Elsevier

NeuroImage

Volume 108, March 2015, Pages 450-459
NeuroImage

Improved longitudinal [18F]-AV45 amyloid PET by white matter reference and VOI-based partial volume effect correction

https://doi.org/10.1016/j.neuroimage.2014.11.055Get rights and content

Highlights

  • Different reference regions were compared for [18F]-AV45 PET quantification.

  • Effects of segmentation and partial volume effect correction (PVEC) were assessed.

  • White matter (WM) SUV was most stable between diagnosis groups.

  • WM and brainstem reference improved discriminatory power compared to cerebellum.

  • WM reference including PVEC distinctly improved longitudinal PET assessment.

Abstract

Amyloid positron-emission-tomography (PET) offers an important research and diagnostic tool for investigating Alzheimer's disease (AD). The majority of amyloid PET studies have used the cerebellum as a reference region, and clinical studies have not accounted for atrophy-based partial volume effects (PVE). Longitudinal studies using cerebellum as reference tissue have revealed only small mean increases and high inter-subject variability in amyloid binding. We aimed to test the effects of different reference regions and PVE-correction (PVEC) on the discriminatory power and longitudinal performance of amyloid PET.

We analyzed [18F]-AV45 PET and T1-weighted MRI data of 962 subjects at baseline and two-year follow-up data of 258 subjects. Cortical composite volume-of-interest (VOI) values (COMP) for tracer uptake were generated using either full brain atlas VOIs, gray matter segmented VOIs or gray matter segmented VOIs after VOI-based PVEC. Standard-uptake-value ratios (SUVR) were calculated by scaling the COMP values to uptake in cerebellum (SUVRCBL), brainstem (SUVRBST) or white matter (SUVRWM). Mean SUV, SUVR, and changes after PVEC were compared at baseline between diagnostic groups of healthy controls (HC; N = 316), mild cognitive impairment (MCI; N = 483) and AD (N = 163). Receiver operating characteristics (ROC) were calculated for the discriminations between HC, MCI and AD, and expressed as area under the curve (AUC). Finally, the longitudinal [18F]-AV45-PET data were used to analyze the impact of quantitation procedures on apparent changes in amyloid load over time.

Reference region SUV was most constant between diagnosis groups for the white matter. PVEC led to decreases of COMP-SUV in HC (− 18%) and MCI (− 10%), but increases in AD (+ 7%). Highest AUCs were found when using PVEC with white matter scaling for the contrast between HC/AD (0.907) or with brainstem scaling for the contrast between HC/MCI (0.658). Longitudinal increases were greatest in all diagnosis groups with application of PVEC, and inter-subject variability was lowest for the white matter reference.

Thus, discriminatory power of [18F]-AV45-PET was improved by use of a VOI-based PVEC and white matter or brainstem rather than cerebellum reference region. Detection of longitudinal amyloid increases was optimized with PVEC and white matter reference tissue.

Introduction

Alzheimer's disease (AD) is the most common form of dementia; its incidence increases exponentially as a function of age, which is imposing an onerous burden on health care systems in societies with aging populations (Ziegler-Graham et al., 2008). Neurofibrillary tangles and amyloid plaques together comprise the hallmark neuropathology of AD (Braak and Braak, 1991). Elevated brain amyloid burden is now clearly associated with cognitive decline in the healthy elderly (HC) (Lim et al., 2012) and in cases of mild cognitive impairment (MCI) (Lim et al., 2014). Amyloid PET offers a feasible tool for the early detection of brain amyloidosis, and the recent development of fluorine-18 labeled amyloid radioligands such as [18F]-AV45 has made this technique available to PET centers lacking an on-site cyclotron/radiochemistry facility.

In clinical PET practice, Aβ-positivity and -negativity are visually assessed with good inter- and intra-reader agreement (Clark et al., 2012). However, a semiquantitative approach is better suited especially to the requirements of longitudinal clinical trials of amyloidosis progression and treatment. The issue of defining an optimal reference region has been extensively discussed for normalization of [18F]-fluorodeoxyglucose-(FDG) PET relative to cerebellum, pons/brainstem, global mean, or a reference cluster (Bohnen et al., 2012, Borghammer et al., 2009, Dukart et al., 2013, Yakushev et al., 2008).

In PET imaging with [18F]-AV45- and [11C]-PiB, the entire cerebellum and the cerebellar gray matter (GM) have emerged as the most widely used reference regions for quantitation of amyloid burden (Weiner et al., 2013). However, a recent longitudinal [11C]-PiB PET study of mild cognitive impairment (MCI) and AD showed high inter-subject variability based on a cerebellar GM reference (van Berckel et al., 2013). Furthermore, amyloid PET results are potentially biased by partial volume effects (PVE), which have a considerable impact in patients with pronounced atrophy (Thomas et al., 2011), which is particularly problematic in longitudinal studies.

Given these considerations, we aimed to compare systematically the quantitation of [18F]-AV45-PET results for different reference regions, using as our material the Alzheimer's Disease Neuroimaging Initiative (ADNI)-dataset, which includes more than 1000 amyloid PET cases. Furthermore, we set about to investigate the impact of a volume-of-interest (VOI)-based partial volume effect correction (PVEC) on the semiquantitative analyses. Receiver operating characteristics (ROC) were obtained for the baseline discrimination of HC from MCI and AD cases in order to identify the most sensitive amyloid-PET analysis. Finally, two-year longitudinal [18F]-AV45-PET data from 258 patients used to test the impact of the above factors on apparent changes in amyloid load with time.

Section snippets

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 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public–private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI),

Demographics

See Table 1 for details of the study population. At baseline, 108/316 (32%) HC subjects, 267/483 (55%) MCI subjects and 143/163 (88%) AD subjects had a positive [18F]-AV45 scan (SUVR > 1.10). From those subjects with an additional 2-year follow-up scan, 26/88 (30%) HC subjects, 66/148 (45%) MCI subjects and 16/22 (73%) AD subjects had a positive [18F]-AV45 baseline scan. Nine converters to MCI and one converter to AD were observed in HC subjects. Five re-converters to HC and 12 converters to AD

Discussion

We present a systematic investigation of the discriminatory performance of different reference regions for [18F]-AV45-PET quantitation, making use of the hitherto largest dataset of PET examinations in AD patients, along with MCI and HC groups. In addition, we tested effects of additionally performed MRI-based segmentation and VOI-based PVEC in our nearly 1000 cases. ROC analyses revealed that the discriminatory power between MCI or AD and the healthy control population was increased by using

Conclusion

We aimed to optimize the discriminative fitness of [18F]-AV45-PET by methodological considerations of cortical atrophy/spill-over and comparison of reference regions. We found clear benefits from using VOI-based PVEC and defining the white matter or the brainstem rather than cerebellum as the reference region; both procedures increased the discriminatory power between MCI and HC, which are key target groups in therapy trials. Furthermore, longitudinal increases in amyloidosis during follow-up

Acknowledgments

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec

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    Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (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 writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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