrated by high quality in-vivo mouse images.

In this work, a GPU-accelerated fully 3D ordered-subset expectation maximization (OSEM) image reconstruction with point spread function (PSF) modeling was developed for a small animal PET scanner with a long axial field of view (FOV). Dual-ended readout detectors that provided high depth of interaction (DOI) resolution were used for the small animal PET scanner to simultaneously achieve uniform high spatial resolution and high sensitivity. First, we developed a novel sinogram generation method, in which the dimension of the sinogram was determined first and then an event was assigned to a few neighboring sinogram elements by using weights that are inversely proportional to the distance from the measured line of response (LOR) to the LOR of the sinogram elements. System geometric symmetry, precomputation of LOR-driven ray-tracing and texture memory were applied to accelerate the GPU-based reconstruction. We developed a spatially variant PSF model where the PSF parameters were obtained by using point source images measured at 18 positions in the FOV and a spatial invariant PSF model where the PSF parameters were obtained by using only one image measured at the center FOV. The performance of the image reconstruction method was evaluated by using simulated phantom data as well as phantom and in-vivo mouse data acquired on the scanner. The results showed that the proposed reconstruction method provided better spatial resolution, a higher contrast recovery coefficient and lower noise than the OSEM reconstruction and was more than 1000 times faster than the CPU-based reconstruction. The spatially variant PSF model did not result in any spatial resolution improvement compared to the spatial invariant PSF model, and thus, the latter that is much easier to implement in image reconstruction and can be used in a small animal PET scanner using detectors with very high DOI resolution. A whole body 18F-FDG mouse image with high resolution and a high contrast to noise ratio was obtained by using the proposed reconstruction method.