The present study shows that the pons is the best brain region for intensity normalization of brain 18FDG PET scans for the detection of metabolic changes related to ageing. These findings are reinforced by the fact that our results are duplicated in two independent populations of healthy controls with both conventional and digital PET systems.
Metabolic changes related to ageing, which are physiological changes that have been widely studied, have been highlighted in well-conducted studies involving absolute glycolytic quantification, which remains the gold standard for normalization in intensity of brain 18FDG PET images . Even if these age-related changes affect the quasi-totality of brain areas , a more pronounced age-related effect is visualized in the frontal and temporal regions (Fig. 1), which is in accordance with previously reported results in semi-quantitative analyses . Interestingly, our results indicate that the most suitable regions for intensity normalization of brain 18FDG PET scans are the pons and the cerebellum (Table 1), these two regions being known to be poorly affected by age-related changes. Among both regions, intensity normalization by the pons exhibits the best performances after SPM analyses (Fig. 1). This region has previously been proposed as a reference for the detection of Alzheimer’s disease . As long as the pons is free of any pathological involvement, this region should be recommended for visual as well as semi-quantitative analyses of brain 18FDG PET imaging related to other conditions.
Histogram-based methods have been also recently proposed to improve the intensity PET normalization. These results were however obtained in healthy subjects with artificial introduced hypometabolisms . These data-driven methods need group of patients whereas the pons allows the intensity normalization of brain PET images at the individual level, easily applicable for the visual analysis in clinical routine.
Our results are strengthened by the fact that they have been obtained twice, after regional correlation analyses and voxel-to-voxel analyses. Moreover, these results are visualized in two different populations for which brain 18FDG PET scans have been acquired with two different PET technology systems.
Of note, more significant results were observed when using the population having performed a brain 18FDG PET with the digital camera than those having performed brain PET images with the conventional system (Table and Fig. 1). These results are in accordance with the higher performance parameters provided with the new digital PET systems when compared to conventional PET systems , even if the spatial normalization method and post-filter smoothing of images have been adapted for digital PET technology. In addition, the intensity normalization by the pons showed better correlation performances with age when using the digital system. This highlights that digital PET technology modifies the results of PET intensity normalization with its ability to delineate small anatomical structures such as the pons.