Validation Of A Spatial Normalization Method Using A Principal Component Derived Adaptive Template For [F-18]Florbetaben Pet

Quantification may help in the context of amyloid-beta positron emission tomography (PET). Quantification typically requires that PET images be spatially normalized, a process that can be subject to bias. We herein aimed to test whether a principal component approach (PCA) previously applied to [F-18]flutemetamol PET extends to [F-18] florbetaben. PCA was applied to [F-18]florbetaben PET data for 132 subjects (70 Alzheimer dementia, 62 controls) and used to generate an adaptive synthetic template. Spatial normalization of [F-18]florbetaben data using this approach was compared to that achieved using SPM12's magnetic resonance (MR) imaging driven algorithm. The two registration methods showed high agreement and minimal difference in standardized uptake value ratios (SUVR) (R-2 = 0.997 using cerebellum as reference region and 0.996 using the pons). Our method allows for robust and accurate registration of [F-18]florbetaben images to template space, without the need for an MR image, and may prove of value in clinical and research settings.