Motion correction in PET has become more important as system resolution has improved. The purpose of this study was to evaluate the accuracy of three motion compensation methods, event-by-event motion compensation with list-mode reconstruction (MOLAR), frame-based motion correction, and post-reconstruction image registration. Motion compensated image reconstructions were carried out with simulated HRRT data, using a range of motion information based on human motion data. ROI analyses in high contrast regions were performed to evaluate the accuracy of all the motion compensation methods, with particular attention to within-frame motion.Our study showed that MOLAR with list-mode based motion correction using accurate motion data can reliably correct for all reasonable head motions. Over all motions, the average ROI count was within 0.1±4.2% and 0.7±0.9% of the reference, no-motion value for two different ROIs. The location of the ROI centroid was found to be within 0.7±0.3mm of that of the reference image for the raphe nucleus. Frame-based motion compensation and post-reconstruction image registration were able to correct for small (<5mm), but the ROI intensity begins to deteriorate for medium motions (5-10mm), especially for small brain structures such as the raphe nucleus. For large (>10mm) motions, the average centroid locations of the raphe nucleus ROI had an offset error of 1.5±1.8mm and 1.8±1.8mm for each of the frame-based methods. For each frame-based method, the decrease in the average ROI intensity was 16.9±4.3% and 20.2±9.9% respectively for the raphe nucleus, and was 5.5±2.2% and 7.4±0.2% for putamen. Based on these data, we conclude that event-by-event based motion correction works accurately for all reasonable motions, whereas frame-based motion correction is accurate only when the within-frame motion is less than 10mm.