Comparison of delay correction methods to estimate ¹⁸F‐florbetaben kinetics in the brain using an image derived input function on total‐body EXPLORER PET

Background

Absolute quantification of β‐amyloid binding from ¹⁸F‐florbetaben PET requires compartment kinetic modeling and an input function. This study uses non‐invasive Image Derived Input functions (IDIFs), derived using a new total‐body EXPLORER PET/CT scanner (Spencer et al., 2021). For accurate kinetic modelling, it is imperative to correct for time delay from the IDIF location to tracer arrival to the tissue of interest. The standard approach of Joint Estimation (JE) fits for delay time in tandem with other kinetic parameters and is computationally extensive (Feng et al., 2021). This study investigates the utility of Leading Edge (LE) ‐a novel and computationally efficient pulse timing method to estimate delay (Li et al., 2021) compared to JE in estimating ¹⁸F‐florbetaben kinetics in the brain grey matter regions, known to be vulnerable in Alzheimer’s Disease (AD).

Method

The study cohort included 14 individuals (9 cognitively unimpaired, 2 MCI, and 3AD; age = 66‐86 years) of the UC Davis Alzheimer’s Disease Research Center. Dynamic total‐body ¹⁸F‐florbetaben PET images were acquired for 110min using the uEXPLORER, and reconstructed (resolution = 2.344 mm isotropic voxels, framing protocol:30×2s, 12×10s, 7×60s, 20×300s).

The dynamic brain‐cropped images were motion corrected (Jenkinson et al., 2002), and linearly registered to individual subject’s T1W (Jenkinson & Smith, 2001). Individual T1W were segmented using DKT atlas to obtain cerebral grey matter ROIs (Desikan et al., 2006). Dynamic time activity curves for each ROI were fit to the two‐tissue compartmental model, using a subject‐specific IDIF (population metabolite‐corrected) derived from the descending aorta (Figure1). Kinetic parameters were estimated and compared between JE and LE methods.

Result

Strong associations were observed between JE and LE estimated ¹⁸F‐florbetaben kinetic parameters, including vb(r = 0.84, p<0.001), K₁(r = 1, p<0.001), k₂(r = 0.9, p<0.001), k₃(r = 0.93, p<0.001), and k₄(r = 0.86, p<0.001), in all the brain regions (Figure2). Quantitative kinetic results averaged across the selected grey matter regions (N = 11) are reported in Figure3.

Conclusion

This study demonstrated the utility of IDIF extracted from total‐body dynamic PET to estimate ¹⁸F‐florbetaben tracer kinetics in the brain grey matter regions. Furthermore, the results show that LE can be an efficient surrogate for JE based approach in grey matter focused PET kinetic modelling of ¹⁸F‐florbetaben.