Quantification of [11C]PBR28 PET studies (draft)

PBR28 is a selective second-generation ligand with high affinity for the translocator protein (TSPO), formerly known as peripheral benzodiazepine receptor (PBR), a marker of inflammation. Increased distribution volume of [11C]PBR28 has been observed in the brain of baboons and humans after injection of E. Coli lipopolysaccharide (Hannestad et al., 2012; Sandiego et al, 2015; Yoder et al, 2015). The cerebral artery occlusion model of neuroinflammation and stroke in rats has shown a localized increase in [11C]PBR28 SUV (Imaizumi et al, 2007; Toth et al, 2016), and that increase could be reversed by injection of PK11195 (Imaizumi et al, 2007). Also herpes simplex encephalitis model in rats resulted into increased [11C]PBR28 uptake (Parente et al, 2016). Alzheimer’s disease model in mice has shown increased [11C]PBR28 uptake (Mirzaei et al, 2016). Shao et al (2013) demonstrated increased [11C]PBR28 SUV in rat models of acute inflammation (induced with carrageenan) and adjuvant arthritis model. [11C]PBR28 SUV increased in the aged rats with increasing neuroinflammation (Walker et al, 2015).

In humans, genetic polymorphism (rs6971) affects the binding of [11C]PBR28 and other TSPO ligands, leading to three TSPO binding profiles: high-affinity binders (HAB), low-affinity binders (LAB) with 50-fold reduction in affinity, and mixed-affinity binders (MAB) which express both TSPO types in equal proportion (Owen et al, 2010 and 2012; Kreisl et al., 2010 and 2013a; Yoder et al, 2013). [11C]PBR28 uptake in LAB group is too low to be reliably quantified with PET; therefore genotyping must be done prior to PET to omit the LAB subjects. The rs6971 polymorphism affects cholesterol binding to TSPO, and in MAB group [11C]PBR28 uptake has been shown to correlate negatively with plasma cholesterol level (Kim et al., 2018).

Increased uptake of [11C]PBR28 has been seen in MS patients (Oh et al, 2011; Park et al, 2015), in ALS (Zürcher et al, 2015), and in HIV-positive humans (Vera et al, 2016). [11C]PBR28 binding correlates with severity of Alzheimer’s disease (Kreisl et al, 2013b). Myeloperoxidase inhibition in patients with Parkinson’s disease decreased the binding of [11C]PBR28 (Jucaite et al., 2015). TSPO blocking study in rhesus monkeys suggests that non-displaceable uptake is very small, only about 5% of the total distribution volume (Imaizumi et al., 2008). In human TSPO blocking study VND was estimated to be about 2, while VND was about 3 and 4 for MAB and HAB groups, respectively, suggesting that BPND in HAB is twice that of MABs (Owen et al, 2014).

The highest uptake of [11C]PBR28 is in kidneys, spleen, heart, and lungs, organs with high TSPO expression, except in LAB where uptake can only be seen in the liver, gallbladder, and urinary bladder (Brown et al, 2007; Kreisl et al, 2010).


Scan protocol

90-minute dynamic scan with arterial blood sampling is typically used (Kreisl et al, 2013b; Owen et al, 2014; Risso et al, 2014; Gershen et al, 2015; Bloomfield et al, 2016; Collste et al., 2016). Hirvonen et al (2012), Hannestad et al (2013), Park et al (2015), and Sandiego et al (2015) scanned subjects for 120 minutes. Jucaite et al (2015) performed the analysis using 45-, 60-, and 90-min time intervals, and recommended using 60-min scans to minimize contribution of radioligand metabolites to the brain data.

Scans should be performed at the same time of day, because afternoon scans tend to provide higher uptake values than morning scans because of changes in plasma concentrations (Collste et al., 2016).

Analysis methods

TSPO is expressed not only on activated microglia but also in endothelial walls and immune cells of the blood. The marked endothelial binding of [11C]PBR28 and other second-generation TSPO ligands prevents the use of traditional reference tissue models or using supervised cluster analysis to extract the reference time-activity curve, since there is no TSPO-free region (Imaizumi et al., 2008).

Quantification of [11C]PBR28 brain PET studies is usually performed using reversible two-tissue compartmental model (2TCM). Compartmental models with arterial plasma input provide us the distribution volume (VT). VT estimated using multilinear analysis MA1 with fit start time of 30 min was found to correlate well with VT from 2TCM by Hannestad et al (2013) and Park et al (2015). Sandiego et al (2015) used LEGA method (start time 30 min) to calculate parametric VT images. In contrast to several human studies, in rhesus monkeys the 1TCM was found to provide more accurate VT than 2TCM (Imaizumi et al., 2008).

TSPO tracer kinetics in the brain seem to consist of a slow irreversible (during PET scan) component localized in the brain main vasculature, venous sinuses, and arteries, as well as a faster reversible component attributed to microglia (Rizzo et al, 2014). When the slow component for endothelial vascular TSPO binding has been included in the compartmental model (2TCM-1K), substantially better fits have been obtained. Resulting VT estimates are about one-third of the VTs from conventional 2TCM analysis, and correlation is poor (Rizzo et al., 2014). Simulations suggest that 2TCM-1K would provide VT that is more sensitive to true variations in microglial TSPO binding. In addition to endothelial vascular binding, 2TCM-1K might also correct for tissue uptake of label-carrying plasma metabolites of the tracer. 2TCM-1K model has since been used by Bloomfield et al (2016).

Oh et al (2011) used Logan plot to calculate parametric VT images from 120-min PET scans; image noise leads to negative bias in Logan plot VT, and VT in image was indeed 9% smaller than VT from regional analysis. In rats, both Logan plot and 2TCM are feasible (Toth et al, 2015).

VT shows high intersubject variability, also after taking TSPO genotype into account, possibly because of variable plasma protein binding of the tracer (Rizzo et al, 2014; Bloomfield et al, 2016). Therefore VT has been divided by the measured free fraction of tracer in the plasma (fp) (Oh et al. 2011; Hannestad et al, 2013; Gershen et al, 2015; Lyoo et al, 2015). However it has been estimated that the measurement error and noise from fp may even increase the variability in “corrected” VT (Park et al, 2015; Bloomfield et al, 2016), and therefore it has been proposed that VT ratio should be used instead, obtained by dividing regional VT by VT from the whole brain (Bloomfield et al, 2016). Lyoo et al (2015) did not find any difference in COV% of VT and VT/fp. Kanegawa et al (2016) have shown that [11C]PBR28 binding in blood cells correlates with VT in the brain, even when TSPO genotype is taken into account. Normalization of brain VT by VT of blood cells reduced intra-individual variability only when time between PET scans was short (Kanegawa et al., 2016).

The mean absolute variability in VT (estimated using 2TCM where VB was fixed to 5%) in the brain gray matter in healthy human subjects, determined in test-retest setting, was 18.3±12.7 % and ICC 0.90-0.94 for 91-min scans (Collste et al., 2016); reducing analysis time to 63 min did not increase the variability. Performing both of the scans at the same time of the day reduced variance to 15.9±12.2 % for the 91-min analysis, since the afternoon scans resulted in higher VT estimates than morning scans; this was caused by reduced plasma TAC in the afternoon scans (Collste et al., 2016).

VT in HAB group is about 40% higher than in MAB group (Kreisl et al, 2013a). Therefore Gershen et al (2015) multiplied the VT in MAB group by 1.4.

TSPO occupancy study was analyzed 2TCM, using Lassen plot to estimate VND (Frankle et al., 2017).

Logan plot with reference tissue input has also been used to analyze [11C]PBR28 data. Datta et al (2017) used caudate as the reference region when studying brain white matter in healthy subjects and subjects with MS.


Standardized uptake value (SUV) is a semiquantitative method that is often used to analyze also [11C]PBR28 data. Toth et al (2015) have shown that SUV and VT correlate well in healthy rats and mice, and test-retest variability is good. However, in baboon studies correlation was poor (Yoder et al, 2015).

In human studies, SUV calculation time was set to 40-90 min by Kim et al (2013) and Gershen et al (2015), 60-90 min by Zürcher et al (2015), and 60-120 min by Hirvonen et al (2012). If inflammation is localized on one side of the only, for example in stroke or epilepsy, Asymmetry index (AI) can be calculated from SUV (Gershen et al., 2015). Lyoo et al (2015) noticed that uptake in cerebellum did not differ between healthy subjects and patients with Alzheimer’s disease (AD), and proposed using SUV ratio (SUVR) from 60-90 min, with cerebellum as pseudo-reference region. Nair et al (2016) reported that the best test-retest variability in AD study was obtained when using whole brain as the pseudo-reference. Albrecht et al (2018) proposed using occipital cortex as pseudo-reference region in patients with chronic low back pain or ALS.

In a mice study SUV between 30 and 60 min was calculated, and normalized by dividing it with the SUV of heart (Mirzaei et al., 2016).

Brown adipose tissue

Because of the abundance of mitochondria in brown adipose tissue (BAT), TSPO tracers have the potential to be used for in vivo detection of BAT. Ran et al. (2018) have successfully used [11C]PBR28 PET late scan (60-90 min p.i.) in BAT imaging in humans.

Arterial plasma input

Blood-to-plasma conversion

Correction of arterial blood TAC to plasma requires that plasma-to-blood ratio is known. This ratio for [11C]PBR28 is changing during the scan, and it is different in the binding groups. Blood-to-plasma ratios can be fitted using program fit_bpr, with option -model=ihillb for HAB and MAB groups, and option -model=ihillb for LAB group.

Population ratio curves for each binding group are implemented in programs b2plasma and p2blood.

Plasma metabolite correction

Arterial plasma TAC needs to be corrected for metabolites. Collste et al (2016) fitted plasma parent fractions with three-exponential function, and Albrecht et al (2018) with biexponential function. Parente et al (2016) used one-phase exponential function in rat studies. Narendran et al (2014) applied the Hill model fitting. Bloomfield et al (2016) used extended Hill (sigmoidal) function for fitting [11C]PBR28 plasma parent fractions:

, where p1 > 0.

Owen et al (2014) fitted parent fractions using sigmoid function:

Park et al (2015) fitted plasma parent fractions with an inverted gamma function.

See also:


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Created at: 2015-08-03
Updated at: 2018-05-18
Written by: Vesa Oikonen, Jouni Tuisku