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Though currently approved for visual assessment only, there is evidence to suggest that quantification of amyloid-β (Aβ) PET images may reduce interreader 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, make spatial normalization challenging. In this study, we proposed a method to spatially normalize 18 F-flutemetamol images using a synthetic template based on principal-component images to overcome these challenges. &lt;strong&gt;Methods:&lt;/strong&gt; 18 F-flutemetamol PET and corresponding MR images from a phase II trial (&lt;em&gt;n&lt;/em&gt; = 70), including subjects ranging from Aβ-negative to Aβ-positive, were spatially normalized to standard space using an MR-driven registration method (SPM12). 18 F-flutemetamol images were then intensity-normalized using the pons as a reference region. Principal-component images were calculated from the intensity-normalized images. A linear combination of the first 2 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 into our registration method, by which 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 2 steps. First, coregistered 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. Second, to quantitatively compare the 2 registration methods, additional data from an ongoing study were spatially normalized using both methods, with correlation analysis done on the resulting cortical SUV ratios. &lt;strong&gt;Results:&lt;/strong&gt; 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. 18 F-flutemetamol quantification showed strong agreement between the SUV ratios for the PET- and MR-driven methods (&lt;em&gt;R&lt;/em&gt; 2 = 0.996; pons reference region). &lt;strong&gt;Conclusion:&lt;/strong&gt; The principal-component template registration method allows for robust and accurate registration of 18 F-flutemetamol images to a standardized template space, without the need for an MR image.

Spatial Normalization of18F-Flutemetamol PET Images Using an Adaptive Principal-Component Template

Spatial Normalization of¹⁸F-Flutemetamol PET Images Using an Adaptive Principal-Component Template