Improved SUVR calculation for [18F]‐AV45 amyloid pet imaging using a novel reference region approach

Abstract

AbstractBackgroundUtilization of a data‐driven approach to find a more sensitive reference region (RR) for [18F]‐AV45 PET imaging that differentiates the spectrum of Alzheimer’s disease (AD) and improves especially longitudinal study designs.MethodData of 283 participants (135 amyloid‐negative cognitively normal (CN), and 148 amyloid‐positive AD) from the ADNI database (http://adni.loni.usc.edu/) was used. All [18F]‐AV45 scans were co‐registered, normalized, and skull‐stripped. The dataset was split into a training‐and test‐dataset. Voxel‐wise group comparisons were performed in the training‐set (75 CNs, 77 ADs) to identify a RR that is void off on‐target tracer uptake. Potential clusters were used to extract mean global SUVRs in the test‐dataset (60 CNs, 70 ADs). Effect sizes between novel and commonly used RRs were compared. Baseline and follow‐up data of 19 CNs, 36 participants with mild cognitive impairment, and 26 ADs was used to test whether the newly identified RR is more sensitive to assess longitudinal change than common RRs. Effect sizes of change in SUVR between baseline and follow‐up were used as metric of sensitivity.ResultThe training dataset yielded two novel RRs in the brainstem and the cerebellar white matter. These new RRs showed higher effect sizes compared with commonly used RRs. However, no significant differences in the effect sizes were observed when examining longitudinal change in SUVR compared with commonly used RRs.ConclusionThe data‐driven approach did not improve SUVR measurements for [18]‐AV45 imaging. However, our analyses proved that the commonly used RRs are also longitudinally stable and sensitive to differentiate clinical from non‐clinical groups.