11C-PiB and 18F-florbetapir cortical retention ratios were highly correlated in a subset of the ADNI population (n = 32). Correlations between PiB2 and florbetapir cortical retention ratios were strong, regardless of which of the processing methods were used (Spearman ρ = 0.86–0.95), with the highest association resulting from image data that had no adjustments for uniform voxel size and smoothing. In addition, the range of florbetapir values was small relative to PiB, and this difference in radioligand scales could be accounted for by converting an existing PiB cutoff to florbetapir units (and vice versa). The resulting cutoffs were highly consistent in their assignment of Aβ-positive and -negative status for all subjects and closely matched previously reported, independently derived cutoffs for PiB and florbetapir.
The reduced range of florbetapir retention values, compared with PiB, has been reported previously (7
) and for other 18
F tracers such as florbetaben (15
). This phenomenon may be related to the influence of nonspecific white matter retention on cortical and reference regions (via partial-volume effects) that has been observed with florbetapir previously (16
) and with other 18
F ligands, including flutemetamol (17
), florbetaben (15
), and AZD-4694 (19
). However, nonspecific retention in white matter has been reported with PiB as well (20
). Because we found that PiB cortical retention ratios were higher than florbetapir values for Aβ-positive subjects, another explanation is that florbetapir has less gray matter plaque retention than PiB relative to a similar amount of white matter retention. These explanations, separately or together, may account for the higher y
-intercept values in the PiB–florbetapir regression equations relative to the PiB1–PiB2 equations (). The influence of white matter retention on estimation of cortical retention ratios for both tracers may also be exacerbated by image smoothing, possibly accounting for the slightly reduced PiB–florbetapir association with smoothed (rather than unsmoothed) image data.
Our findings suggest that image analysis methods such as spatial normalization and precise definition of cortical regions of interest minimally influence the quantification of cortical retention estimates. This result was surprising, because the methods were considerably different. The PET-template method involved spatial normalization of images and use of functionally defined cortical regions that were not restricted to gray matter, whereas the Freesurfer method used gray matter–specific regions of interest in subjects’ native space. In addition, somewhat different sets of cortical voxels were included in the average cortical retention ratios for the 2 methods; for example, the PET-template method used regions that resulted from a statistical (voxelwise) contrast, whereas the Freesurfer method used anatomically defined regions that were limited to gray matter voxels only. Finally, whether the data were at uniform voxel size or resolution, and which reference region was used to normalize cortical retention ratios, had little impact on the PiB-–florbetapir association, although the reference region did influence the scale of retention values (with use of the pons–brain stem resulting in the narrowest range of cortical values).
We also observed that cutoffs for establishing positive and negative Aβ status could be accurately transformed between radioligands and processing methods. The PiB threshold of 1.47 (14
) (based on cerebellar gray matter normalization and Freesurfer analysis) could be converted to a florbetapir threshold of 1.13 (based on whole cerebellum normalization and PET-template analysis), a value that is close to an independently derived florbetapir threshold of 1.10 (10
). Existing Aβ PET thresholds thus appear to have a high level of internal consistency, despite originating from separate datasets and processing methods. However, the PiB threshold was derived from a receiver-operating-characteristic analysis that has limited validity because it included in the negative-standard-of-truth group a proportion of amyloid-positive cognitively normal subjects. The 1.10 florbetapir threshold was therefore advantageous in that it has been further validated in histopathology studies (5
). This threshold has also been applied in recent longitudinal studies showing that Aβ-positive status in cognitively normal and MCI subjects was associated with greater cognitive decline than Aβ-negative status (2
An important limitation of this study was the relatively small size of this convenience sample and the considerable time intervals between the 2 sets of scans. Participants may have experienced changes in amyloid plaque load during the 1.5-y interval between their PiB and florbetapir scans. Indeed, 5 of 32 MCI subjects converted to AD between their PiB2 and florbetapir sessions; however, there was no evidence for a different pattern of associations for these subjects.
These analyses are an initial step in addressing the need for standardization of Aβ PET methodologies. PET image data are currently acquired using a combination of radioligands, scanner types, and analysis methods, which has raised questions about differences in the criteria for positive–negative status across radioligands and analysis pipelines. Here, we demonstrate that despite these acquisition differences, reliable numeric conversions can be made.