Abstract
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.
Highlights
After the first successful study using the 11C-labeled amyloidb (Ab)–selective ligand Pittsburgh compound B [1], Ab imaging with PET has exerted a rapid and extensive influence on Alzheimer disease research
As a requisite for SUV ratio (SUVR) computation, a PET image must first be parcellated into anatomically meaningful regions; the gold standard for this type of approach requires access to a subject’s T1-weighted MR image and manual delineation of volumes of interest (VOIs) in native space
The amount of gray matter in the cerebellar gray (CG) VOI was higher for the principal-component template approach (P, 0.001), whereas the amount of white matter was lower (P, 0.001) (Supplemental Figs. 2A and B)
Summary
After the first successful study using the 11C-labeled amyloidb (Ab)–selective ligand Pittsburgh compound B [1], Ab imaging with PET has exerted a rapid and extensive influence on Alzheimer disease research. As a requisite for SUVR computation, a PET image must first be parcellated into anatomically meaningful regions; the gold standard for this type of approach requires access to a subject’s T1-weighted MR image and manual delineation of volumes of interest (VOIs) in native space This method, is time-consuming and may be subject to interreader variability; further, structural imaging can prove challenging to perform in clinical settings. Though use of a subject’s MR image stands as a possible solution to this challenge, MRI is not always available as part of routine clinical workup, highlighting the relevance of a PET-based method able to resolve the bias imposed by variability in Ab ligand uptake To overcome these problems, we developed a fully automated PET-only registration method using a synthetic template based on principal-component images. We describe the creation of the principalcomponent template model, its integration into an image registration algorithm, and the validation of our method against an MR-driven approach for spatial normalization (SPM12)
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