The pons as reference region for intensity normalization in semi-quantitative analysis of brain ¹⁸FDG PET: application to metabolic changes related to ageing in conventional and digital control databases

In brain 2-deoxy-2-[¹⁸F]fluoro-d-glucose (¹⁸FDG) PET, scaling of tracer uptake to a reference region is essential for data analyses. However, there is currently no genuine recommendation of guidelines for visual or semi-quantitative ¹⁸FDG PET analyses [1, 2]. Proportional scaling [3], i.e., intensity normalization based on the whole-brain, and that related to specific brain areas, such as the cerebellum [4] and the pons [5], have been proposed, especially for the diagnosis of neurodegenerative disorders. Nevertheless, proportional scaling intensity normalization is biased in cases of diffuse hypometabolism leading to artefactual hypermetabolism [6]. The choice of other specific brain regions assumes that this reference region for intensity normalization is not physio-pathologically affected.

The gold standard method for brain analysis remains the absolute quantification of brain glycolytic metabolism, but it is principally used for research purposes, and its non-applicability in routine is related to the invasive measures of radioactivity determined by sampling arterial blood [7]. The best way to define a reference region for semi-quantitative analysis could be based on these absolute quantification studies, which showed that the metabolic changes related to ageing affect the quasi-totality of brain regions in healthy subjects [7].

Meanwhile, digital PET technology provides significant advances in the quality of brain ¹⁸FDG PET images mainly through improvements in spatial resolution that could modify the choice of the reference region for intensity normalization with a better visualization of small anatomical structures [8]. For this purpose, we propose to define the most appropriate region for intensity normalization in semi-quantitative analysis of brain ¹⁸FDG PET given the well-known physiological metabolic changes related to ageing in two different populations of healthy controls who underwent brain ¹⁸FDG PET with conventional and digital PET systems.

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 analysis of brain ¹⁸FDG PET images [7]. Even if these age-related changes affect the quasi-totality of brain areas [7], a more pronounced age-related effect is visualized in the frontal and temporal regions (Fig. 1), which is in accordance with previously reportss [10, 11]. 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 [5]. The normalization by the pons region allows to highlight subtle changes of ageing described in studies with absolute quantification and should thus be the more appropriate region for semi-quantification of brain ¹⁸FDG PET images. 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, even though further studies are needed to translate our results to a patient group approach. Conversely, using the pons for intensity normalization of ¹⁸FDG PET images requires to imperatively correct the statistical models from the age covariable.

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 [12]. 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. Targeting a specific brain region for normalization is nevertheless subject to the fact that we must precisely know brain areas affected by the pathology, which could be in some cases a limitation.

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 1 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 [8], even if the spatial normalization method and post-filter smoothing of images have been adapted for digital PET technology. 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.

In conclusion, the current study proposes to use the pons as a reference region for intensity normalization of semi-quantitative analysis of brain ¹⁸FDG PET with both conventional and digital PET technologies. This method is reported for the detection of metabolic changes related to ageing but should be proposed for visual as well as semi-quantitative analyses of brain ¹⁸FDG PET imaging related to other pathophysiological mechanisms as long as the pons region is free of any pathological involvement. Further studies are needed to translate these results to a patient group approach.