Tracer-specific reference tissues selection improves detection of 18F-FDG, 18F-florbetapir, and 18F-flortaucipir PET SUVR changes in Alzheimer’s disease

Yanxiao Li1,2 | Yee Ling Ng1 | Manish D. Paranjpe3 | Qi Ge1 | Fengyun Gu1,4 |

Panlong Li5 | Shaozhen Yan6 | Jie Lu6 | Xiuying Wang2 | Yun Zhou1 |

for the Alzheimer’s Disease Neuroimaging Initiative

1Central Research Institute, United Imaging

Healthcare Group Co., Ltd, Shanghai, China

2School of Computer Science, The University

of Sydney, Sydney, New South Wales,


3Harvard-MIT Health Sciences and

Technology Program, Harvard Medical School,

Boston, Massachusetts, USA

4Department of Statistics, University College

Cork, Cork, Ireland

5School of Electrical and Information

Engineering, Zhengzhou University of Light

Industry, Zhengzhou, Henan, China

6Department of Radiology and Nuclear

Medicine, Xuanwu Hospital, Capital Medical

University, Beijing, China


Yun Zhou, United Imaging Healthcare Group

Co. Ltd, 2258 Chengbei Road, Jiading District,

Shanghai 201807, China.

Email: yun.zhou@united-imaging.com

Xiuying Wang, School of Computer Science,

The University of Sydney, City Road,

Camperdown/Darlington NSW 2006,


Email: xiu.wang@sydney.edu.au


This study sought to identify a reference tissue-based quantification approach for

improving the statistical power in detecting changes in brain glucose metabolism,

amyloid, and tau deposition in Alzheimer’s disease studies. A total of 794, 906, and

903 scans were included for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir, respec-

tively. Positron emission tomography (PET) and T1-weighted images of participants

were collected from the Alzheimer’s disease Neuroimaging Initiative database,

followed by partial volume correction. The standardized uptake value ratios (SUVRs)

calculated from the cerebellum gray matter, centrum semiovale, and pons were eval-

uated at both region of interest (ROI) and voxelwise levels. The statistical power of

reference tissues in detecting longitudinal SUVR changes was assessed via paired

t-test. In cross-sectional analysis, the impact of reference tissue-based SUVR differ-

ences between cognitively normal and cognitively impaired groups was evaluated by

effect sizes Cohen’s d and two sample t-test adjusted by age, sex, and education

levels. The average ROI t values of pons were 86.62 and 38.40% higher than that of

centrum semiovale and cerebellum gray matter in detecting glucose metabolism

decreases, while the centrum semiovale reference tissue-based SUVR provided

higher t values for the detection of amyloid and tau deposition increases. The three

reference tissues generated comparable d images for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir and comparable t maps for 18F-florbetapir and 18F-flortaucipir, but

pons-based t map showed superior performance in 18F-FDG. In conclusion, the

tracer-specific reference tissue improved the detection of 18F-FDG, 18F-florbetapir,

and 18F-flortaucipir PET SUVR changes, which helps the early diagnosis, monitoring

of disease progression, and therapeutic response in Alzheimer’s disease.

Abbreviations: ACR, annual change rate; AD, Alzheimer’s disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative; ATN, amyloid, tau, and neurodegeneration; Aβ, amyloid-β; CDR, clinical

dementia rating; CI, cognitively impaired; CN, cognitively normal; GM, gray matter; MCI, mild cognitively impaired; MMSE, Mini-Mental Status Examination; MNI, Montreal Neurologic Institute;

PVC, partial volume correction; ROI, region of interest; SUVR, standardized uptake value ratio.

Received: 15 September 2021 Revised: 17 December 2021 Accepted: 30 December 2021

DOI: 10.1002/hbm.25774

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any

medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

© 2022 United Imaging Healthcare. Human Brain Mapping published by Wiley Periodicals LLC.

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K E YWORD S 18F-FDG, 18F-florbetapir, 18F-flortaucipir, Alzheimer’s disease, reference tissue


Alzheimer’s disease (AD) is a progressive neurodegenerative disease

associated with memory deficits and cognitive impairments, brain

deposition of amyloid-β (Aβ) peptide plaques, neurofibrillary tangles

composed of hyperphosphorylated tau (ptau) protein, and glucose

hypometabolism (DeTure & Dickson, 2019; Serrano-Pozo, Frosch,

Masliah, & Hyman, 2011; Uddin et al., 2018). The standardized assess-

ment of pathological processes underlying AD can be accomplished

by biomarker-evidenced amyloid, tau, and neurodegeneration (ATN)

framework (Jack Jr et al., 2018). Positron emission tomography (PET)

using radiolabeled ligands including 18F-FDG, 18F-florbetapir, and 18F-flortaucipir has been widely used to assess neurodegeneration,

deposition of Aβ fibrils, and tau for diagnosis and monitoring progres-

sion of AD. Standardized uptake value ratio (SUVR) relative to a refer-

ence tissue is commonly used for ATN PET quantification.

Specifically, 18F-FDG SUVR can be used to estimate the metabolic

glucose uptake rate ratio (Y. Wu et al., 2012; Y. G. Wu, 2008), while

the 18F-florbetapir and 18F-flortaucipir SUVRs can be used to approxi-

mate the tracer distribution volume ratio (DVR) of binding to Aβ and

tau, respectively (Wong et al., 2010; Zhou et al., 2021; Zhou, Endres,

Braši�c, Huang, & Wong, 2003; Zhou, Sojkova, Resnick, &

Wong, 2012).

Various reference tissues-based SUVRs have been used in previ-

ous AD studies, leading to different statistical power in PET assess-

ments (Chen et al., 2015; Zhou et al., 2021). The pons reference

region previously demonstrated the best preservation of glucose

metabolism in AD and therefore was deemed as a reliable reference

tissue for brain 18F-FDG PET normalization (Minoshima, Frey, Fos-

ter, & Kuhl, 1995). Reference tissues including whole brain (Nugent

et al., 2020), cerebellum gray matter (GM; Förster et al., 2012;

Ossenkoppele et al., 2012), and pons (Alexander, Chen, Pietrini,

Rapoport, & Reiman, 2002; Ortner et al., 2019; Schmidt et al., 2008)

have been used to calculate 18F-FDG PET SUVR in longitudinal AD

studies. The effect of reference tissues including cerebellum GM,

pons, and whole brain on 18F-FDG PET SUVR have been also evalu-

ated in cross-sectional (Minoshima et al., 1995; Yakushev et al., 2008)

and longitudinal AD studies (Nugent et al., 2020; Verger, Doyen, Cam-

pion, & Guedj, 2021). Similarly, different reference tissues including

cerebellar GM, centrum semiovale, pons, and corpus callosum have

been used to quantify 18F-florbetapir and 11C-PIB amyloid PET

(Blautzik et al., 2017; Chen et al., 2015; Chiao et al., 2019; Heeman

et al., 2020; Shokouhi et al., 2016; Su et al., 2015; Wang et al., 2021;

Xie et al., 2020) and 18F-flortaucipir tau PET (Baker et al., 2017; Cho

et al., 2020; Devous Sr. et al., 2018; Southekal et al., 2018; Zhao, Liu,

Ha, Zhou, & Alzheimer’s Disease Neuroimaging Initiative, 2019). The

amyloid PET SUVRs calculated from different reference tissues were

compared and evaluated by correlation analysis of SUVR versus cog-

nitive assessment (Chen et al., 2015), test–retest analysis (Blautzik

et al., 2017), and effect size for evaluation of the treatment response

(Chiao et al., 2019). Our previous research has demonstrated that spa-

tially constrained kinetic model with dual reference tissues comprising

of cerebellum GM and centrum semiovale significantly improves

quantification of relative perfusion and tau binding (Zhou et al., 2021).

In previous longitudinal 18F-FDG and 18F-florbetapir PET studies, dif-

ferent reference tissues based SUVRs were compared, but the com-

parisons were limited to the region of interest (ROI) levels. Also, these

previous studies focused 18F-FDG PET in normal aging (Nugent

et al., 2020; Verger et al., 2021) or amyloid treatment effects in mild

cognitive impairment (MCI) and AD participants (Chen et al., 2015;

Chiao et al., 2019). Moreover, for tau PET studies, to the best of our

knowledge, there has been no evaluation for multiple reference tis-

sues in longitudinal studies.

The selection of an appropriate reference tissue is reliant on mul-

tivariable factors and imperative aspects such as the studied popula-

tion, study sample size, PET acquisition protocol, and the type of

radiopharmaceutical used. The objective of this study is to improve

statistical power for detecting 18F-FDG, 18F-florbetapir, and 18F-flortaucipir PET SUVR changes in AD by selecting appropriate

reference tissues. Using the AD Neuroimaging Initiative (ADNI), we

performed longitudinal analysis on individuals with disease progres-

sion regardless of their disease stage, whether from cognitively nor-

mal (CN) to AD, MCI to AD, or CN to MCI. The impact of the

reference tissue selection on discriminating between CN and cogni-

tively impaired (CI) were also evaluated. This study is the most com-

prehensive comparative analysis of different reference tissues

measured with multiple radiotracers to monitor the progression of

AD. Our study may improve clinical staging diagnosis through quanti-

tative PET which has important implications for biomarker-guided

precision medicine.


2.1 | Participants

All 18F-FDG, 18F-florbetapir, and 18F-flortaucipir PET and structural

MRI data in this study were obtained from the AD Neuroimaging Ini-

tiative (ADNI) dataset (https://adni.loni.usc.edu). Informed written

consent was obtained from all participants at each site. In total, we

downloaded 794 18F-FDG-PET scans, 906 18F-florbetapir-PET scans,

and 903 18F-flortaucipir-PET scans, which encompass 420, 434, and

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666 participants, respectively. Demographics and clinical assessments

including the Mini-Mental Status Examination (MMSE), Clinical

Dementia Rating (CDR), and clinical diagnostic status of CN, MCI, and

AD participants were also obtained.

2.2 | PET data acquisition and image preprocessing

Raw T1-weighted structural MRI and preprocessed 18F-FDG, 18F-

florbetapir, and 18F-flortaucipir PET images of each subject were

downloaded from the ADNI database. The downloaded PET images

were aligned, averaged, reoriented, and interpolated into a standard

160 � 160 � 96 voxel image grid and smoothed with an 8 mm in full

width at half maximum (FWHM) 3D Gaussian filter by the ADNI con-

sortium with 1.5 mm cubic voxels. Further details of PET and

T1-weighted MR acquisition protocols can be found at http://adni.

loni.usc.edu/methods/pet-analysis-method/pet-analysis/ and http://

adni.loni.usc.edu/methods/documents/mri-protocols/, respectively.

The PET and MRI data were further processed by partial volume

correction (PVC) and spatial normalization, both using Statistical Para-

metric Mapping (SPM12, Wellcome Department of Imaging Neurosci-

ence, Institute of Neurology, London, UK) in the MATLAB R2020b (The

MathWorks Inc., Natick, MA) environment, as reported in our earlier

studies (Paranjpe et al., 2019; Yan et al., 2020, 2021). PVC was per-

formed to minimize the possibility of underestimation in PET images,

especially for small brain regions such as amygdala and striatum. The

reblurred Van Cittert iteration method was applied for PVC in individual

PET images, where a 3D Gaussian Kernel of 8 mm FWHM was used as

the spatial smoothing function with step length α of 1.5 (Tohka &

Reilhac, 2008). All PET images were then coregistered to the individ-

ual’s own structural MRI images, which were normalized to the stan-

dard Montreal Neurologic Institute (MNI) space using an MRI template

(image volume: 121 � 145 � 121, voxel size: 1.5 � 1.5 � 1.5 mm in x,

y, z). The median (interquartile range) of time intervals between PET

and MRI are 28(49) days, 30(48) days, and 51(129.5) days for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir, respectively. The transformation

parameters determined by MRI spatial normalization were then applied

to the coregistered PET images for PET spatial normalization. SUVR

images were calculated relative to the cerebellum GM (SUVRCereb_GM),

centrum semiovale (SUVRCS), and pons (SUVRPons) reference tissues.

The ROI SUVR values were calculated by applying ROIs on the SUVR

images in the standard MNI space for minimizing variance related to

the variability of ROI volume and shape in native space (Gottesman

et al., 2017; Liu et al., 2019; Paranjpe et al., 2019; Tudorascu

et al., 2018; Yan et al., 2021). A total of 18 ROIs including three refer-

ence tissues (cerebellum GM, centrum semiovale, and pons) and an

additional 15 ROIs including the orbital frontal, prefrontal, superior

frontal, medial temporal, inferior temporal, lateral temporal, parietal,

posterior precuneus, anterior cingulate, posterior cingulate, occipital,

entorhinal cortex, amygdala, hippocampus, and parahippocampal gyrus

regions were manually delineated on the MRI template using the

PMOD software program (PMOD 4.002, PMOD Technologies Ltd.,

Zürich, Switzerland) in standard MNI space. These ROI templates were

previously developed in the Johns Hopkins Department of Radiology

and have been validated in our former studies (Liu et al., 2019; Paranjpe

et al., 2019; Yan et al., 2020, 2021; Zhou et al., 2021).

2.3 | Longitudinal SUVR PET analysis for cognitively declined participants

The effects of different reference tissues were evaluated on the sensi-

tivity of SUVR measurements for the detection of cognitively declined

populations. The baseline and last scans were defined as the subject’s first and last scan in the downloaded data. Participants at the last scan

who had an increased CDR score (Morris, 1993) or evidence of clinical

disease progression (CN to MCI, MCI to AD, or CN to AD) were

included in the longitudinal studies. This population inclusion criterion

was consistent across 18F-FDG, 18F-florbetapir, and 18F-flortaucipir

studies. Based on the baseline and last scan SUVR values of each sub-

ject, paired statistical t values were calculated at both ROI- and voxel-

levels for each reference tissue. The annual change rates for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir uptake were further calculated as


Annual Change Rate ACRxð Þ ¼ SUVRlast scan�SUVRbaselineð Þ= SUVRbaseline �Tð Þ, ð1Þ

where x represents the reference tissue (cerebellum GM, centrum

semiovale, or pons), SUVRlast scan and SUVRbaseline are the SUVR values

of the ROI at the last scan and baseline, T is the time interval from

baseline to the last scan in years.

2.4 | Cross-sectional SUVR PET analysis for CN and CI participants

To comprehensively assess the performance of the cerebellum GM,

centrum semiovale, and pons reference tissues in discriminating

between CN or CI individuals, ROI- and voxelwise-based cross-

sectional statistical analyses were performed. All baseline scans

were included for cross-sectional analysis and participants were

classified as either CN (CDR = 0) or CI (CDR ≥ 0.5; Zhou

et al., 2021). To investigate the sensitivity of the PET SUVR mea-

surement in discriminating CN from CI, effects sizes were approxi-

mated using Cohen’s d (Chand et al., 2020; Cohen, 1988; Lopresti

et al., 2005; Sullivan & Feinn, 2012; Zhou et al., 2021) of CN and CI

groups as follows:

d¼ mean SUVRx at group1ð Þ�mean SUVRx at group2ð Þð Þ�SDpooled,

ð2Þ where SDpooled represents the standard deviation of SUVR in pooled

population, x represents either cerebellum GM, centrum semiovale, or

pons reference tissues. Since the 18F-FDG SUVR decreased while the 18F-florbetapir and 18F-flortaucipir SUVR increased with disease pro-

gression, we set group 1 to be CN and group 2 to be CI for 18F-FDG,

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and group1 to be CI and group2 to be CN for 18F-florbetapir and 18F-flortaucipir.

For correcting the influence of covariates, the two-sample inde-

pendent t test adjusted by age, sex, and education levels were per-

formed at voxelwise level using SPM12. For ROI-based analysis, the

generalized linear model was used to assess the group difference in

SUVR for each ROI by adjusting for covariates, the Bonferroni-

corrected p-value < .05 was defined as significant.


3.1 | Study cohort characteristics

Study cohort characteristics for participants in the longitudinal ana-

lyses are summarized in Table 1. A total of 53, 55, and 20 participants

were included with a mean time interval between the baseline and last

scan of 63.42 ± 27.15 months, 57.05 ± 18.75 months, and 19.88

± 8.03 months for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir,

respectively. For each tracer, there were substantial differences

between the baseline and last scan in age, education level, and CDR.

There was no significant difference in the baseline and last scan for

the MMSE of the participants in 18F-flortaucipir. Comparison across

tracers demonstrated that MMSE values at the baseline showed dif-

ferences between 18F-FDG and 18F-flortaucipir groups and between 18F-florbetapir and 18F-flortaucipir groups. Other groups showed no

significant difference in age and CDR in all three tracers.

Study cohort characteristics for participants in the cross-sectional

study are listed in Table 2. There were no significant differences

between CN and CI groups in terms of their age and education level,

but considerable differences were observed for the MMSE and CDR

scores for 18F-FDG and 18F-florbetapir PET studies. Significant differ-

ences were detected in age, education level, MMSE, and CDR scores

in 18F-flortaucipir PET study.

3.2 | Effect of reference tissue selection on sensitivity to detect longitudinal PET SUVR changes in AD

3.2.1 | 18F-FDG PET

The statistic t maps based on the 18F-FDG SUVR images for three ref-

erence tissues are illustrated in Figure 1. It was evident that the t-

values calculated from SUVR images were in the order of t(SUVRPons)

> t(SUVRCereb_GM) > t(SUVRCS) in the frontal, temporal and parietal

regions. ROI-based analysis showed consistent results as demon-

strated in Figure 2. The t(SUVRPons) showed the greatest sensitivity in

the orbital frontal, prefrontal, superior frontal, lateral temporal,

inferior temporal, posterior precuneus, anterior cingulate, posterior

cingulate, caudate, entorhinal cortex, amygdala, hippocampus, and

parahippocampal gyrus, 86.62 ± 47.63% higher than the t(SUVRCS)

and 38.40 ± 29.13% higher than the t(SUVRCereb_GM). In contrast, T A B L E 1

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