Search

Introduction: Recent evidence suggests the blood-to-brain influx rate (K1) in TSPO PET imaging as a promising biomarker of blood-brain barrier (BBB) permeability alterations commonly associated with peripheral inflammation and heightened immune activity in the brain. However, standard compartmental modeling quantification is limited by the requirement of invasive and laborious procedures for extracting an arterial blood input function. In this study, we validate a simplified blood-free methodologic framework for K1 estimation by fitting the early phase tracer dynamics using a single irreversible compartment model and an image-derived input function (1T1K-IDIF). Methods: The method is tested on a multi-site dataset containing 177 PET studies from two TSPO tracers ([11C]PBR28 and [18F]DPA714). Firstly, 1T1K-IDIF K1 estimates were compared in terms of both bias and correlation with standard kinetic methodology. Then, the method was tested on an independent sample of [11C]PBR28 scans before and after inflammatory interferon-α challenge, and on test-retest dataset of [18F]DPA714 scans. Results: Comparison with standard kinetic methodology showed good-toexcellent intra-subject correlation for regional 1T1K-IDIF-K1 (ρintra = 0.93 ± 0.08), although the bias was variable depending on IDIF ability to approximate blood input functions (0.03-0.39 mL/cm3/min). 1T1K-IDIF-K1 unveiled a significant reduction of BBB permeability after inflammatory interferon-α challenge, replicating results from standard quantification. High intra-subject correlation (ρ = 0.97 ± 0.01) was reported between K1 estimates of test and retest scans. Discussion: This evidence supports 1T1K-IDIF as blood-free alternative to assess TSPO tracers' unidirectional blood brain clearance. K1 investigation could complement more traditional measures in TSPO studies, and even allow further mechanistic insight in the interpretation of TSPO signal. [ABSTRACT FROM AUTHOR]

We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation. Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to incorporate a priori knowledge about the AIF's functional form and shape, enabling precise predictions of the concentrations of [ 11 C ]PBR28 in whole blood and the free tracer in metabolite-corrected plasma. We found a robust linear correlation between our model's predicted AIF curves and those obtained through traditional, invasive measurements. We achieved an average cross-validated Pearson correlation of 0.86 for whole blood and 0.89 for parent plasma curves. Moreover, our method's ability to estimate the volumes of distribution across several key brain regions – without significant differences between the use of predicted versus actual AIFs in a two-tissue compartmental model – successfully captures the intrinsic variability related to sex, the binding affinity of the translocator protein (18 kDa), and age. These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings. • Tracer metabolism can be retrieved from PET images Integrating prior knowledge in NN. • PET dynamic is channel-wise encoded via 3D separable convolutions. • PET quantification is not affected by the use of predicted AIFs. [ABSTRACT FROM AUTHOR]