A Fully Automatic Technique for Precise Localization and Quantification of Amyloid-β PET Scans

Abstract

Spatial heterogeneity in the accumulation of amyloid-β plaques throughout the brain during asymptomatic as well as clinical stages of Alzheimer disease calls for precise localization and quantification of this protein using PET imaging. To address this need, we have developed and evaluated a technique that quantifies the extent of amyloid-β pathology on a millimeter-by-millimeter scale in the brain with unprecedented precision using data from PET scans. Methods: An intermodal and intrasubject registration with normalized mutual information as the cost function was used to transform all FreeSurfer neuroanatomic labels into PET image space, which were subsequently used to compute regional SUV ratio (SUVR). We have evaluated our technique using postmortem histopathologic staining data from 52 older participants as the standard-of-truth measurement. Results: Our method resulted in consistently and significantly higher SUVRs in comparison to the conventional method in almost all regions of interest. A 2-way ANOVA revealed a significant main effect of method as well as a significant interaction effect of method on the relationship between computed SUVR and histopathologic staining score. Conclusion: These findings suggest that processing the amyloid-β PET data in subjects' native space can improve the accuracy of the computed SUVRs, as they are more closely associated with the histopathologic staining data than are the results of the conventional approach.

Keywords: 18F-florbetaben PET; Alzheimer disease; FreeSurfer; amyloid-β; spatial normalization.

FIGURE 1.

Flowchart of our PET image reconstruction technique (native space): generation of static PET image (A), merging of PET and CT (B), coregistration (C), and FreeSurfer segmentation (D). T1∼T5 denote the 5 different transformation matrices obtained by rigid-body registrations.

FIGURE 2.

Flowchart of standard PET image reconstruction technique (standard space method): input data (A), coregistration (B), and ROI generation (C). CSF = cerebrospinal fluid; GM = gray matter; WM = white matter. T1 denotes the transformation matrices obtained by rigid-body registration, and W1 denotes the warping field obtained by nonlinear registration.

FIGURE 3.

Box-and-whisker plots with slope test parameters showing SUVR distribution obtained using native and standard methods at each level of postmortem histopathologic staging. Depicted is distribution of regional uptake for each of 7 selected ROIs in separate plots. × indicates the mean, − indicates the median, and + shows the outliers.

FIGURE 4.

Box plots Illustrate the distribution of SUVRs computed with native and standard space method at each level of histopathologic stage from aggregated data (all regions). Lines depict the regression slope computed separately for native and standard space, indicating the strength of relationship between SUVR and histopathologic staging. × indicates the mean, − indicates the median, and + shows the outliers.