Centiloid harmonisation across PET cameras without using paired scan

Abstract Background The Centiloid (CL) scale allows quantification of amyloid‐b accumulation across multiple PET tracers for the diagnosis of Alzheimer’s disease. The CL quantification on different PET cameras may result in significant quantification bias when merging cohorts acquired from multiple cameras or in the longitudinal studies when change of camera occurs. In this study, we propose a harmonisation method, based on the non‐negative matrix factorisation (NMF), to reduce the quantification bias between different cameras without using paired scans. Method Three hundred and forty‐eight 18F‐NAV4694 scans from Australian Imaging Biomarkers and Lifestyle study acquired on three PET cameras (N = 116 each), Siemens Biograph mCT, Philips Allegro and Philips Gemini TF64 were used to train the model. Participants who switched camera between two consecutive timepoints were selected as the validation data. Seventeen participants imaged on the Biograph mCT and Gemini (2.63±0.51 years scan interval), and fifty‐two participants imaged on the Gemini and Allegro (2.73±0.73 years). One hundred participants who have two scans (2.26±0.82 years) on Gemini were used to define a reference rate of change (CL/year). Figure 1 illustrates the harmonisation workflow. Result Figure 2 compares the curves of CL change rate against baseline CL for mCT to Gemini compared to the reference. The harmonised fitting is closer to the reference than the one without harmonisation in the low to middle CL range. The fitting in high CL range (CL>80) deviates from the reference probably because of the lack of subjects in this range. Figure 3 compares the results between Phillips Allegro and Gemini TF 64. The harmonised fitting in Figure 3 is nearly parallel to the reference for the entire CL range. In contrast, the fitting on the unharmonized data shows negative rate of change for the very negative subjects and much higher CL change rate for the very positive subjects, which are strongly different compared to the reference. Conclusion We propose a practical and economical CL harmonisation for different PET cameras without using paired data. The NMF based method shows promising results for scanner harmonisation, though more validation are needed in future for different PET models.