Study designPart I of the study

In the first part of the study, we performed arterial blood sampling in 20 subjects and validated the resulting AIF against four different IDIF generated by manual and automated extraction of the PET signal from the carotid artery (Fig. 1). As a result, the IDIF with the closest match to the AIF was determined.

Fig. 1

Obtaining the input functions by continuous sampling of whole blood from the radial artery (left) and by manual (middle) and automated (right) extraction of the PET signal from the carotid artery. Calculation and comparison of [18F]PI-2620 quantification parameters such as VT, VT ratio, DV ratio and SUV ratio values using AIF and IDIF

Part II of the study

In the second part of the study, we performed IDIF quantification in 40 subjects using the IDIF protocol which showed the highest correlation with AIF. The subjects belonged to three different groups, namely healthy controls, PSP patients and AD patients. The obtained quantification parameters were used to evaluate whether they were suitable to differentiate between the groups.

SubjectsPart I of the study

Arterial blood sampling was performed as part of an ongoing study protocol in patients with PSP and healthy controls (EudraCT-Nr.: 2021–000201-24, ethics committee of the LMU Munich: approval ID 21–0170) and in an observational study in multiple diseases (DRKS00016920, ethics committee of the LMU Munich: approval IDs 17–569 and 19–022). Per 12/2023 eight healthy controls, ten patients with PSP and two disease controls (one patient with Parkinson disease (PD) and one patient with frontotemporal dementia (FTD)) were included (Table 1).

Table 1 Subject characteristicsPart II of the study

In the second part of the study, healthy controls, patients with PSP, and patients with AD were randomly selected from the ongoing observational study. 15 healthy controls were included together with 15 patients with probable or possible PSP according to current diagnostic criteria [9] and 10 patients with biologically defined typical AD (A + T + N +) [10] (Table 1). The ATN criteria (concerning the pathological processes ß amyloid deposition (A), pathologic tau (T) and neurodegeneration (N)) were defined on PET images in a clinical routine setting via visual inspection of late phase images (90–110 min post-injection). Amyloid rating was additionally supported by semi-quantitative analysis using HERMES Gold software (Hermes Medical Solutions AB, Stockholm, Sweden), but we assured that no borderline cases were included. Thus, all A + cases were positive based on a visual amyloid PET read (tracer: [18F]Flutemetamol (FMM) or [18F]Florbetaben (FBB), median administered activity: 182 ± 11 MBq, same PET scanner as used for tau-PET). Visual rating of tau-positivity was performed with adaption of the FDA approach for [18F]Flortaucipir to [18F]PI-2620. To this end, [18F]PI-2620 images (30–60 min) [1] were scaled by the cerebellum and visually inspected by trained readers. The N status was examined based on the early phase of amyloid [11,12,13,14] using Minoshima projections as commonly used for [18F]FDG PET. Furthermore, all images were in parallel inspected via the brain tool of HERMES Brass software (Hermes Medical Solutions AB, Stockholm, Sweden). Z-score deviation of more than two in AD-typical regions and an AD-like pattern were applied as semiquantitative visually guided criteria.

PET imaging

[18F]PI-2620 was synthesized as previously described [4]. The administered activity ranged between 156 and 223 MBq (median administered activity: 189 MBq), applied as a slow (10 s) intravenous bolus injection.

PET imaging was performed in a full dynamic setting (scan duration: 0–60 min post-injection) using a Siemens Biograph True point 64 PET/CT (Siemens, Erlangen, Germany) or a Siemens mCT (Siemens, Erlangen, Germany). The dynamic brain PET data were acquired in list-mode over 60 minutes and reconstructed into 35 time frames (12 × 5 s, 6 × 10 s, 3 × 20 s, 7 × 60 s, 4 × 300 s and 3 × 600 s) using a 336 × 336 × 109 matrix (voxel size: 1.02 × 1.02 × 2.03 mm3) and the built-in 3-dimensional ordered subset expectation maximization (OSEM) algorithm with 4 iterations, 21 subsets and a 5 mm full-width-at-half-maximum Gaussian filter on the Siemens Biograph and with 5 iterations, 24 subsets and a 5 mm full-width-at-half-maximum Gaussian filter on the Siemens mCT. A CT served for attenuation correction (tube voltage: 120 kV, tube current: 33 mA, pitch: 1.5, rotation time: 0.5 s). As scatter correction, single scatter simulation was used.

Input functionPart I of the study Arterial input function

AIF were obtained by continuous sampling of whole blood from the radial artery using the Swisstrace Blood Sampling System (Swisstrace, Menzingen, Switzerland) (Fig. 1). The blood flow was controlled by a peristaltic pump (0–5 min post-injection: 300 ml/min, 6–20 min post-injection: 150 ml/min, 21–60 min post-injection: 20 ml/min). The measured activity concentration was decay corrected. The cross-calibration of the external detector of the blood sampling system, the dose calibrator and the PET scanner was routinely checked.

Image derived input function

IDIF were generated by manual and automated extraction of the PET signal from the carotid artery over the 60-minute dynamic PET scan. There was an initial quality control for all PET images, also with regard to motion. PET images which showed too much motion (> 10 mm) were excluded [15]. This was the case for two subjects. The included PET images showed an average movement in x, y and z direction of 0.24 mm, 0.35 mm, 1.61 mm.

For manual extraction, the blood activity concentration in the bilateral carotid artery was detected in early frames of the dynamic PET images (usually frame 1 to 7), and spheres with a diameter of 5.0 mm were placed as volumes of interest (VOI) in the pars cervicalis of the internal carotid artery prior to entering the pars petrosal using PMOD version 4.2 (PMOD Technologies, Zürich, Switzerland) (Fig. 1). The activity concentration over time was calculated with the average and the five highest (max5) voxel intensity values (similar approach see [16]) of the VOI.

For automated extraction of carotid artery SUV time series, dynamic PET images were first motion corrected using the implemented motion correction tool of PMOD (i.e. rigid alignment of subsequent frames) and averaged. The resulting mean PET image was then warped to Montreal Neurology Institute (MNI) space via the 30–60 minutes summation image, using a custom in-house [18F]PI-2620 MNI template obtained by the PNEURO pipeline [17], via a high dimensional non-linear warping algorithm implemented in the Advanced Normalization Tools Software (ANTs) package.

Independent component analysis (ICA) with a pre-defined 10 component solution was applied to the native space dynamic PET image to parcellate the image into variance components that represent maps of temporally correlated voxels. The underlying rationale is that voxels belonging to the carotid artery should show a highly temporally correlated SUV signal across the dynamic scan, which should be identifiable using ICA. The resulting component maps were warped to MNI space using the ANTs-derived high-dimensional warping parameters and matched against a custom in-house carotid artery template in MNI space using spatial correlation to extract a subject-specific carotid component. The subject-specific carotid component in the MNI space was then automatically masked using a binary image that restricts the carotid artery to a segment in the upper part of the pars cervicalis, in line with the manual approach described above. Lastly, the masked subject-specific carotid image was warped back to native space using the ANTs derived warping parameters with nearest-neighbour interpolation to maintain a binary image. This image was further eroded using FSL to eliminate voxels close to the vessel walls, which may confound the carotid signal. The eroded binary carotid image was then applied to the native space dynamic PET image to extract the activity-time series (average and maximum value) across the 60 minutes scanning duration within the segment that corresponds to the manually selected volume (Fig. 1).

To compare the input functions, the activity concentrations obtained from continuous blood sampling were averaged over intervals corresponding to the frame durations of the PET images. Furthermore, the delay between the arrival of radioactivity in the radial artery and the carotid artery was considered by matching the IDIF peak to the peak of the AIF.

Part II of the study Image derived input function

IDIF were generated by the manual method with the five highest voxel intensity values (max5).

Quantification ParametersPart I of the study

Volume of distribution (VT) images were calculated with the AIF and IDIF using Logan plots [18], which assume that the data become linear after an equilibration time t*. t* was fitted based on the maximum error criterion, which indicates the maximum relative error between the linear regression and the Logan-transformed measurements in the segment starting from t*. The maximum error was set to 10%. The percent masked pixels were set to 0%. The Putamen, which was defined by manual placement of a VOI (sphere with a diameter of 10 mm), served as tissue region.

All images were transformed to MNI space using the established [18F]PI-2620 PET template [7]. Automatized brain normalization settings in PMOD included nonlinear warping, 8 mm input smoothing, equal modality, 16 iterations, frequency cutoff 3, regularization 1.0, and no thresholding. Using the mean voxel value of a VOI placed in the inferior cerebellum as the scaling factor, VT ratio images were calculated.

Average VT and VT ratio values were obtained in 9 PSP target regions, predefined by the atlas of basal ganglia [19], the Brainnetome atlas [20], and the Hammers atlas [21], based on earlier autopsy data [22]: globus pallidus (internus and externus), putamen, subthalamic nucleus, substantia nigra, dorsal midbrain, dentate nucleus, dorsolateral prefrontal cortex (DPFC), and medial prefrontal cortex (MPFC).

Part II of the study

In addition to VT images, distribution volume (DV) and SUV images were calculated. For computing DV images, simplified reference tissue modeling (SRTM2) was performed as implemented in the freely available QModeling toolbox (for detailed methods see [23]). Using the mean voxel value of a VOI placed in the inferior cerebellum as the scaling factor, VT ratio, DV ratio and SUV ratio images were calculated. All images were transformed to MNI space.

Average VT, VT ratio, DV ratio and SUV ratio values were obtained in the 9 PSP target regions. In addition, average VT, VT ratio, DV ratio and SUV ratio values were obtained in Braak regions [24]. The regional VT, VT ratio, DV ratio and SUV ratio values were additionally transformed into z-score values by subtracting the mean of the healthy controls from each value and then dividing by the standard deviation of the healthy controls (z-score = (value – mean) / standard deviation).

Statistics

GraphPad Prism version 9.1.2 (226) (GraphPad Software, San Diego, United States) was used for statistical testing. P values less than 0.05 were considered significant (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). Before all t-tests, normality tests (D’Agostino & Pearson test, Anderson–Darling test, Shapiro–Wilk test, Kolmogorov–Smirnov test) were performed using QQ plots. The radioactivity concentrations and the area under the curves (AUC) of the AIF and the IDIF were compared using a paired two-tailed t-test, Pearson correlation coefficients r, coefficients of determination r2 and a repeated measures ANOVA. Coefficients of variation (CoV) were estimated for regional VT and VT ratio values displayed as mean ± standard deviation and compared by a paired two-tailed t-test. Correlations between regional VT values calculated with the AIF and the IDIF were examined using Pearson correlation coefficient r. Regional and overall VT and VT ratio values of patients with PSP and healthy or disease controls were compared by using an unpaired two-tailed t-test. Comparisons between VT, VT ratio, SUV ratio and DV ratio values were done using one way ANOVA, Pearson correlation coefficients r and Cohen’s D.