AbstractBackgroundThis study aims to quantify amyloid and tau buildup in older individuals with kinetic models that leverage dynamics in aorta IDIFs and the brain utilizing [18F]‐Florbetaben and [18F]‐PI‐2620 total‐body EXPLORER PET.MethodFifteen adults (9 CU, 2 MCI, and 3 AD) aged 66‐86 underwent dynamic total‐body 18F‐florbetaben [110min] and PI‐2620 PET [90min] (United Imaging). 3 CU adults also underwent Tau PET for this initial analysis. IDIFs were derived from descending aorta ROIs. PET volumes were motion corrected (FSL‐MCFLIRT) and linearly registered (FSL‐FLIRT) to T1W image. The DKT ATLAS was used to segment brain cortical regions that are involved in neurodegeneration for PET SUVR measurements. PET SUVR means were calculated from 10 index regions and the cerebellar gray matter as the reference. Dynamic time activity curves were fit to the two‐tissue compartment model (2TCM) using joint estimation and population metabolite‐corrected IDIFs (Li, JNM 2021). The Multi‐linear Reference Tissue Model (MRTM) to calculate distribution volume ratio (DVR) with reference to cerebellar gray (Ichise, JCBFM 2003).Result[18F]‐Florbetaben Aβ+/Tau‐ SUVR was the highest in each brain index region, while PI‐2620 showed elevated SUVR in brain regions associated with early Braak stages I‐III in both Aβ‐/Tau‐ and Aβ+/Tau‐ (Fig 2). In our tau‐negative cohort, PI‐2620 signal in both cortical and reference regions, as expected, had low specific distribution volume (Vs) values (1.43 ±0.56) in both HC and AD. [18F]‐Florbetaben cortical Vs values were roughly 4 times higher than cerebellum in the Aβ+/Tau‐ (AD) individual and PI‐2620 cortical Vs values were 3.5 times higher compared to the cerebellum in both HC and AD. Mean k₁ values, which reflect regional blood flow, were similar in both AD and HC in both [18F]‐florbetaben and PI‐2620 PET analyses (Table 1). SUVR and DVR from MRTM kinetic models were correlated for each tracer; with slight overestimation of SUVR compared to DVR.ConclusionAbsolute quantification of amyloid and tau binding from total‐body PET data is feasible using aorta IDIFs and enables high quality kinetic modeling for accurate measures in clinical research of aging and dementia.