Twenty cognitively normal subjects, 14 female and 6 male (69 ± 8 years old), underwent 120 min dynamic [18F]MK-6240 coffee-break PET protocol and T1-MRI for this study on two separate days up to 60 days apart. The study has been approved by the institutional review board of our institution, and all subjects signed an informed consent form.

Subjects underwent dynamic coffee-break PET protocol study on a Siemens Healthineers Biograph™ mCT64 PET/CT scanner post intravenous bolus injection of 217 ± 66 MBq of [18F]MK-6240. No fasting or diet restrictions were required before the study. Dynamic PET of the brain started simultaneously with the radiotracer injection and continued for 60 min using the following time-binning sequence (12 × 10s, 2 × 60s, 1 × 120s, 1 × 240s, 10 × 300s) on the first scan. On the second scan 6 × 300s time-binning sequence was used, from 90 min p.i. to 120 min.

PET emission data were first corrected for attenuation using a low-dose CT (120 kVp; 45 mAs), scatter, and random events, and then reconstructed into a 400 × 400 × 109 matrix (voxel dimensions, 1.08 × 1.08 × 2.03 mm3) using 3D OSEM algorithm provided by the manufacturer (4 iterations and 21 subsets, and 4.0 mm cutoff frequency smoothing filter) with time-of-flight.

MRI were acquired on a Siemens Healthcare TIM TRIO 3.0T scanner. T1 magnetization-prepared rapid gradient-echo (T1-MPRAGE) acquisition protocol was used to provide optimal gray matter (GM)/white matter contrast using a 64-channel head coil, with TR = 2400 ms, TE = 16 ms, TI = 900s, FOV = 256 mm, and 0.5 mm isotropic voxels. T1-weighted images have a 512 × 512 × 416 pixels (256 × 256 × 208 mm) matrix, 16- bits per pixel, 2 pixels/mm resolution, 0.5 × 0.5 × 0.5 mm voxel size.

Inter-frame PET motion correction was performed by coregistering each of the frames to the average of frames 16 and 17 (reference), using Normalized Mutual Information in PMOD® v3.9 (Fuse-It tool).

The Brainstem, Frontal lobe, Temporal lobe, Parietal lobe, Occipital lobe, Insula, Parahippocampi, Fusiform, Cingulate, Precuneus, White Matter (WM), Cortical Gray Matter (GM), Cerebellar GM, Thalamus, Caudate, Putamen, Pallidum, Hippocampus, and Amygdala VOIs were segmented using Freesurfer (http://surfer.nmr.mgh.harvard.edu/) [10] and then overlaid on the PET dynamic images.

PET-to- T1-weighted MRI image registration was done using the same PMOD® image registration tool above.

The [18F]MK-6240 VT and DVR of each of the aforementioned segmented brain structures were measured using a two-tissue compartment model (VT2TCM and DVR2TCM) as well as Logan model (DVRLogan) [11] with the cerebellar GM as reference tissue [1, 2]. 2TCM quantification was carried out using an image-derived input function (IDIF) with the method described by Kang et al., 2018 [12]. Specifically, the lumen was first segmented using a 4 mm diameter ROI in 4 consecutive slices over the C2 portion of the carotid arteries (bilateral) and in the third PET time frame (i.e. 20–30 s p.i.). The lumen VOI was then dilated within each of the 4 slices by 2 PET voxels (i.e. ~2.2 mm), and the resulting hollow disk region was used to correct for spill-over to adjacent epithelial tissue. The epithelial VOI was then again dilated by 4 voxels (i.e. ~4.8 mm), resulting in a background VOI. Partial volume correction (PVC) to the IDIF was finally carried out using a regional voxel-based PVC tool from PMOD and a 1.1 × 1.1 × 0.9 mm³ point spread function (estimated in an ongoing study). Whole blood IDIF was fitted with 3-exponential function and then metabolite correction was performed using population-based data previously reported by Guehl et al. 2019 [2].

Dynamic scans times ranging from 60 min to 30 min in 5 min intervals were investigated to identify the shortest acquisition time-window (SAT) that would reproduce the [18F]MK-6240 VT2TCM using a 120 min dynamic scan (gold standard). The SAT was defined as the shortest dynamic time that would result in an error in VT2TCM, compared to that measured from the 120 min dynamic dataset, that is within its limits of reproducibility (LOR) for all the segmented brain regions. 2TCM- and Logan reference tissue - based models, as well as the SUVR were also investigated.

The reproducibility of [18F]MK-6240 kinetic parameters, specifically VT2TCM and DVR2TCM and DVRLogan respectively, deduced from the 120 min dynamic image set (gold standard), were then assessed using the aforementioned SAT that reproduced the VT2TCM results, as described above.

SUVR was also measured using 90–120 min post-injection after 3-exponential extrapolating the SAT dynamic dataset to 120 min (SUVR90 − 120−Extrap). Results were finally compared to those from the original 120 min dynamic dataset (SUVR90 − 120 min). The cerebellar grey matter was used as a reference region in all cases [1, 2].

Statistical analysis was performed using a hierarchical approach. First, the correlations between deduced from the SAT VT2TCM, DVR2TCM and DVRLogan, and those measured from the 120 min were respectively calculated using the Spearman correlation coefficient (R). Non-parametric Passing–Bablok regression analysis [13] was performed to test for systematic bias (95% confidence interval [CI] for intercept (α) does not include 0) and proportional (95% CI for slope (β) does not include 1) between the sets of parameters that exhibited a correlation R > 0.5; Passing-Bablock analysis should not be used in the case of weak correlations [13]. Random differences between the SAT and 120 min dynamic scans were measured using residual standard deviation. If the slope and intercept were not significantly different from 1 to 0, respectively, Bland–Altman analysis [14] was performed to calculate the 95% LOR, after testing for the normality assumption on the differences between the two sets of kinetic rate constants using the Kolmogorov–Smirnov test. All statistical analyses were performed using, using MedCalc Statistical Software version 20.217 (MedCalc Software bv, Ostend, Belgium; https://www.medcalc.org; 2020).