Background

Cerebral blood flow (CBF) is known to decline with age, and elderly individuals (75–80 years) may present with reductions in CBF of up to 25 % compared with young adults (±25 years) [1, 2]. In the context of Alzheimer’s disease (AD), additional reductions in CBF have been reported in several cortical brain regions such as the frontal, parietal and temporal cortices with both absolute reductions as well as relative to cerebellar grey matter reference tissue [3, 4]. This pattern of CBF reductions is considered characteristic of AD pathology and may therefore be used as proxy for measuring disease severity or progression [5, 6]. The gold standard technique for measuring CBF is [15O]H2O positron emission tomography (PET) [7], but MR-based techniques such as arterial spin labelling (ASL) have also been introduced [5]. More recently, several studies have evaluated whether a valid proxy of cerebral perfusion can be obtained from the early frames of dynamic scans using currently available PET tracers (e.g. for measuring amyloid-β or tau burden) [3, 8]. The relative influx rate (R1=K1/K1’), which can be calculated from these early frames, is an indirect measure of relative CBF as it is also affected by the extraction fraction (K1=E·CBF). In particular, for the amyloid tracer [11C]PiB, Chen and colleagues have reported that in cortical regions, relative tracer delivery R1 showed moderate-to-high correlations with relative CBF measured using dynamic [15O]H2O PET (with a range of ρ=0.68–0.84, p<0.001, across cortical regions and a value of ρ=0.82, p<0.001, for the global cortical region), thereby indicating that [11C]PiB could be used for dual-biomarker imaging [9]. Furthermore, Bilgel and colleagues demonstrated that in a longitudinal setting with an average follow-up duration of 2.5 years, rates of change as measured with R1 showed low-to-moderate correlations with changes in [15O]H2O PET CBF (median r =0.42 across cortical regions and a value of r =0.65 for the global cortical region). In addition, rates of change as measured with R1 required the smallest sample-size for detecting group-wise differences (27 % reduction compared with [15O]H2O PET), suggesting this proxy could be suitable for tracking long-term longitudinal changes in CBF [8]. As longitudinal PET studies in AD become more common for measuring treatment efficacy or disease progression, it is important to know the intrinsic variability of R1 in order to determine what magnitude of change in R1 signifies an actual change. Therefore, the purpose of the present retrospective analysis of a previously reported test-retest (TRT) [11C]PiB study was to assess the precision of [11C]PiB R1.

Materials and Methods

Subjects

Data from twelve participants belonging to a TRT study conducted within the Amsterdam UMC, location VUmc, were reanalysed as the original study only reported TRT variability for the non-displaceable binding potential [10]. This dataset consisted of five cognitively unimpaired (CU) subjects, one patient with mild cognitive impairment (MCI) and six with AD dementia, which in the present study was used to examine TRT variability for R1 [10]. Before enrolment, written informed consent was obtained from all individual participants included in the study, and the Medical Ethics Review Committee of the Amsterdam UMC, location VUmc, had approved the study.

Image Acquisition

All subjects underwent a structural T1-weighted MR scan on a 1.5T Siemens Sonata scanner and, within 1 week, two 90-min dynamic [11C]PiB PET scans (test and retest) on a Siemens ECAT EXACT HR+ scanner [10]. Each dynamic scan consisted of 23 consecutive time frames (1×15, 3×5, 3×10, 2×30, 3×60, 2×150, 2×300, 7×600 s). All participants received an intravenous injection of, on average 332±70 MBq for test (353±26 for CUs, 138 for the MCI patient, and 342±66 for the AD dementia patients)and 376±43 MBq (355±37 for CUs, 368 for the MCI patient, and 393±47 for the AD dementia patients) for retest scans.

Image Processing

Structural T1-weighted MR images were co-registered to their corresponding PET image segmented into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) using PVE-lab software [11]. Next, volumes of interest (VOIs) were delineated based on the Hammers atlas and a reference tissue time–activity curve (TAC) of the cerebellar grey matter was extracted [12, 13].

Parametric Analysis

The PPET software tool [14] with the voxel-based implementation of the two-step simplified reference tissue model (SRTM2), as validated for [11C]PiB, and cerebellar grey matter as reference tissue were used to compute relative tracer delivery (R1) images [15,16,17]. For SRTM2, k2' was determined across all voxels with a BPND higher than 0.05 by taking the median k2' from a first run using receptor parametric mapping (RPM) [18]. Regional R1 values were obtained by superimposing the following grey matter VOIs on the parametric images: medial and lateral anterior temporal lobe, posterior temporal lobe, superior, middle and inferior temporal gyrus, fusiform gyrus, parahippocampal and ambient gyrus, anterior and posterior cingulate gyrus, middle and orbitofrontal gyrus, gyrus rectus, inferior and superior frontal gyrus, pre- and post-central gyrus, superior parietal gyrus and the (infero)lateral remainder of the parietal lobe and a global cortical composite region (i.e. volume-weighted average across all target regions).

Statistical Analysis

Statistical analyses were performed in R (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria). First, global R1 values were compared between CU and AD dementia groups using a non-parametric Mann-Whitney U test, separately for test and retest scans. Next, TRT variability was calculated for regional and global cortical R1 values according to Eq. 1, where T represents the estimate of R1 measured during test, and R the one measured during retest.

$$ TrT\ variability\ \left(\%\right)=\frac{\mid T-R\mid }{0.5\bullet \mid T+R\mid}\bullet 100 $$
(1)

In addition, a correlation analysis was used to assess the relationship between TRT variability and regional volume. Furthermore, to assess the relationship between test and retest R1 measures, linear mixed effect models (LME) were fitted and correlation coefficients were calculated using the nlme and MuMIn packages, respectively [19, 20]. Visual read (amyloid-β positive or negative) was used as a covariate and the analysis accounted for the within-subject correlation between regions. Finally, a Bland-Altman analysis was used to assess potential bias between test and retest R1 using the blandr package [21, 22].

Results

Participant characteristics are shown in Table 1. Relative tracer delivery measures (R1) are reported in Table 2, with a significantly lower global R1 in AD dementia patients compared with CU participants, for both test and retest scans (p < 0.01).

Table 1 Subject demographics
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Table 2 Relative tracer delivery values by diagnostic group
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Regional and global cortical TRT variability values are presented in Table 3. TRT variability for the global cortical composite was low (1.70 %), while the range of regional TRT variability showed slightly higher values (range: 1.52–5.78 %). Furthermore, there was a trend effect towards smaller TRT variability for larger regions (R2=0.14, p=0.09).

Table 3 Regional test-retest variability (%) of R1
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LME analyses showed that test and retest R1 values were strongly correlated and that the slope was not significantly different from 1 (R2=0.92, slope=0.98 C.I. [0.94–1.01], p<0.001). Furthermore, amyloid status as measured by visual read did not have a significant effect on this relationship. Finally, Bland-Altman analysis showed a negligible bias (0.69±3.07 %) between test and retest R1 (Fig. 1). All analyses were also carried out using RPM-derived R1 which resulted in essentially identical results (data not shown).

Fig. 1.
figure 1

Relationship between SRTM2-derived test and retest R1. (a) The correlation between R1 test and retest measures, with R2 and slope parameters corresponding to the LME analysis and (b) a Bland-Altman plot, which indicates the bias between the two measures.

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Discussion

The present study assessed precision of the R1 parameter, a measure of relative tracer delivery, through a retrospective analysis of a previously reported [11C]PiB test-retest study [11]. Low test-retest variability was observed for SRTM2-derived R1, and this was true for regions of different sizes.

Differences in R1 between diagnostic groups were as expected, with lower average R1 values in AD dementia patients compared with CU participants. This finding is in agreement with existing literature where decreases in (relative) perfusion related to AD pathology have been reported for both R1 and [15O]H2O PET studies [3, 23, 24]. Furthermore, by incorporating these two groups, the present study covered the entire range of R1 values that would be expected in clinical studies across the AD spectrum.

Excellent TRT variability was observed for the global cortical composite (1.70 %) and only a slightly poorer TRT variability for some of the smaller regions (max 5.8 %). These findings were supported by the results of the LME analysis which showed a high correlation between test and retest R1 measures (R2=0.92) and a slope that was close to identity. As expected, the results indicate that smaller TRT variability was associated with larger regions. This finding suggests that studies should consider looking at relatively larger regions with PET when their aim is to detect small (<5 %) changes. Despite distinct kinetics, the present findings were also comparable with results from a [18F]florbetapir study that assessed TRT variability of SRTM-derived R1 in a very similar population in terms of age and diagnosis (max. TRT variability of 6 %) [25]. Comparing [11C]PiB R1 TRT variability with TRT variability of absolute perfusion as measured with the gold-standard, [15O]H2O PET [26], shows that the present results are slightly better, indicating that R1 is a more precise measure likely due to the fact that it is a relative measure. Furthermore, given that R1 is an indirect measure of perfusion, variation in the extraction fraction may compensate day-to-day fluctuations in flow to maintain constant delivery. On the other hand, this also means that alterations in extraction fraction may bias R1, as opposed to the gold standard [15O]H2O PET which provides a direct measurement of CBF. Unfortunately, to date, no studies have reported TRT variability of relative perfusion as measured with [15O]H2O PET with cerebellar grey matter reference tissue for a direct comparison. Nonetheless, a study by Bilgel and colleagues compared long-term variability of CBF proxies (i.e. [11C]PiB PET-derived R1 and early frame standardised uptake value ratios) to that of [15O]H2O PET and reported that, across these three measures, the highest longitudinal stability was obtained with R1. The present study demonstrates that R1 also has low short-term variability. Therefore, R1 could be considered a valid alternative to measuring longitudinal changes in CBF, thereby circumventing the need for a separate [15O]H2O PET scan. Unfortunately, it remains unclear whether changes across a lifetime are comparable between R1 and rCBF as measured by [15O]H2O PET. One study reports that, in an elderly population (77±8 years old), the yearly percentage change in R1 was lower (−0.28 %) than that in regional CBF (−0.41 %) as measured by [15O]H2O PET, although it was not reported whether this difference was significant [8]. Nevertheless, a smaller change in R1, especially in elderly subjects, may be related to an increased extraction fraction [27]. However, longitudinal studies of R1 in a younger population are needed to confirm whether the same difference is present earlier in life. Furthermore, using a relative parameter to measure CBF such as R1 essentially assumes that there are no CBF changes in the reference tissue (R1=K1/K1’). In this regard, it should be noted that differences in whole cerebellum CBF (a commonly used reference tissue) have been reported when comparing AD dementia patients and age-matched controls [28]. In contrast, such differences have not been demonstrated for cerebellar cortex CBF by studies using a similar design in terms of technique and participants [24, 29,30,31]. This suggests that careful interpretation is required when comparing longitudinal R1 measurement between AD  dementia patients and controls or that alternative reference tissues, unaffected by CBF changes, should be considered. Yet, further research is required to understand whether such changes in cerebellar CBF also occur in early AD stages.

Conclusion

Relative tracer delivery rate R1 of [11C]PiB showed high global and regional precision in participants covering the AD spectrum. Therefore, [11C]PiB R1 appears to be a stable parameter for measuring cross-sectional differences and longitudinal changes in relative CBF.