Analysis of [11C]PE2I
Unlike many other DAT ligands, [11C]PE2I is a highly selective radioligand for DAT, and does not accumulate in regions rich in the serotonin or noradrenaline transporter. The extrastriatal PE2I binding is very low. In vitro binding of [125I]PE2I in substantia nigra was about 50% of binding in caudate and putamen, and 15% in thalamus (Hall et al., 1999).
[11C]PE2I has favourable radiation dosimetry, so that two or three repeated PET studies per patient can be performed (Ribeiro et al., 2007).
[11C]PE2I shows relatively high non-specific binding to white matter of both cerebrum and cerebellum: it is evident in most published in vitro binding assays and PET images (Hall et al., 1999; Poyot et al., 2001); in vitro accumulation of [125I]PE2I corresponds to 30-35% of the specific binding in caudate and putamen (Hall et al., 1999). This is probably due to its relatively high lipophilicity (Hall et al., 1999).
Compartment model analysis
Pinborg et al. (2002, 2005) have fitted traditional one- and two-tissue compartment models to human [123I]PE2I-SPECT data. They found that for all ROIs, AIC indicated a significantly better fit using the two-tissue compartment model compared with the one-tissue compartment model, and that this was most evident in the receptor poor cortical regions. They assumed that the second tissue compartment may be explained by parallel compartment (heterogeneity) instead of serial compartment (nonspecific binding), which might in turn explain the non-physiological rate constants and compartment distribution volumes (Pinborg et al., 2002). For [11C]PE2I, Odano et al (2012) proposed using two-tissue compartmental model, with K1/k2 constrained to the value obtained in reference region.
At early publications, because of the poor resolution of SPECT and PET scanners, it was expected that the relatively high accumulation in white matter could be forming most of the "parallel compartment". Another explanation - presence of a radioactive metabolite passing the blood-brain barrier - was at that time suggested to be unlikely on basis that the two main metabolites of [11C]PE2I are polar compounds (Halldin et al., 2003). But now with more recent studies it has been confirmed, both with rat and human studies, that metabolites do exist (Jucaite et al., 2006; Shetty et al., 2007; Hirvonen et al., 2008). At least two or three metabolites have been identified for [11C]PE2I in human studies (Jucaite et al., 2006, Hirvonen et al., 2008). Although the metabolites contribute to the tissue uptake, tissue heterogeneity (or partial volume) affects the results, too (Odano et al., 2006 and 2007).
Pinborg et al (2002) estimated that the first-pass extraction fraction of [123I]PE2I was about 0.72 in striatum and about 0.34-0.42 in cortical regions. With [11C]PE2I the first-pass extraction fraction might be even higher. This suggests that when vascular radioactivity concentration is concerned, arterial blood concentration represents only the arterial blood volume fraction, and venous volume fraction can possibly be neglected.
According to Pinborg et al (2005), the full reference tissue input model and the simplified reference tissue input model (SRTM) performed equally well with [123I]PE2I SPECT data based on AIC, and the BPND estimates were similar than with Logan analysis when study length was 90 min or longer (Pinborg et al., 2005). But later DeLorenzo et al. (2009) found that SRTM does actually not perform as well as Logan analysis. Pixel-by-pixel SRTM analysis of [11C]PE2I data has provided excellent results when comparing subjects with and without neurodegeneration (Jonasson et al., 2013), and shown promise for differential diagnosis of parkinsonian disorders (Appel et al., 2015).
A non-iterative two tissue compartment model (2TCNI) has been suggested by DeLorenzo et al (2009). With this non-iterative method, the data is compared to a set of pre-calculated functions (Ogden et al., 2007), rather than performing non-linear least squares fit.
DeLorenzo et al (2009) have compared results from one- and two-tissue compartment models both with iterative and non-iterative solutions, basis pursuit method, Logan analysis, LEGA and bloodless Logan analysis, LEGA, and SRTM. 2TCNI model with 100 min scans is recommended. 2TCNI is found to perform best in terms of test-retest percentage difference, within subject mean sum of squares, variance, identifiability (data stability) and time stability (method reaches the steady state earlier than others).
Still, it is not evident if the metabolites have been taken into account in interpretation of method comparison. It might be that metabolites are affecting the results in an unknown way. For example, the determination of the length of the scan may be affected by the fact that it is actually the metabolites that finally reach a stable state instead of specifically bound ligand. So we cannot be sure if the specific binding has reached stable state actually earlier.
In Turku, the iterative version of 2TC model could also be considered. DeLorenzo et al (2009) suspected that the fit was trapped into local minima, but for us it should not be a problem because of global optimization methods.
Poyot et al (2001) validated the use of multi-injection protocol with arterial input in primates for estimation of B’max. In putamen and caudate, B’max correlated strictly with [125I]PE2I determined in vitro, although the values were different.
Non-compartment analysis (Logan plot)
SPECT studies in healthy volunteers, Parkinson’s disease (PD) patients, and nonhuman primates with [123I]PE2I have validated the use of the tracer and Logan graphical method with occipital cortex input for studying the binding of PE2I and the clinical features of PD (Pinborg et al., 2002 and 2005; Prunier et al., 2003a and 2003b). In SPECT studies, occipital cortex is used as reference region instead of cerebellum because occipital cortex is readily visible and well defined in SPECT images.
Pinborg et al (2002) applied Logan plot to the [123I]PE2I SPECT studies with metabolite corrected plasma input, and reference tissue input with and without reference tissue k2 correction (k'2=0.013±0.006 min-1 from six subjects, determined from the intercept of the plasma input Logan plot of the occipital cortex). Their Logan plot linearity is not reached before 120 min in all cases, neither with plasma or occipital cortex input. Surprisingly, applying k'2 correction did not decrease the time to reach linearity, but even increased it in some cases. Pinborg et al (2002) recommend using Logan plot with occipital cortex input without k'2 correction.
Hirvonen et al (2008) have used Logan analysis with [11C]PE2I with arterial plasma input.
DeLorenzo et al (2009) have studied Logan analysis both with blood input, and reference region (cerebellum) input in a [11C]PE2I PET study. They suggest that 2TCNI method as the best option, but both Logan versions are also reported to perform reasonably well. The results correlate well with 2TCNI, even though it was studied that the assumption of only one tissue compartment in reference region is actually not holding true for [11C]PE2I. DeLorenzo et al also note that it has been shown that Logan plot can underestimate non-displaceable binding potential.
Likelihood estimation graphical analysis (LEGA) was studied by DeLorenzo et al (2009) both with blood and reference region (cerebellum) input. It was found to give similar results as Logan analysis, with no significant differences. Yet, Logan analysis performed slightly better in terms of test-retest percentage difference, within subject mean sum of squares and variance.
Seki et al (2010) have suggested the original multilinear reference tissue model (MRTMo). In their publication it was found to perform better than SRTM method. MRTMo is one of the variations of graphical approach, where a multilinear regression is obtained after a certain equilibrium time (Seki et al., 2010; Ichise et al., 1996).
Peak equilibrium (ratio) analysis
Peak equilibrium analysis, (striatum-occipital cortex)/occipital cortex ratio at the peak of (striatum-occipital cortex) curve, provided similar BP estimates and SEM than simplified reference tissue model for [123I]PE2I (Pinborg et al., 2005). However, the ratio was highly variable from 0 to 70-80 min after injection, making identification of the correct scan period important and thus in practice requiring dynamic acquisition. Therefore, Pinborg et al (2005) suggest using bolus+infusion protocol even in clinical studies, because then a certain scan time (after 120 min of constant infusion) can be applied for all subjects.
With [18F]FE-PE2I, early ratio provides good reliability for simplified DAT quantification, and both early and late ratio is suitable for differentiating PD patients from controls (Brumberg et al., 2021).
Simplified reference tissue model (SRTM)
At least Jucaite et al (2005), Odano et al (2006), Leroy et al (2007), Ciumas et al (2008), Arakawa et al (2009), and Artiges et al (2017) have used SRTM with cerebellum as reference region to estimate regional BPND values. Jucaite et al (2006) compared SRTM and plasma input models, and suggest that SRTM can be used in clinical studies when arterial sampling need to be avoided. BPND and R1 from SRTM could be used for differential diagnosis of parkinsonism (Appel et al., 2015).
In a later study by DeLorenzo et al (2009), the SRTM has been noted to perform worse than bloodless Logan analysis or LEGA. On the other hand, Seki et al (2010) have suggested that original multi-linear reference tissue model (MRTMo) should be used instead of SRTM.
About reference regions
DeLorenzo et al (2009) have studied thoroughly the kinetics of [11C]PE2I and at first they studied a large set of brain regions to find a reference region, where best fitting model would be 1TC. This is an assumption made by most reference region input methods. The cerebellum, cuneus, gyrus, dorsal and lateral prefrontal cortex, fusiform gyrus, gyrus rectus, and uncus were run through cluster analysis and all clusters were fit with both 1TC and 2TC model. It turned out that all of these regions and their subregions fit better with 2TC model (lower AIC measure than 1TC model). DeLorenzo et al (2009) still did not exclude the usage of reference region models. Supervised cluster analysis can be used to extract reference region for SRTM in diagnostic PET (Jonasson et al., 2017). But when trying to quantitatively analyse [11C]PE2I data with these methods, one should bear in mind that the reference region models are not fitting the data perfectly.
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Updated at: 2022-01-17
Created at: 2010-04-30
Written by: Vesa Oikonen