PT  - JOURNAL ARTICLE
AU  - Johan Lilja
AU  - Antoine Leuzy
AU  - Konstantinos Chiotis
AU  - Irina Savitcheva
AU  - Jens Sörensen
AU  - Agneta Nordberg
TI  - Spatial Normalization of <sup>18</sup>F-Flutemetamol PET Images Using an Adaptive Principal-Component Template
AID  - 10.2967/jnumed.118.207811
DP  - 2019 Feb 01
TA  - Journal of Nuclear Medicine
PG  - 285--291
VI  - 60
IP  - 2
4099  - http://jnm.snmjournals.org/content/60/2/285.short
4100  - http://jnm.snmjournals.org/content/60/2/285.full
SO  - J Nucl Med2019 Feb 01; 60
AB  - 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 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 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. 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 SUV ratios 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.