IntroductionA standardised method for quantifying β-amyloid PET tracers would allow comparison across different tracers and different sites. The development of the Centiloid scale has aimed to achieve this, applying a common scale to better aid the diagnosis and prognosis of Alzheimer’s disease (AD) and to monitor anti-amyloid therapeutic interventions. Here, we apply the Centiloid method to [18F]flutemetamol and [11C]PiB (PiB, Pittsburgh compound B) PET images and derive the scaling factor to express their binding in Centiloids.MethodsPaired PiB and [18F]flutemetamol scans for 74 subjects, including 24 young healthy controls (37 ± 5 years), were analysed using the standard Centiloid method. The same subjects were also analysed using PMOD- and FSL-based pipelines as well as SPM8. Test-retest analysis of 10 AD subjects was also performed with each pipeline.ResultsThe standard uptake value ratios (SUVR), determined using the standard SPM8 Centiloid process, showed a strong correlation between [18F]flutemetamol (Flute) and PiB binding (SUVR-Flute = 0.77 × SUVR-PiB + 0.22, R2 = 0.96). Application of the standard Centiloid process allowed the calculation of a direct conversion equation for SUVR-Flute to Centiloid units (CL) (CL = (121.42*SUVR-Flute) − 121.16). Analysis of the data via the two alternate Centiloid pipelines allowed us to derive standardised, SPM8-equivalent equations for both PMOD (CL = (115.24*SUVR-Flute) − 107.86) and FSL (CL = (120.32*SUVR-Flute) − 112.75) respectively. Test-retest analysis of 10 AD subjects showed an approximate 2% difference for each pipeline.Conclusions[18F]flutemetamol data can now be expressed in Centiloid units, enhancing its utility in clinical and research applications for β-amyloid imaging. The standard Centiloid method also demonstrates that [18F]flutemetamol has favourable performance compared with PiB and other β-amyloid tracers. Test-retest difference averaged 2%, with no difference between image processing pipelines. Centiloid scaling is robust and can be implemented on a number of platforms.

Background Monitoring changes in amyloid load requires repeatable image quantification methods. Several different brain‐image analysis pipelines are used, including SPM8, PMOD, and FSL [1]. Test‐retest (TRT) repeatability is an important parameter for assessing how useful a given pipeline is for longitudinal within‐subject studies, providing a measure of the pipeline’s likely variability and helping to determine the optimal approach to quantitation [1]. We report the results of a test‐retest study that compared pipelines from relevant software packages Method Six pipelines were evaluated: our previously‐published FSL pipeline (FSL1) [1] and a revised version (FSL2), SPM8, PMOD, and two PET‐only pipelines (CortexID** Windows, PMODPET). FSL2 differs from FSL1 by additional heuristic checks that help identify failed registrations, which are then automatically re‐attempted using adjusted registration parameters. The heuristic checks in FSL2 were manually tuned through qualitative evaluation on a large and heterogenous PET‐MR database comprising 812 Alzheimer’s (AD) and non‐AD subjects, for which FSL2 had a final successful registration rate of 98%.For the TRT analyses, 10 subjects [1] with confirmed AD were evaluated. Each subject received an initial (test) [18F]flutemetamol scan, followed by a retest scan within 2 weeks. For each pipeline, TRT variability was calculated for individual subjects and reported as a group mean and standard deviation (SD) (thereby equally weighting each observation): TRT variability of cortical uptake (%) = |retest – test|/0.5*(retest + test). Results were reported on three different scales: SUVRcereb, SUVRcereb‐1, and centiloids (CL). Result Figure 1 shows TRT variability for each pipeline. Using CL measures for example, FSL2 exhibited the lowest TRT variability (mean ± SD) (2.6 ± 1.9%), followed by FSL1 (3.0 ± 2.7%), SPM8 (3.1 ± 2.7%), PMOD (3.3 ± 1.6%), and the PET‐only pipelines (CortexID: 5.8 ± 4.7%, PMODPET: 6.8 ± 6.4%). Note that FSL2 and PMOD had relatively low dispersion of TRT variability, whereas the other pipelines exhibited a spread that was at least at least 1.4 times larger. Conclusion Our FSL‐based pipeline exhibited higher repeatability than other platforms, thus it may be better‐suited for quantifying small longitudinal changes in cortical amyloid load. [1] Battle et al. EJNMMI Res. 2018;8. **CortexID and its PET‐only adaptive template was used as the core for AmyPype, which outputs centiloid quantification.

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Background: Visual assessment of [ 18 F]flutemetamol amyloid PET images, performed regionally (frontal, lateral temporal, posterior cingulate/precuneus, temporo-parietal, striatum), is recommended, with a positive (abnormal) classification if any one of these regions is clearly positive. Changes in the striatum, known to contain extensive fibrillar amyloid deposits in AD, could identify likely converters in Subjective Cognitive Decline (SDC) or Mild Cognitive Impairment (MCI) subjects. CSF biomarkers are proven to detect abnormal amyloid metabolism in early stages of AD, with Aβ42/40 more reliable than Aβ42 alone. We applied a novel regional Centiloid Atlas, based on standard Centiloid (CL) cortical VOIs with additional striatal , aiming to better stratify amyloid abnormalities. Correlation between CL and Aβ42/40 was also investigated.