The spatial normalization of PET amyloid imaging data is challenging because different white and gray matter patterns of negative (Aβ−) and positive (Aβ+) uptake could lead to systematic bias if a standard method is used. In this study, we propose the use of an adaptive template registration method to overcome this problem. Methods: Data from a phase II study (n = 72) were used to model amyloid deposition with the investigational PET imaging agent 18F-flutemetamol. Linear regression of voxel intensities on the standardized uptake value ratio (SUVR) in a neocortical composite region for all scans gave an intercept image and a slope image. We devised a method where an adaptive template image spanning the uptake range (the most Aβ− to the most Aβ+ image) can be generated through a linear combination of these 2 images and where the optimal template is selected as part of the registration process. We applied the method to the 18F-flutemetamol phase II data using a fixed volume of interest atlas to compute SUVRs. Validation was performed in several steps. The PET-only adaptive template registration method and the MR imaging–based method used in statistical parametric mapping were applied to spatially normalize PET and MR scans, respectively. Resulting transformations were applied to coregistered gray matter probability maps, and the quality of the registrations was assessed visually and quantitatively. For comparison of quantification results with an independent patient-space method, FreeSurfer was used to segment each subject’s MR scan and the parcellations were applied to the coregistered PET scans. We then correlated SUVRs for a composite neocortical region obtained with both methods. Furthermore, to investigate whether the 18F-flutemetamol model could be generalized to 11C-Pittsburgh compound B (11C-PIB), we applied the method to Australian Imaging, Biomarkers and Lifestyle (AIBL) 11C-PIB scans (n = 285) and compared the PET-only neocortical composite score with the corresponding score obtained with a semimanual method that made use of the subject’s MR images for the positioning of regions. Results: Spatial normalization was successful on all scans. Visual and quantitative comparison of the new PET-only method with the MR imaging–based method of statistical parametric mapping indicated that performance was similar in the cortical regions although the new PET-only method showed better registration in the cerebellum and pons reference region area. For the 18F-flutemetamol quantification, there was a strong correlation between the PET-only and FreeSurfer SUVRs (Pearson r = 0.96). We obtained a similar correlation for the AIBL 11C-PIB data (Pearson r = 0.94). Conclusion: The derived adaptive template registration method allows for robust, accurate, and fully automated quantification of uptake for 18F-flutemetamol and 11C-PIB scans without the use of MR imaging data.
- Amyloid imaging
- Image registration