Parametric imaging of dual-time window [18F]flutemetamol and [18F]florbetaben studies

Fiona Heeman,Maqsood Yaqub,Janine Hendriks,Ilona Bader,Frederik Barkhof,Juan Domingo Gispert,Bart N.M Van Berckel,Isadora Lopes Alves,Adriaan A Lammertsma

doi:10.1016/j.neuroimage.2021.117953

Abstract

Optimal pharmacokinetic models for quantifying amyloid beta (Aβ) burden using both [18F]flutemetamol and [18F]florbetaben scans have previously been identified at a region of interest (ROI) level. The purpose of this study was to determine optimal quantitative methods for parametric analyses of [18F]flutemetamol and [18F]florbetaben scans. Forty-six participants were scanned on a PET/MR scanner using a dual-time window protocol and either [18F]flutemetamol (N=24) or [18F]florbetaben (N=22). The following parametric approaches were used to derive DVR estimates: reference Logan (RLogan), receptor parametric mapping (RPM), two-step simplified reference tissue model (SRTM2) and multilinear reference tissue models (MRTM0, MRTM1, MRTM2), all with cerebellar grey matter as reference tissue. In addition, a standardized uptake value ratio (SUVR) was calculated for the 90–110 min post injection interval. All parametric images were assessed visually. Regional outcome measures were compared with those from a validated ROI method, i.e. DVR derived using RLogan. Visually, RPM, and SRTM2 performed best across tracers and, in addition to SUVR, provided highest AUC values for differentiating between Aβ-positive vs Aβ-negative scans ([18F]flutemetamol: range AUC=0.96–0.97 [18F]florbetaben: range AUC=0.83–0.85). Outcome parameters of most methods were highly correlated with the reference method (R2≥0.87), while lowest correlation were observed for MRTM2 (R2=0.71–0.80). Furthermore, bias was low (≤5%) and independent of underlying amyloid burden for MRTM0 and MRTM1. The optimal parametric method differed per evaluated aspect; however, the best compromise across aspects was found for MRTM0 followed by SRTM2, for both tracers. SRTM2 is the preferred method for parametric imaging because, in addition to its good performance, it has the advantage of providing a measure of relative perfusion (R1), which is useful for measuring disease progression.

Highlights

For the latter quantitative applications, it has been suggested that dynamic or dual-time window scanning protocols should be used for ob-Early detection of amyloid-beta (Aβ) plaques has become increas- taining the most accurate Aβ estimates (Berckel BNM van et al, 2013; ingly relevant, in particular with respect to identifying individuals Bullich et al, 2018; Heeman et al, 2019) and, since amyloid patholfor secondary prevention trials and monitoring disease progression in ogy might not follow anatomical boundaries, voxel-wise quantitativeAlzheimer’s disease (AD) (Farrar et al, 2017, Collij et al, 2020)
The present study aimed to evaluate the performance of the most widely used parametric methods for the specific case of dual-time window scans using [18F]flutemetamol and [18F]florbetaben
mini-mental state examination (MMSE) scores were higher for Aβ-negative compared with Aβ-positive participants ([18F]flutemetamol: p=0.014, [18F]florbetaben: p=0.061), while there were no differences in age or proportion males and females (Table 1)

Summary

Introduction

For the latter quantitative applications, it has been suggested that dynamic or dual-time window scanning protocols should be used for ob-Early detection of amyloid-beta (Aβ) plaques has become increas- taining the most accurate Aβ estimates (Berckel BNM van et al, 2013; ingly relevant, in particular with respect to identifying individuals Bullich et al, 2018; Heeman et al, 2019) and, since amyloid patholfor secondary prevention trials and monitoring disease progression in ogy might not follow anatomical boundaries, voxel-wise quantitativeAlzheimer’s disease (AD) (Farrar et al, 2017, Collij et al, 2020). Detection of amyloid-beta (Aβ) plaques has become increas- taining the most accurate Aβ estimates (Berckel BNM van et al, 2013; ingly relevant, in particular with respect to identifying individuals Bullich et al, 2018; Heeman et al, 2019) and, since amyloid patholfor secondary prevention trials and monitoring disease progression in ogy might not follow anatomical boundaries, voxel-wise quantitative. Quantitative phy (PET) scans, using either a dichotomous classification of Aβ burden parametric images allow for voxel-by-voxel analysis between groups or (Mallik et al, 2017) or a more fine-grained quantitative measure of Aβ, as a function of time within the same subjects. Comprehensive which is required for measuring the extent of pathology or quantita- evaluation of parametric methods used for voxel-wise analysis is wartively tracking disease progression (Lammertsma, 2017).

Objectives

Methods

Results

Conclusion