Reference tissue normalization in longitudinal 18F-florbetapir positron emission tomography of late mild cognitive impairment
© Shokouhi et al. 2016
Received: 18 September 2015
Accepted: 4 January 2016
Published: 15 January 2016
Semiquantitative methods such as the standardized uptake value ratio (SUVR) require normalization of the radiotracer activity to a reference tissue to monitor changes in the accumulation of amyloid-β (Aβ) plaques measured with positron emission tomography (PET). The objective of this study was to evaluate the effect of reference tissue normalization in a test–retest 18F-florbetapir SUVR study using cerebellar gray matter, white matter (two different segmentation masks), brainstem, and corpus callosum as reference regions.
We calculated the correlation between 18F-florbetapir PET and concurrent cerebrospinal fluid (CSF) Aβ1–42 levels in a late mild cognitive impairment cohort with longitudinal PET and CSF data over the course of 2 years. In addition to conventional SUVR analysis using mean and median values of normalized brain radiotracer activity, we investigated a new image analysis technique—the weighted two-point correlation function (wS2)—to capture potentially more subtle changes in Aβ-PET data.
Compared with the SUVRs normalized to cerebellar gray matter, all cerebral-to-white matter normalization schemes resulted in a higher inverse correlation between PET and CSF Aβ1–42, while the brainstem normalization gave the best results (high and most stable correlation). Compared with the SUVR mean and median values, the wS2 values were associated with the lowest coefficient of variation and highest inverse correlation to CSF Aβ1–42 levels across all time points and reference regions, including the cerebellar gray matter.
The selection of reference tissue for normalization and the choice of image analysis method can affect changes in cortical 18F-florbetapir uptake in longitudinal studies.
Amyloid-β (Aβ) plaques and neurofibrillary tau tangles are known pathological features of Alzheimer’s disease (AD) [1, 2] that manifest years before the onset of clinical symptoms [3–8]. Aβ plaques are identified in vivo using brain positron emission tomography (PET) with several radiotracers, including 11C-Pittsburgh Compound B (11C-PiB) , 18F-florbetapir , 18F-FDDNP , 18F-florbetaben , and 18F-flutemetamol . The standardized uptake value ratio (SUVR) is a semiquantitative method frequently used in clinical trials of antiamyloid drugs to monitor the accumulation and progression of Aβ plaques and to assess the effects of antiamyloid drug therapy. The SUVR method is used in most large studies because it is easily calculated and does not require long dynamic scans or measurement of the arterial input function. Nevertheless, it requires normalization of regional PET activity to a reference tissue to account for nonspecific radiotracer binding. Because 11C-PiB and 18F-florbetapir target predominately the classic core and neuritic Aβ plaques, which are not evidenced in the cerebellum [14–17], whole cerebellum (or the cerebellar gray matter) is commonly used as a reference region [18, 19]. However, recent research raises new concerns about the accuracy of the SUVR measures using cerebellar normalization. In particular, the variability observed in the longitudinal progression of SUVR values seems to be discrepant with the expected values on the basis of pathological and biological grounds.
Summary of previous longitudinal 18F-florbetapir PET studies for comparison between reference tissues for normalization of PET activity
Brendel et al. 
Chen et al. 
Landau et al. 
Cerebellar gray matter
MCI (n = 483)
MCI (n = 187)
AD (n = 163)
AD (n = 31)
CSF+ (n = 91)
HC (n = 316)
HC (n = 114)
Discrimination power between subject groups, variability in longitudinal increase in Aβ-PET
Longitudinal increase in Aβ-PET and association with clinical decline
Physiological plausible longitudinal increase in Aβ-PET
Best reference region
White matter/brainstem with partial volume correction
Reference regions containing white matter
The objective of our present work was to complement the previous research by the use of a new PET image analytical method as well as longitudinal data of both CSF Aβ1–42 levels and 18F-florbetapir images to identify which reference region normalization results in the optimal visit-to-visit correlation between these two biomarkers of AD pathology. The subjects in this study were those diagnosed with late mild cognitive impairment (LMCI) from the ADNI 2 phase of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with stable CSF Aβ1–42 levels at baseline and at 24-month follow-up; thus, longitudinal changes in 18F-florbetapir PET were not expected to occur, which allows use of their PET images as a test–retest dataset to evaluate the effect of reference region normalization. All PET images are analyzed with the conventional SUVR mean and median measures and with a new PET image cluster analysis tool based on a weighted two-point correlation (wS2). The wS2 method is a statistical tool adopted from astronomy and materials science and can be used to detect specific changes in spatial patterns within Aβ-PET images that we refer to as increased clustering or flocculence. Our preliminary data  indicate the potential utility of this method for detecting longitudinal changes that are difficult to assess with conventional regional mean image values, which typically have large standard deviations.
Alzheimer’s Disease Neuroimaging Initiative
Data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), PET, other biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of MCI and early AD.
Data from 21 ADNI subjects with LMCI were used in our study. We included all subjects with LMCI who had 18F-florbetapir PET and T1-weighted MRI images at baseline and 24-month follow-up scans following the PET technical procedures of the ADNI 2 phase. We further limited our subject selection to patients with LMCI who had longitudinal CSF data obtained at time points close to their PET baseline and follow-up scans. The specific selection of the LMCI subject group from the ADNI 2 phase was based on their stable levels of CSF Aβ1–42, which allowed use of their corresponding longitudinal PET images as a test–retest dataset. While our selection criteria limited the number of available subjects, one of the main advantages of using the ADNI 2 data was the commonality of the image acquisition protocols, which ensured consistency of data within and between sites and thus reduced heterogeneity that would have otherwise added to the variability of both longitudinal and cross-sectional data. The biomarker datasheet containing the CSF Aβ1–42 levels was downloaded from the ADNI archive. The dataset is named UPENN–CSF Biomarkers [ADNI GO/2] version 2013-10-31.
Clinical and demographic data of the ADNI subjects in this study
Baseline age, yr
73 ± 8
APOE A1/A2 carriers, n
Time between PET scan and CSF, days
6 ± 14
CSF Aβ1–42, pg/ml
165 ± 45 (baseline) and 161 ± 46 (follow-up)
18 ± 7 (baseline) and 19 ± 10 (follow-up)
Clinical Dementia Rating score
0.5 ± 0 (baseline) and 0.5 ± 0.3 (follow-up)
Mini Mental State Examination score
28 ± 2 (baseline) and 26 ± 3 (follow-up)
Data acquisition, image reconstruction, and preprocessing
All patient data were acquired at participating ADNI sites. 18F-florbetapir PET, together with concurrent T1-weighted MRI volumes at baseline and 24-months follow-up, were downloaded from the ADNI database. The detailed description of the acquisition protocol can also be found on the ADNI website (http://adni.loni.usc.edu/). According to the ADNI protocols, a 370-MBq bolus injection of radiotracer was administered. This was followed by a 20-minute continuous brain PET imaging session that began approximately 50 minutes after the injection. The images were reconstructed immediately after the 20-minute scan according to scanner-specific reconstruction protocols, each using different versions of a maximum likelihood algorithm, to assess the scan quality and potential presence of motion artifacts. All images were corrected for attenuation and scatter according to the scanner-specific protocols. Upon completion, the imaging data were uploaded to the data archive of the Laboratory of Neuro Imaging at the University of Southern California, where they were coregistered and averaged. These are the datasets used in this study.
In addition to the SUVR mean and median values, we also calculated the wS2 of the florbetapir PET images. The wS2 method is a statistical image analytical method commonly used in astronomy  and materials science . With this method, we derived a quantitative parameter from PET images to characterize the heterogeneity of the Aβ-PET activity distribution, which we refer to as the clustering or flocculence. The wS2 analysis was also implemented with normalized Aβ-PET images. However, unlike the regional mean and median values, changes in wS2 more specifically reflect changes in the spatial patterns of activity. Thus, these changes are potentially less sensitive to minor temporal variations in the reference tissue activity (variations in normalization threshold). PET analysis using the wS2 method also results in smaller standard errors and thus may be more suitable for detecting subtle changes due to the larger effect size. The theoretical framework of wS2 is described in our previous work where this method was validated with 11C-PiB PET data .
Spearman’s rank correlation between CSF Aβ1–42 and PET measures
White matter (10 %)
White matter (100 %)
The results of this study show that analysis of 18F-florbetapir PET data normalized to white matter reference regions results in a higher inverse correlation to CSF Aβ1–42 and that this correlation exhibits less variability over time compared with 18F-florbetapir PET data that are normalized to cerebellar gray matter (Table 3, Fig. 9). These findings are in agreement with recent studies [20–23] in which researchers investigated the effect of reference tissue normalization using a significantly larger number of ADNI subjects. This good agreement despite a smaller cohort in our study could be partially attributed to our subject selection, which consisted of ADNI2 patients with LMCI. As described in the Methods section, the image acquisition protocols of ADNI 2 were designed to ensure consistency of data within and between sites. All our subjects had 18F-florbetapir PET scans at baseline and 24-month follow-up using the same (within-subject) scanner and the same image reconstruction and correction methods. These factors may have helped to reduce potential heterogeneities within this cohort that would otherwise have added to variability in both longitudinal and cross-sectional data.
Another advantage of ADNI 2 is the availability of concurrent CSF Aβ1–42 at both baseline and 24-month follow-up time points, which allowed us to use them as a reference method to correlate with PET data at two different time points. On the basis of their stable CSF Aβ1–42, the brain amyloid levels of these subjects were not expected to change between baseline and the 24-month follow-up PET scans, thus making the 18F-florbetapir PET images from this cohort an appropriate dataset for test–retest variability assessment of reference region normalization. The observed stable CSF Aβ1–42 was not unexpected for subjects with LMCI, because it is known that the biomarkers of amyloid deposition approach a plateau by the onset time of LMCI and clinical AD .
Using a cohort with stable CSF Aβ1–42, our objective was to find a reference tissue that would give the highest and most stability (lowest variability) in Spearman’s rank correlation between these two biomarkers calculated at two time points. While all white matter–normalized SUVRs indicated higher correlation to CSF measures than the cerebellar normalization, the brainstem normalization gave the best results among the white matter regions despite its location at the edge of the PET scanner field of view (FOV). The location of the cerebellum was suspected to be the main reason for variability observed in the previous studies [21, 22]. Due to their location, both brainstem and cerebellum are subject to increased scatter and decreased geometric sensitivity. However, PET data undergo rigorous attenuation, scatter, and normalization corrections to ensure uniformity within the FOV. Also, given that in our study the correlation values for cerebellar normalization were at their lowest levels for both baseline and follow-up time points, other factors, such as biological effects, could be more relevant than scanner-related physical effects. The connecting lines in Fig. 4 show that the within-subject differences between baseline and follow-up PET data (mainly intermediate SUVR mean and median values) were larger than all other normalization schemes (Figs. 5, 6, 7 and 8). In these figures, it is also apparent that the association between all three PET analytical methods and CSF measures become increasingly nonlinear as the PET values increase. This nonlinearity effect was most prominent when cerebellar gray matter was used as reference tissue (Fig. 4), where the CSF data of SUVR mean and median values below 1.5 were clustered around 200 pg/ml and the CSF data of SUVR mean and median values above 1.5 corresponded to CSF measures that remained around 125 pg/ml. All other white matter normalization schemes resulted in slightly more linear associations with CSF measures, in particular for intermediate PET values.
We included CSF because Aβ accumulation has been hypothesized to result from an imbalance between Aβ production and clearance [2, 32–35]. In particular, the impairment of clearance mechanisms seems to be the main cause of Aβ accumulation in sporadic or late-onset forms of AD , which account for the majority of patients with AD. In several previous studies, researchers have observed a relationship between cortical amyloid tracer binding and levels of CSF Aβ1–42 using 11C-PiB  and 18F-florbetapir . These studies, which were based on cerebellar normalization, showed that the CSF levels decreased with increased radiotracer uptake but reached a plateau at higher SUVR values. We made a similar observation with cerebellum normalization (Fig. 4a and b). Other reference region normalizations, the brainstem in particular, resulted in more linear relationships across a wide range of cortical radiotracer uptake values at both baseline and follow-up. We emphasize on the importance of this observation because the axial location of the cerebellum (increased scatter and attenuation) accounted for the observed longitudinal variabilities in previous studies. However, scanner-related effects would affect the PET–CSF association within the whole spectrum of SUVR values. Also, both the brainstem and the cerebellum are equally subject to increased scatter and decreased geometric sensitivity. Our approach might indicate that the variability associated with the reference region normalization may more likely be related to biological factors than to scanner-related effects.
Four different white matter masks (white matter 10 %, white matter 100 %, brainstem, and splenium of corpus callosum) were applied. While the white matter 10 % included the white matter regions that shared borders with gray matter, these regions were removed in the 100 % white matter mask. Correlation values from these two white matter masks and the corpus callosum were similar.
The wS2 technique was used as an additional method complementary to the conventional SUVR analysis that is performed by calculating regional mean and median SUVR values. Compared with the SUVR mean and median values, the wS2 metric was associated with the highest average Spearman’s rank correlation across all time points and reference regions, including the cerebellar gray matter. Given that the wS2 metric is based on changes in image spatial patterns, we expected that this method would be slightly less sensitive to minor temporal variations in reference region radiotracer activity, which would cause variations in normalization thresholds. The wS2 method evaluates associations between voxel values at different distances. These associations remain preserved, to some extent, even when the normalization threshold varies.
To date, we have applied the wS2 analysis with two different radiotracers (11C-PiB and 18F-florbetapir) and have been able to show consistent results. Using a statistical analysis, we evaluated the effect of injected dose (as a surrogate for image noise) and the region size on the wS2 outcomes and made a comparison with SUVR mean and median values. We obtained high and stable correlations between CSF Aβ levels and wS2 outcomes with both radiotracers. Further validations would require a full quantitative analysis using kinetic modeling and dynamic acquisitions. Our main future objective is to test the wS2 methodology with dynamic PET scans and list-mode data acquisition to investigate how different image acquisition (starting time point and duration) and reconstruction parameters (number of iterations and noise regularization) can change the image spatial patterns and subsequently the wS2 outcomes. Image preprocessing is another important factor. Spatial resolutions of human PET scanners range from greater than 2.5-mm full-width half-maximum (FWHM) in some research scanners to greater than 7-mm FWHM in many commonly used clinical PET systems [38–40]. Additional preprocessing steps, such as image smoothing, further reduce the image resolution from 7- to 12-mm FWHM. For example, most reported ADNI analyses use level 4 preprocessed imaging data, which are smoothed to a uniform isotropic resolution of 8-mm FWHM . The smoothing process is beneficial for cross-sectional comparisons and for qualitative visual reads by clinicians, due to the improved uniformity. However, it has a disadvantage in that potentially important high-resolution spatial patterns are smoothed away . The spatial smoothing of within-subject longitudinal can reduce the effect size . We are the first group, to our knowledge, to propose a method designed to improve understanding of the nature of nonuniform spatial activity patterns that explain the impact of spatial smoothing on longitudinal changes.
The selection of reference tissue for normalization of 18F-florbetapir PET images as well as the image analysis method can modify the quantitative outcomes in longitudinal studies. Understanding factors that contribute to temporal variations of reference region radiotracer uptake merits further investigation.
Alzheimer’s Disease Assessment Scale–Cognitive subscale
Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s Disease Neuroimaging Initiative composite memory score
area under the curve
11C-Pittsburgh Compound B
field of view
late mild cognitive impairment
mild cognitive impairment
Mini Mental State Examination
magnetic resonance imaging
positron emission tomography
region of interest
standardized uptake value
standardized uptake value ratio
- wS2 :
weighted two-point correlation function
The data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of MCI and early AD. Data collection and sharing for this project was funded by National Institutes of Health [NIH] grant U01 AG024904) and U.S. Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following entities: AbbVie; the Alzheimer’s Association; the Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. This study was supported by NIH grants R00 EB009106 (to SS) and K23 NS080988 (to DC). The authors thank Todd Peterson and Noor Tantawy at Vanderbilt University Institute of Imaging Science and Garry Smith at Vanderbilt University and VA Medical Center-Nashville for supportive discussions. The data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or the writing of this report. A complete listing of ADNI investigators can be found at https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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