Spatial normalization of [¹⁸F]flutemetamol PET images utilizing an adaptive principal components template

Abstract

Though currently approved for visual assessment only, there is evidence to suggest that quantification of amyloid-β (Aβ) PET images may reduce inter-reader variability and aid in the monitoring of treatment effects in clinical trials. Quantification typically involves a regional atlas in standard space, requiring PET images to be spatially normalized. Different uptake patterns in Aβ-positive and Aβ-negative subjects, however, makes spatial normalization challenging. In this study we propose a method to spatially normalize [¹⁸F]flutemetamol images, using a synthetic template based on principal component images to overcome these challenges. Methods: [¹⁸F]Flutemetamol PET and corresponding MR images from a phase II trial (n = 70), including subjects ranging from Aβ-negative to Aβ-positive, were spatially normalized to standard space using an MR driven registration method (SPM12). [¹⁸F]Flutemetamol images were then intensity normalized using the pons as reference region. Principal component images were calculated from the intensity normalized images. A linear combination of the first two principal component images was then used to model a synthetic template, spanning the whole range from Aβ-negative to Aβ-positive. The synthetic template was then incorporated in our registration method, where the optimal template was calculated as part of the registration process, providing a PET only driven registration method. Evaluation of the method was done in two steps. First, co-registered gray matter masks generated using SPM12 were spatially normalized using the PET and MR driven methods, respectively. The spatially normalized gray matter masks were then visually inspected and quantified. Secondly, to quantitatively compare the two registration methods, additional data from an ongoing study were spatially normalized using both methods with correlation analysis on the resulting cortical SUVR values. Results: All scans were successfully spatially normalized using the proposed method, with no manual adjustments performed. Both visual and quantitative comparison between the PET and MR driven methods showed high agreement in cortical regions. [¹⁸F]Flutemetamol quantification showed strong agreement between the SUVR values for the PET and MR driven methods (R2=0.996; pons reference region). Conclusion: The principal component template registration method allows for robust and accurate registration of [¹⁸F]flutemetamol images to a standardized template space, without the need for an MR image.