Non‐invasive quantification and SUVR validation of [18F]‐florbetaben with total‐body EXPLORER PET. (20th December 2022)

• Record Type: Journal Article
• Authors: Holy, Emily Nicole; Fan, Audrey P.; Alfaro, Evelyn Rebekha; Fletcher, Evan; Spencer, Benjamin A.; Cherry, Simon R.; DeCarli, Charles S.
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.066123
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Kinetic modeling of brain amyloid deposition with [18F]‐florbetaben more accurately quantifies the binding density to amyloid plaques compared to standardized uptake ratios (SUVR) (Becker, JNM2013). The total‐body EXPLORER PET scanner with an axial field of view of 194cm supports the acquisition of a full human body in one scan and permits noninvasive Image‐Derived Input Functions (IDIFs) as an alternative to arterial blood sampling (Badawi, JNM 2019). Our aim is to quantify amyloid buildup with kinetic models that leverage dynamics in aorta IDIFs and the brain utilizing [18F]‐Florbetaben and validate with SUVR in an elderly cohort. Method: Twelve adults aged 66‐86 underwent dynamic total‐body 18F‐florbetaben PET for 110min, spatial resolution = 2.344mm, average tracer dose = 299.70 MBq. 8 cognitively‐normal, 3 Mild Cognitive Impairment (MCI), and 1 Alzheimer's disease (AD). Regions of interests were drawn in the middle descending aorta and eroded to exclude the vessel walls to derive IDIFs (Fig.1). Next, regional brain analysis including PET image matching to previously acquired T1‐weighted MRI, normalization, and segmentation were performed in PMOD (v4.2) using the AAL atlas. PET SUVR (90‐110min) means were calculated from 7 index regions (lateral frontal, medial frontal, anterior cingulate, posterior cingulate, lateral temporal, lateral parietal, and precuneus) and the cerebellar gray matter. Dynamic time activity curves (TACs) from the same brain regions were fit to the two‐tissue compartment model (2TCM) using population metabolite‐corrected IDIFs; and the Multi‐linear Reference Tissue Model (MRTM) to calculate distribution volume ratio (DVR) with reference to cerebellar gray (Ichise, JCBFM 2003). Result: Amyloid‐positive patients showed the highest SUVR in brain index regions (Fig.2). High quality dynamic kinetic modeling was achieved (Fig.3), with high SUVR accumulation in index regions compared to cerebellum at later time points in amyloid‐positive cases. SUVR and DVR from kinetic models were strongly correlated; with slight overestimation of SUVR compared to DVR (Fig.4). DVR values from the MRTM were lower than (86.7% of) DVR quantified by 2TCM. Conclusions: Absolute quantification of amyloid binding from total‐body [18F]‐florbetaben PET data is feasible using aorta IDIFs with high agreement between dynamic binding parameters compared to SUVR in discriminating positive and negative scans.