Because preclinical imaging offers challenges and opportunities, we set out to investigate and optimize image processing techniques to measure changes in mouse brain metabolism with preclinical (18)F-FDG PET/CT. In particular, we considered the effects of scan length, image registration methods, image quantification methods, and smoothing during statistical parametric mapping (SPM). METHODS: A cohort of 12 wild-type mice was scanned on 3 occasions at an average age of 6, 10, and 14 mo. The impact of the scan length (10, 20, 30, or 40 min) was determined, and images were registered to a template based on either the PET or the CT image. Analysis was performed using SPM or predefined regions of interest (ROIs). Data were expressed in units of standardized uptake value or percentage injected dose per gram of tissue for absolute values; images were also normalized to whole-brain activity. RESULTS: Significant variability was observed in global brain (18)F-FDG uptake between animals. Normalizing images to the whole-brain activity significantly improved detection of regional changes in metabolism. Registration based on CT images provided greater power for detecting changes in metabolism than did registration based on PET images only. In line with an age-dependent decline in brain metabolism, both ROI and SPM-based methods revealed significant changes; SPM, however, was generally more sensitive and region-specific. For example, small clusters of voxels within an ROI differed significantly between ages even in the absence of significant changes in average uptake over the whole region. Finally, and contrary to expectation, we found little benefit from longer scan times yet a marked reduction in uptake from 45 to 85 min after injection and regional variations in the rate of washout. CONCLUSION: With appropriate processing, preclinical PET/CT provides a highly sensitive method for reliable identification of metabolic changes in the mouse brain.