Voxel-based statistical analysis and quantification of amyloid PET in the Japanese Alzheimer's disease neuroimaging initiative (J-ADNI) multi-center study

Abstract

Background: Amyloid PET plays a vital role in detecting the accumulation of in vivo amyloid-β (Aβ). The quantification of Aβ accumulation has been widely performed using the region of interest (ROI)-based mean cortical standardized uptake value ratio (mcSUVR). However, voxel-based statistical analysis has not been well studied. The purpose of this study was to examine the feasibility of analyzing amyloid PET scans by voxel-based statistical analysis. The results were then compared to those with the ROI-based mcSUVR. In total, 166 subjects who underwent ¹¹C-PiB PET in the J-ADNI multi-center study were analyzed. Additionally, 18 Aβ-negative images were collected from other studies to form a normal database. The PET images were spatially normalized to the standard space using an adaptive template method without MRI. The mcSUVR was measured using a pre-defined ROI. Voxel-wise Z-scores within the ROI were calculated using the normal database, after which Z-score maps were generated. A receiver operating characteristic (ROC) analysis was performed to evaluate whether Z-sum (sum of the Z-score) and mcSUVR could be used to classify the scans into positive and negative using the central visual read as the reference standard. PET scans that were equivocal were regarded as positive.

Results: Sensitivity and specificity were respectively 90.8% and 100% by Z-sum and 91.8% and 98.5% by mcSUVR. Most of the equivocal scans were subsequently classified by both Z-sum and mcSUVR as false negatives. Z-score maps correctly delineated abnormal Aβ accumulation over the same regions as the visual read.

Conclusions: We examined the usefulness of voxel-based statistical analysis for amyloid PET. This method provides objective Z-score maps and Z-sum values, which were observed to be helpful as an adjunct to visual interpretation especially for cases with mild or limited Aβ accumulation. This approach could improve the Aβ detection sensitivity, reduce inter-reader variability, and allow for detailed monitoring of Aβ deposition.

Trial registration: The number of the J-ADNI study is UMIN000001374.

Keywords: 11C-PiB; Amyloid; PET; Voxel-based statistical analysis; Z-score.

Fig. 1

Eighteen cases of Aβ-negative ¹¹C-PiB PET images used to create the normal database. The clinical diagnosis of each case is described in the upper left

Fig. 2

The empirical PiB-prone (EPP) ROI template

Fig. 3

Average (left) and standard deviation (SD) (right) maps created as the normal database. The SD map has been masked by the empirical PiB-prone (EPP) region of interest template

Fig. 4

The workflow of the voxel-based statistical analysis

Fig. 5

Representative positive and negative cases and their Z-score maps depicted in MNI space

Fig. 6

mcSUVR and Z-sum values for positive, equivocal, and negative groups (a, b) as well as those for normal control, mild cognitive impairment, and Alzheimer’s disease groups (c, d)

Fig. 7

PET images and Z-score maps in MNI space, and the three physicians’ visual read and consensus comments for three representative cases, in which official consensus interpretation was “equivocal.” There are the cases in which three physicians' visual read matched (a) or did not match (b and c). The Z-score maps correctly delineated abnormal Aβ accumulation over the same regions as the visual read (red arrows). F, L, Pa, Po, and S are frontal lobe, lateral temporal lobe, lateral parietal lobe, posterior cingulate gyrus and precuneus, and striatum, respectively. Class., Nega., Posi., and Equiv. are classification, negative, positive, and equivocal, respectively. The circle and triangle represent the positive and equivocal region, as judged by the physicians