Feed

Quantification of [¹¹C]CURB, an irreversible positron emission tomography (PET) radiopharmaceutical used to image fatty acid amide hydrolase (FAAH), requires invasive arterial sampling. Developing a non-invasive approach for [¹¹C]CURB is challenging, since traditional reference region models are not directly applicable and the ubiquitous brain expression of FAAH complicates the identification of a ligand-free reference region. This study aimed to introduce and validate the irreversible reference tissue model (IRTM) used in conjunction with a data-driven, clustering-based white matter (WM) pseudo-reference region.

IRTM was implemented using a coupled-fit approach to estimate a common k 2 ' , resulting in two independent parameters per volume-of-interest: R₁ and k_f . Primary quantification was performed using the macroparameters R i = K i / K 1 ' = R 1 ⁢ k 3 / k f (relative net influx) and R ⁢ k 3 = λ ⁢ k 3 / K 1 ' = R 1 ⁢ k 3 / k 2 (relative trapping index). Simulations characterized the sensitivity of IRTM to differences in cerebral blood volume between target and reference tissues. The model was validated against the gold standard arterial input function (AIF)-based quantification using retrospective [¹¹C]CURB PET data from 10 healthy participants. A partition-based clustering algorithm was used to extract the centermost WM time-activity curve as a pseudo-reference region.

In the IRTM validation study with human data, R_i emerged as a more robust metric than Rk ₃, with the latter exhibiting high between-subject variability. Although simulations identified systematic biases in IRTM estimates driven by blood volume asymmetries, IRTM-derived R_i estimates showed strong concordance with AIF-based R_i (R ² = 0.96), λk ₃ (R ² = 0.90), and K_i (R ² = 0.96). Mean errors in R_i ranged from -11.2 ± 9.9% in the frontal lobe to -3.1 ± 10.4% in the amygdala. Voxel-wise parametric maps demonstrated high image quality and signal-to-noise ratio at the individual subject level.

This study provides a validated, fully non-invasive framework for [¹¹C]CURB PET quantification. Combined with a partition-based clustering approach to select a data-driven WM pseudo-reference region, IRTM achieved excellent concordance with AIF-based measurements, supporting the feasibility of IRTM for non-invasive FAAH imaging.