List-mode reconstruction for the FOCUS-220 with motion correction and spatially-variant probability density functions: Application to awake monkey imaging

Motion correction for PET brain imaging of awake non-human primates (NHP) is critical if head fixation is not used. We have previously implemented a multi-acquisition frame (MAF) method, in which the Iist-mode data were divided into sub-frames for reconstruction, based on thresholds for intra-frame motion and minimum frame duration. In this method, residual intra-frame motion is present and scan data are discarded when large motion occurs within short time intervals. In this study, we implemented a list-mode reconstruction algorithm for the FOCUS-220 with motion correction, based on MOLAR (Motion-compensation OSEM Listmode Algorithm for Resolution-recovery Reconstruction). Also, we incorporated spatially-variant resolution kernels in the algorithm to account for varying spatial resolution throughout the field-of-view (FOV). The combination of event-by-event motion correction and spatially varying kernels is important for this application since the NHP moves its head throughout the FOV. Point sources reconstructed using MOLAR with spatially-variant resolution kernels gave 1~mm tangential resolution, better than the maximum a posteriori (MAP) method for the entire FOV. Radial resolution was below 2mm at 7cm radial offset, better than the 2.7mm FWHM for MAP. Fine structures (1.6mm diameter) of a mini-hot phantom were more clearly recovered by MOLAR than MAP at the edge of the FOV. When applied to awake NHP imaging, MOLAR with event-by-event motion correction produced better image delineation of small brain regions than the MAF -based motion correction method, and yielded images of comparable quality to anesthetized scans. We conclude that list-mode reconstruction using MOLAR with motion correction and spatially-variant resolution kernels generates high quality images for awake NHP brain PET studies.