Analysis of [18F]FE-PE2I

[18F]FE-PE2I is a highly selective radioligand for dopamine transporter (DAT), with reversible and relatively fast kinetics in the brain (Schou et al., 2009; Varrone et al., 2009 and 2011). The effect of radiometabolites on quantification is smaller than with [11C]PE2I (Sasaki et al., 2012). Peak uptake occurs within 10 min in all brain regions, with maximum of ∼3-5 SUV (Sasaki et al., 2012).

The binding mechanism of [18F]FE-PE2I and [11C]PE2I to DAT involves two steps: a fast step of complex formation, and slow isomerization of the complex. The latter step is three times faster with PE2I than with FE-PE2I (Kukk et al., 2018).

The effective dose from [18F]FE-PE2I is similar to other DAT radioligands. The urinary bladder receives the highest dose, followed by the liver (Lizana et al., 2018).

Arterial input function

[18F]FE-PE2I has two major radiometabolites in the plasma, representing 20% (lipophilic metabolite) and 70% (hydrophilic metabolite) of plasma radioactivity at 30 min after administration (Sasaki et al., 2012).

Metabolism is similar in control subjects and PD patients (Fazio et al., 2015).

Compartmental model

Two-tissue compartment model (2TCM), using metabolite-corrected arterial plasma curve as input function, fits all brain regions better than One-tissue compartment model (Sasaki et al., 2012). 2TCM can provide K1 and VT with good identifiability. VT estimates were stable with fit times of 60-90 min. Regional BPND can be calculated from VT estimates as

and BPND is about 4.0-4.5 in the putamen and caudate, 0.5 in the midbrain, and 0.2 in the thalamus (Sasaki et al., 2012). It should be noted that 2TCM does not account for the lipophilic radiometabolite in the brain, causing biased estimates of model parameters. Using the sum of parent radioligand and lipophilic radiometabolite as input function would introduce a different bias because the tissue kinetics of these components are not similar; additionally, this method did not produce good VT estimates, especially in the cerebellum (Sasaki et al., 2012).


Simplified reference tissue input model (SRTM) with cerebellum as reference tissue provides BPND estimates that correlate well with estimates from 2TCM, but the results are overestimated in the low-density regions and underestimated in high-density regions. On the other hand, inter-subject variability of BPND was smaller with SRTM than with 2TCM (Sasaki et al., 2012).

The R1 from SRTM provides an estimate relative regional cerebral blood flow (Jacobson Mo et al., 2022).

Logan plot

Logan graphical method with cerebellum input can be used to calculate BPND images from a 66-min PET scan (Fazio et al., 2015). Wavelet-based denoising strategy has been used to reduce the voxel-to-voxel noise level (Fazio et al., 2018), and it has been shown to provide good repeatability and reliability in PD patients (Kerstens et al., 2020).

SUV ratio

SUV ratio (SUVR) at ∼70 min after injection correlates well with BPND, and is not markedly affected by K1 (Ikoma et al., 2015.

At pseudoequilibrium (peak time of tissue - reference tissue curve) the SUVR-1 represents BPND. SUVR-1 calculated from a static 25 min scan starting 16 min after administration is in good agreement with BPND from reference input Logan plot (Sonni et al., 2016). Similar time range was found to be optimal in comparison to BPND by Delva et al (2020), but they proposed using occipital cortex as reference tissue instead of cerebellum, and ratio at 50-60 min for clinical practise. Early (15-45 min) SUVR-1 provides good reliability for simplified DAT quantification, and both early and late (51-81 min) SUVR-1 are suitable for differentiating PD patients from controls (Brumberg et al., 2021).

See also:


Varrone A, Tóth M, Steiger C, Takano A, Guilloteau D, Ichise M, Gulyás B, Halldin C. Kinetic analysis and quantification of the dopamine transporter in the nonhuman primate brain with 11C-PE2I and 18F-FE-PE2I. J Nucl Med. 2011; 52(1): 132-139. doi: 10.2967/jnumed.110.077651.

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Updated at: 2022-12-09
Created at: 2022-01-17
Written by: Vesa Oikonen