Population-based input function and image-derived input function for [¹¹C](R)-rolipram PET imaging: methodology, validation and application to the study of major depressive disorder

A Comparison of Arterial Input Function Interpolation Methods for Patlak Plot Analysis of 68Ga-PSMA-11 PET Prostate Cancer Studies

Positron emission tomography (PET) imaging enables quantitative assessment of tissue physiology. Dynamic pharmacokinetic analysis of PET images requires accurate estimation of the radiotracer plasma input function to derive meaningful parameter estimates, and small discrepancies in parameter estimation can mimic subtle physiologic tissue variation. This study evaluates the impact of input function interpolation method on the accuracy of Patlak kinetic parameter estimation through simulations modeling the pharmacokinetic properties of [68Ga]-PSMA-11. This study evaluated both trained and untrained methods. Although the mean kinetic parameter accuracy was similar across all interpolation models, the trained node weighting interpolation model estimated accurate kinetic parameters with reduced overall variability relative to standard linear interpolation. Trained node weighting interpolation reduced kinetic parameter estimation variance by a magnitude approximating the underlying physiologic differences between normal and diseased prostatic tissue. Overall, this analysis suggests that trained node weighting improves the reliability of Patlak kinetic parameter estimation for [68Ga]-PSMA-11 PET.

Kinetic Modeling of Brain [18-F]FDG Positron Emission Tomography Time Activity Curves with Input Function Recovery (IR) Method

Accurate positron emission tomography (PET) data quantification relies on high-quality input plasma curves, but venous blood sampling may yield poor-quality data, jeopardizing modeling outcomes. In this study, we aimed to recover sub-optimal input functions by using information from the tail (5th-100th min) of curves obtained through the frequent sampling protocol and an input recovery (IR) model trained with reference curves of optimal shape. Initially, we included 170 plasma input curves from eight published studies with clamp [18F]-fluorodeoxyglucose PET exams. Model validation involved 78 brain PET studies for which compartmental model (CM) analysis was feasible (reference (ref) + training sets). Recovered curves were compared with original curves using area under curve (AUC), max peak standardized uptake value (maxSUV). CM parameters (ref + training sets) and fractional uptake rate (FUR) (all sets) were computed. Original and recovered curves from the ref set had comparable AUC (d = 0.02, not significant (NS)), maxSUV (d = 0.05, NS) and comparable brain CM results (NS). Recovered curves from the training set were different from the original according to maxSUV (d = 3) and biologically plausible according to the max theoretical K1 (53//56). Brain CM results were different in the training set (p < 0.05 for all CM parameters and brain regions) but not in the ref set. FUR showed reductions similarly in the recovered curves of the training and test sets compared to the original curves (p < 0.05 for all regions for both sets). The IR method successfully recovered the plasma inputs of poor quality, rescuing cases otherwise excluded from the kinetic modeling results. The validation approach proved useful and can be applied to different tracers and metabolic conditions.

Comparison of cardiac image-derived input functions for quantitative whole body [18F]FDG imaging with arterial blood sampling

Introduction: Dynamic positron emission tomography (PET) and the application of kinetic models can provide important quantitative information based on its temporal information. This however requires arterial blood sampling, which can be challenging to acquire. Nowadays, state-of-the-art PET/CT systems offer fully automated, whole-body (WB) kinetic modelling protocols using image-derived input functions (IDIF) to replace arterial blood sampling. Here, we compared the validity of an automatic WB kinetic model protocol to the reference standard arterial input function (AIF) for both clinical and research settings. Methods: Sixteen healthy participants underwent dynamic WB [18F]FDG scans using a continuous bed motion PET/CT system with simultaneous arterial blood sampling. Multiple processing pipelines that included automatic and manually generated IDIFs derived from the aorta and left ventricle, with and without motion correction were compared to the AIF. Subsequently generated quantitative images of glucose metabolism were compared to evaluate performance of the different input functions. Results: We observed moderate to high correlations between IDIFs and the AIF regarding area under the curve (r = 0.49-0.89) as well as for the cerebral metabolic rate of glucose (CMRGlu) (r = 0.68-0.95). Manual placing of IDIFs and motion correction further improved their similarity to the AIF. Discussion: In general, the automatic vendor protocol is a feasible approach for the quantification of CMRGlu for both, clinical and research settings where expertise or time is not available. However, we advise on a rigorous inspection of the placement of the volume of interest, the resulting IDIF, and the quantitative values to ensure valid interpretations. In protocols requiring longer scan times or where cohorts are prone to involuntary movement, manual IDIF definition with additional motion correction is recommended, as this has greater accuracy and reliability.

Simulation of a novel, non-invasive radiation detector to measure the arterial input function for dynamic positron emission tomography

BACKGROUND

Dynamic positron emission tomography (dPET) is a nuclear medicine imaging technique providing functional images for organs of interest with applications in oncology, cardiology, and drug discovery. This technique requires the acquisition of the time-course arterial plasma activity concentration, called the arterial input function (AIF), which is conventionally acquired via arterial blood sampling.

PURPOSE

The aim of this study was to (A) optimize the geometry for a novel and cost efficient non-invasive detector called NID designed to measure the AIF for dPET scans through Monte Carlo simulations and (B) develop a clinical data analysis chain to successfully separate the arterial component of a simulated AIF signal from the venous component.

METHODS

The NID was optimized by using an in-house Geant4-based software package. The sensitive volume of the NID consists of a band of 10 cm long and 1 mm in diameter scintillating fibers placed over a wrist phantom. The phantom was simulated as a cylinder, 10 cm long and 6.413 cm in diameter comprised of polyethylene with two holes placed through it to simulate the patient's radial artery and vein. This phantom design was chosen to match the wrist phantom used in our previous proof of concept work. Two geometries were simulated with different arrangements of scintillating fibers. The first design used a single layer of 64 fibers. The second used two layers, an inner layer with 29 fibers and an outer layer with 30 fibers. Four positron emitting radioisotopes were simulated: ¹⁸ F, ¹¹ C, ¹⁵ O, and ⁶⁸ Ga with 100 million simulated decay events per run. The total and intrinsic efficiencies of both designs were calculated as well as the full width half maximum (FWHM) of the signal. In addition, contribution by the annihilation photons versus positrons to the signal was investigated. The results obtained from the two simulated detector models were compared. A clinical data analysis chain using an expectation maximization maximum likelihood algorithm was tested. This analysis chain will be used to separate arterial counts from the total signal.

RESULTS

The second NID design with two layers of scintillating fibers had a higher efficiency for all simulations with a maximum increase of 17% total efficiency for ¹¹ C simulation. All simulations had a significant annihilation photon contribution. The signal for ¹⁸ F and ¹¹ C was almost entirely due to photons. The clinical data analysis chain was within 1% of the true value for 434 out of 440 trials. Further experimental studies to validate these simulations will be required.

CONCLUSIONS

The design of the NID was optimized and its efficiency increased through Monte Carlo simulations. A clinical data analysis chain was successfully developed to separate the arterial component of an AIF signal from the venous component. The simulations show that the NID can be used to accurately measure the AIF non-invasively for dPET scans.

Patient-Adaptive Population-Based Modeling of Arterial Input Functions

Kinetic modeling of dynamic PET data requires knowledge of tracer concentration in blood plasma, described by the arterial input function (AIF). Arterial blood sampling is the gold standard for AIF measurement, but is invasive and labour intensive. A number of methods have been proposed to accurately estimate the AIF directly from blood sampling and/or imaging data. Here we consider fitting a patient-adaptive mixture of historical population time course profiles to estimate individual AIFs. Travel time of a tracer atom from the injection site to the right ventricle of the heart is modeled as a realization from a Gamma distribution, and the time this atom spends in circulation before being sampled is represented by a subject-specific linear mixture of population profiles. These functions are estimated from independent population data. Individual AIFs are obtained by projection onto this basis of population profile components. The model incorporates knowledge of injection duration into the fit, allowing for varying injection protocols. Analyses of arterial sampling data from 18F-FDG, 15O-H2O and 18F-FLT clinical studies show that the proposed model can outperform reference techniques. The statistically significant gain achieved by using population data to train the basis components, instead of fitting these from the single individual sampling data, is measured on the FDG cohort. Kinetic analyses of simulated data demonstrate the reliability and potential benefit of this approach in estimating physiological parameters. These results are further supported by numerical simulations that demonstrate convergence and stability of the proposed technique under varying training population sizes and noise levels.

cAMP-PKA cascade: An outdated topic for depression?

Depression is a common neuropsychiatric disorder characterized by persistent depressed mood and causes serious socioeconomic burden worldwide. Hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis, deficiency of monoamine transmitters, neuroinflammation and abnormalities of the gut flora are strongly associated with the onset of depression. The cyclic AMP (cAMP)/protein kinase A (PKA) cascade, a major cross-species cellular signaling pathway, is supposed as important player and regulator of depression onset by controlling synaptic plasticity, cytokinesis, transcriptional regulation and HPA axis. In the central nervous system, the cAMP-PKA cascade can dynamically shape neural circuits by enhancing synaptic plasticity, and affect K⁺ channels by phosphorylating Kir4.1, thereby regulating neuronal excitation. The reduction of cAMP-PKA cascade affects neuronal excitation as well as synaptic plasticity, ultimately leading to pathological outcome of depression, while activation of cAMP-PKA cascade would provide a rapid antidepressant effect. In this review, we proposed to reconsider the function of cAMP-PKA cascade, especially in the rapid antidepressant effect. Local activation or indirect activation of PKA through adjusting anchor proteins would provide new idea for acute treatment of depression.

Short 2-[18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer

Purpose: To test a short 2-[¹⁸F]Fluoro-2-deoxy-D-glucose (2-[¹⁸F]FDG) PET dynamic acquisition protocol to calculate K_i using regional Patlak graphical analysis in patients with non-small-cell lung cancer (NSCLC).

Methods: 24 patients with NSCLC who underwent standard dynamic 2-[¹⁸F]FDG acquisitions (60 min) were randomly divided into two groups. In group 1 (n = 10), a population-based image-derived input function (pIDIF) was built using a monoexponential trend (10–60 min), and a leave-one-out cross-validation (LOOCV) method was performed to validate the pIDIF model. In group 2 (n = 14), K_i was obtained by standard regional Patlak plot analysis using IDIF (0–60 min) and tissue response (10–60 min) curves from the volume of interests (VOIs) placed on descending thoracic aorta and tumor tissue, respectively. Moreover, with our method, the Patlak analysis was performed to obtain K_i,s using IDIF_Fitted curve obtained from PET counts (0–10 min) followed by monoexponential coefficients of pIDIF (10–60 min) and tissue response curve obtained from PET counts at 10 min and between 40 and 60 min, simulating two short dynamic acquisitions. Both IDIF and IDIF_Fitted curves were modeled to assume the value of 2-[¹⁸F]FDG plasma activity measured in the venous blood sampling performed at 45 min in each patient. Spearman's rank correlation, coefficient of determination, and Passing–Bablok regression were used for the comparison between K_i and K_i,s. Finally, K_i,s was obtained with our method in a separate group of patients (group 3, n = 8) that perform two short dynamic acquisitions.

Results: Population-based image-derived input function (10–60 min) was modeled with a monoexponential curve with the following fitted parameters obtained in group 1: a = 9.684, b = 16.410, and c = 0.068 min⁻¹. The LOOCV error was 0.4%. In patients of group 2, the mean values of K_i and K_i,s were 0.0442 ± 0.0302 and 0.33 ± 0.0298, respectively (R² = 0.9970). The Passing–Bablok regression for comparison between K_i and K_i,s showed a slope of 0.992 (95% CI: 0.94–1.06) and intercept value of −0.0003 (95% CI: −0.0033–0.0011).

Conclusions: Despite several practical limitations, like the need to position the patient twice and to perform two CT scans, our method contemplates two short 2-[¹⁸F]FDG dynamic acquisitions, a population-based input function model, and a late venous blood sample to obtain robust and personalized input function and tissue response curves and to provide reliable regional K_i estimation.

Kinetic modeling and parameter estimation of TSPO PET imaging in the human brain

PURPOSE

Translocator protein 18-kDa (TSPO) imaging with positron emission tomography (PET) is widely used in research studies of brain diseases that have a neuro-immune component. Quantification of TSPO PET images, however, is associated with several challenges, such as the lack of a reference region, a genetic polymorphism affecting the affinity of the ligand for TSPO, and a strong TSPO signal in the endothelium of the brain vessels. These challenges have created an ongoing debate in the field about which type of quantification is most useful and whether there is an appropriate simplified model.

METHODS

This review focuses on the quantification of TSPO radioligands in the human brain. The various methods of quantification are summarized, including the gold standard of compartmental modeling with metabolite-corrected input function as well as various alternative models and non-invasive approaches. Their advantages and drawbacks are critically assessed.

RESULTS AND CONCLUSIONS

Researchers employing quantification methods for TSPO should understand the advantages and limitations associated with each method. Suggestions are given to help researchers choose between these viable alternative methods.

Supervised clustering for TSPO PET imaging

PURPOSE

This technical note seeks to act as a practical guide for implementing a supervised clustering algorithm (SVCA) reference region approach and to explain the main strengths and limitations of the technique in the context of 18-kilodalton translocator protein (TSPO) positron emission tomography (PET) studies in experimental medicine.

BACKGROUND

TSPO PET is the most widely used imaging technique for studying neuroinflammation in vivo in humans. Quantifying neuroinflammation with PET can be a challenging and invasive procedure, especially in frail patients, because it often requires blood sampling from an arterial catheter. A widely used alternative to arterial sampling is SVCA, which identifies the voxels with minimal specific binding in the PET images, thus extracting a pseudo-reference region for non-invasive quantification. Unlike other reference region approaches, SVCA does not require specification of an anatomical reference region a priori, which alleviates the limitation of TSPO contamination in anatomically-defined reference regions in individuals with underlying inflammatory processes. Furthermore, SVCA can be applied to any TSPO PET tracer across different neurological and neuropsychiatric conditions, providing noninvasivequantification of TSPO expression.

METHODS

We provide an overview of the development of SVCA as well as step-by-step instructions for implementing SVCA with suggestions for specific settings. We review the literature on SVCAapplications using first- and second- generation TSPO PET tracers and discuss potential clinically relevant limitations and applications.

CONCLUSIONS

The correct implementation of SVCA can provide robust and reproducible estimates of brain TSPO expression. This review encourages the standardisation of SVCA methodology in TSPO PET analysis, ultimately aiming to improve replicability and comparability across study sites.

Comparison of Invasive and Non-invasive Estimation of [11C]PBR28 Binding in Non-human Primates

PURPOSE

To identify a reliable alternative to the full blood [¹¹C]PBR28 quantification method that would be easily replicated in multiple research and clinical settings.

PROCEDURES

Ten [¹¹C]PBR28 scans were acquired from 7 healthy non-human primates (NHP). Arterial input functions (AIFs) were averaged to create a population template input function (TIF). Population-based input functions were created by scaling the TIF with injected activity per body weight (PBIF) or unmetabolized tracer activity in blood at 15-,30-, and 60-min post-injection (PBIF15, PBIF30, and PBIF60). Two additional input functions were used: the native unmetabolized total plasma activity (Totals) and the Totals curve metabolite corrected by a scaled template parent fraction from a 30-min sample (TPF30-IF). Total distribution volumes (V_Ts) were calculated using PBIF, PBIF30, PBIF15, PBIF60, Totals, TPF30-IF, and the individual AIF (V_T^AIF). Distribution volume ratios (DVR) were computed using the cerebellum and the centrum semiovale (CSO), as pseudo-reference regions (DVR^Cereb, DVR^CSO). Results obtained with each method were compared to V_T^AIF. Applicability of these alternative methods was tested on an independent pharmacological challenge dataset of microglial activation and depletion. Evaluation was carried at baseline, immediately after intervention (acute), and weeks post-intervention (post-recovery).

RESULTS

V_Ts computed using PBIF15 and PBIF30 showed the best correlation to V_T^AIF (r > 0.90), while V_T derived from the blood-free-scaled PBIF showed poor correlation (r = 0.46) and DVR^CSO correlated the least (r = 0.26). In the pharmacological challenge study, most population-derived V_T values were comparable to V_T^AIF at baseline and showed varied sensitivity to challenges at acute and post-recovery evaluation. DVR values did not detect relevant changes.

CONCLUSIONS

Population-based input functions scaled with a single blood sample might be a useful alternative to using AIF to compute [¹¹C]PBR28 binding in healthy NHPs or animals with comparable metabolism and overall perform better than pseudo-reference regions approaches.

Population-based input function for TSPO quantification and kinetic modeling with [11C]-DPA-713

INTRODUCTION

Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [11C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF.

MATERIALS AND METHODS

Eighteen subjects including six healthy volunteers (HV) and twelve Parkinson disease (PD) subjects from two [11C]-DPA-713 PET studies were included. Each subject underwent 90 min of dynamic PET imaging. Five healthy volunteers underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient-specific AIF (PSAIF, ground-truth standard) and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects and after normalizing the PSAIFs by 3 techniques: (a) Weightsubject×DoseInjected, (b) area under AIF curve (AUC), and (c) Weightsubject×AUC. The variability in the VT measured with PSAIF, in the test-retest study, was determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus, and amygdala) using the Bland-Altman analysis and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed.

RESULTS

Bland-Altman analysis showed systematic bias between test and retest studies. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and ± 70%. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, a significant difference between the results generated by the three normalization techniques existed for all brain structures except for the brainstem (P-value = 0.095). The mean % difference and 95% LOA is -10% and ±45% for Weightsubject×DoseInjected; +8% and ±50% for AUC; and +2% and ± 38% for Weightsubject×AUC. In all cases, normalizing by Weightsubject×AUC yielded the smallest % bias and variability (% bias = ±2%; LOA = ±38% for all brain regions). Estimating the reproducibility of PBIF-kinetics to PSAIF based on disease groups (HV/PD) and genotype (MAB/HAB), the average VT values for all regions obtained from PBIF is insignificantly higher than PSAIF (%difference = 4.53%, P-value = 0.73 for HAB; and %difference = 0.73%, P-value = 0.96 for MAB). PBIF also tends to overestimate the difference between PD and HV for HAB (% difference = 32.33% versus 13.28%) and underestimate it in MAB (%difference = 6.84% versus 20.92%).

CONCLUSIONS

PSAIF kinetic results are reproducible with PBIF, with variability in VT within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible and can be an alternative to PSAIF.

Quantification of [18F]UCB-H Binding in the Rat Brain: From Kinetic Modelling to Standardised Uptake Value

PURPOSE

[¹⁸F]UCB-H is a specific positron emission tomography (PET) biomarker for the Synaptic Vesicle protein 2A (SV2A), the binding site of the antiepileptic drug levetiracetam. With a view to optimising acquisition time and simplifying data analysis with this radiotracer, we compared two parameters: the distribution volume (Vt) obtained from Logan graphical analysis using a Population-Based Input Function, and the Standardised Uptake Value (SUV).

PROCEDURES

Twelve Sprague Dawley male rats, pre-treated with three different doses of levetiracetam were employed to develop the methodology. Three additional kainic acid (KA) treated rats (temporal lobe epilepsy model) were also used to test the procedure. Image analyses focused on: (i) length of the dynamic acquisition (90 versus 60 min); (ii) correlations between Vt and SUV over 20-min consecutive time-frames; (iii) and (iv) evaluation of differences between groups using the Vt and the SUV; and (v) preliminary evaluation of the methodology in the KA epilepsy model.

RESULTS

A large correlation between the Vt issued from 60 to 90-min acquisitions was observed. Further analyses highlighted a large correlation (r > 0.8) between the Vt and the SUV. Equivalent differences between groups were detected for both parameters, especially in the 20-40 and 40-60-min time-frames. The same results were also obtained with the epilepsy model.

CONCLUSIONS

Our results enable the acquisition setting to be changed from a 90-min dynamic to a 20-min static PET acquisition. According to a better image quality, the 20-40-min time-frame appears optimal. Due to its equivalence to the Vt, the SUV parameter can be considered in order to quantify [¹⁸F]UCB-H uptake in the rat brain. This work, therefore, establishes a starting point for the simplification of SV2A in vivo quantification with [¹⁸F]UCB-H, and represents a step forward to the clinical application of this PET radiotracer.

Simulation study of a coincidence detection system for non-invasive determination of arterial blood time-activity curve measurements

Background

Arterial sampling in PET studies for the purposes of kinetic modeling remains an invasive, time-intensive, and expensive procedure. Alternatives to derive the blood time-activity curve (BTAC) non-invasively are either reliant on large vessels in the field of view or are laborious to implement and analyze as well as being prone to many processing errors. An alternative method is proposed in this work by the simulation of a non-invasive coincidence detection unit.

Results

We utilized GATE simulations of a human forearm phantom with a blood flow model, as well as a model for dynamic radioactive bolus activity concentration based on clinical measurements. A fixed configuration of 14 and, also separately, 8 detectors were employed around the phantom, and simulations were performed to investigate signal detection parameters. Bismuth germanate (BGO) crystals proved to show the highest count rate capability and sensitivity to a simulated BTAC with a maximum coincidence rate of 575 cps. Repeatable location of the blood vessels in the forearm allowed a half-ring design with only 8 detectors. Using this configuration, maximum coincident rates of 250 cps and 42 cps were achieved with simulation of activity concentration determined from ¹⁵O and ¹⁸F arterial blood sampling. NECR simulated in a water phantom at 3 different vertical positions inside the 8-detector system (Y = − 1 cm, Y = − 2 cm, and Y = −3 cm) was 8360 cps, 13,041 cps, and 20,476 cps at an activity of 3.5 MBq. Addition of extra axial detection rings to the half-ring configuration provided increases in system sensitivity by a factor of approximately 10.

Conclusions

Initial simulations demonstrated that the configuration of a single half-ring 8 detector of monolithic BGO crystals could describe the simulated BTAC in a clinically relevant forearm phantom with good signal properties, and an increased number of axial detection rings can provide increased sensitivity of the system. The system would find use in the derivation of the BTAC for use in the application of kinetic models without physical arterial sampling or reliance on image-based techniques.

Reliable quantification of 18F-GE-180 PET neuroinflammation studies using an individually scaled population-based input function or late tissue-to-blood ratio

Purpose

Tracer kinetic modeling of tissue time activity curves and the individual input function based on arterial blood sampling and metabolite correction is the gold standard for quantitative characterization of microglia activation by PET with the translocator protein (TSPO) ligand ¹⁸F-GE-180. This study tested simplified methods for quantification of ¹⁸F-GE-180 PET.

Methods

Dynamic ¹⁸F-GE-180 PET with arterial blood sampling and metabolite correction was performed in five healthy volunteers and 20 liver-transplanted patients. Population-based input function templates were generated by averaging individual input functions normalized to the total area under the input function using a leave-one-out approach. Individual population-based input functions were obtained by scaling the input function template with the individual parent activity concentration of ¹⁸F-GE-180 in arterial plasma in a blood sample drawn at 27.5 min or by the individual administered tracer activity, respectively. The total ¹⁸F-GE-180 distribution volume (V_T) was estimated in 12 regions-of-interest (ROIs) by the invasive Logan plot using the measured or the population-based input functions. Late ROI-to-whole-blood and ROI-to-cerebellum ratio were also computed.

Results

Correlation with the reference V_T (with individually measured input function) was very high for V_T with the population-based input function scaled with the blood sample and for the ROI-to-whole-blood ratio (Pearson correlation coefficient = 0.989 ± 0.006 and 0.970 ± 0.005). The correlation was only moderate for V_T with the population-based input function scaled with tracer activity dose and for the ROI-to-cerebellum ratio (0.653 ± 0.074 and 0.384 ± 0.177). Reference V_T, population-based V_T with scaling by the blood sample, and ROI-to-whole-blood ratio were sensitive to the TSPO gene polymorphism. Population-based V_T with scaling to the administered tracer activity and the ROI-to-cerebellum ratio failed to detect a polymorphism effect.

Conclusion

These results support the use of a population-based input function scaled with a single blood sample or the ROI-to-whole-blood ratio at a late time point for simplified quantitative analysis of ¹⁸F-GE-180 PET.

Electronic supplementary material

The online version of this article (10.1007/s00259-020-04810-1) contains supplementary material, which is available to authorized users.

Substitution of venous for arterial blood sampling in the determination of regional rates of cerebral protein synthesis with L-[1-11C]leucine PET: A validation study

We developed and validated a method to estimate input functions for determination of regional rates of cerebral protein synthesis (rCPS) with L-[1-¹¹C]leucine PET without arterial sampling. The method is based on a population-derived input function (PDIF) approach, with venous samples for calibration. Population input functions were constructed from arterial blood data measured in 25 healthy 18-24-year-old males who underwent L-[1-¹¹C]leucine PET scans while awake. To validate the approach, three additional groups of 18-27-year-old males underwent L-[1-¹¹C]leucine PET scans with both arterial and venous blood sampling: 13 awake healthy volunteers, 10 sedated healthy volunteers, and 5 sedated subjects with fragile X syndrome. Rate constants of the L-[1-¹¹C]leucine kinetic model were estimated voxel-wise with measured arterial input functions and with venous-calibrated PDIFs. Venous plasma leucine measurements were used with venous-calibrated PDIFs for rCPS computation. rCPS determined with PDIFs calibrated with 30-60 min venous samples had small errors (RMSE: 4-9%), and no statistically significant differences were found in any group when compared to rCPS determined with arterial input functions. We conclude that in young adult males, PDIFs calibrated with 30-60 min venous samples can be used in place of arterial input functions for determination of rCPS with L-[1-¹¹C]leucine PET.

Noninvasive PK11195-PET Image Analysis Techniques Can Detect Abnormal Cerebral Microglial Activation in Parkinson's Disease

BACKGROUND AND PURPOSE

Neuroinflammation has been implicated in the pathophysiology of Parkinson's disease (PD), which might be influenced by successful neuroprotective drugs. The uptake of [11 C](R)-PK11195 (PK) is often considered to be a proxy for neuroinflammation, and can be quantified using the Logan graphical method with an image-derived blood input function, or the Logan reference tissue model using automated reference region extraction. The purposes of this study were (1) to assess whether these noninvasive image analysis methods can discriminate between patients with PD and healthy volunteers (HVs), and (2) to establish the effect size that would be required to distinguish true drug-induced changes from system variance in longitudinal trials.

METHODS

The sample consisted of 20 participants with PD and 19 HVs. Two independent teams analyzed the data to compare the volume of distribution calculated using image-derived input functions (IDIFs), and binding potentials calculated using the Logan reference region model.

RESULTS

With all methods, the higher signal-to-background in patients resulted in lower variability and better repeatability than in controls. We were able to use noninvasive techniques showing significantly increased uptake of PK in multiple brain regions of participants with PD compared to HVs.

CONCLUSION

Although not necessarily reflecting absolute values, these noninvasive image analysis methods can discriminate between PD patients and HVs. We see a difference of 24% in the substantia nigra between PD and HV with a repeatability coefficient of 13%, showing that it will be possible to estimate responses in longitudinal, within subject trials of novel neuroprotective drugs.

Dynamic PET of human liver inflammation: impact of kinetic modeling with optimization-derived dual-blood input function

The hallmark of nonalcoholic steatohepatitis is hepatocellular inflammation and injury in the setting of hepatic steatosis. Recent work has indicated that dynamic 18F-FDG PET with kinetic modeling has the potential to assess hepatic inflammation noninvasively, while static FDG-PET is less promising. Because the liver has dual blood supplies, kinetic modeling of dynamic liver PET data is challenging in human studies. This paper aims to identify the optimal dual-input kinetic modeling approach for dynamic FDG-PET of human liver inflammation. Fourteen patients with nonalcoholic fatty liver disease were included. Each patient underwent 1 h dynamic FDG-PET/CT scan and had liver biopsy within six weeks. Three models were tested for kinetic analysis: the traditional two-tissue compartmental model with an image-derived single-blood input function (SBIF), a model with population-based dual-blood input function (DBIF), and a new model with optimization-derived DBIF through a joint estimation framework. The three models were compared using Akaike information criterion (AIC), F test and histopathologic inflammation score. Results showed that the optimization-derived DBIF model improved liver time activity curve fitting and achieved lower AIC values and higher F values than the SBIF and population-based DBIF models in all patients. The optimization-derived model significantly increased FDG K1 estimates by 101% and 27% as compared with traditional SBIF and population-based DBIF. K1 by the optimization-derived model was significantly associated with histopathologic grades of liver inflammation while the other two models did not provide a statistical significance. In conclusion, modeling of DBIF is critical for dynamic liver FDG-PET kinetic analysis in human studies. The optimization-derived DBIF model is more appropriate than SBIF and population-based DBIF for dynamic FDG-PET of liver inflammation.

Pharmacokinetic Characterization of [18F]UCB-H PET Radiopharmaceutical in the Rat Brain

The synaptic vesicle glycoprotein 2A (SV2A), a protein essential to the proper nervous system function, is found in presynaptic vesicles. Thus, SV2A targeting, using dedicated radiotracers combined with positron emission tomography (PET), allows the assessment of synaptic density in the living brain. The first-in-class fluorinated SV2A specific radioligand, [¹⁸F]UCB-H, is now available at high activity through an efficient radiosynthesis compliant with current good manufacturing practices (cGMP). We report here a noninvasive method to quantify [¹⁸F]UCB-H binding in rat brain with microPET. Validation study in rats confirmed the need of high enantiomeric purity to target SV2A in vivo. We demonstrated the reliability of a population-based input function to quantify SV2A in preclinical microPET setting. Finally, we investigated the in vivo metabolism of [¹⁸F]UCB-H and confirmed the negligible amount of radiometabolites in the rat brain. Hence, the in vivo quantification of SV2A using [¹⁸F]UCB-H microPET seems a promising tool for the assessment of the synaptic density in the rat brain, and opens the way for longitudinal follow-up in neurodegenerative disease rodent models.

Feasibility study of TSPO quantification with [18F]FEPPA using population-based input function

PURPOSE

The input function (IF) is a core element in the quantification of Translocator protein 18 kDa with positron emission tomography (PET), as no suitable reference region with negligible binding has been identified. Arterial blood sampling is indeed needed to create the IF (ASIF). In the present manuscript we study individualization of a population based input function (PBIF) with a single arterial manual sample to estimate total distribution volume (VT) for [18F]FEPPA and to replicate previously published clinical studies in which the ASIF was used.

METHODS

The data of 3 previous [18F]FEPPA studies (39 of healthy controls (HC), 16 patients with Parkinson's disease (PD) and 18 with Alzheimer's disease (AD)) was reanalyzed with the new approach. PBIF was used with the Logan graphical analysis (GA) neglecting the vascular contribution to estimate VT. Time of linearization of the GA was determined with the maximum error criteria. The optimal calibration of the PBIF was determined based on the area under the curve (AUC) of the IF and the agreement range of VT between methods. The shape of the IF between groups was studied while taking into account genotyping of the polymorphism (rs6971).

RESULTS

PBIF scaled with a single value of activity due to unmetabolized radioligand in arterial plasma, calculated as the average of a sample taken at 60 min and a sample taken at 90 min post-injection, yielded a good interval of agreement between methods and optimized the area under the curve of IF. In HC, gray matter VTs estimated by PBIF highly correlated with those using the standard method (r2 = 0.82, p = 0.0001). Bland-Altman plots revealed PBIF slightly underestimates (~1 mL/cm3) VT calculated by ASIF (including a vascular contribution). It was verified that the AUC of the ASIF were independent of genotype and disease (HC, PD, and AD). Previous clinical results were replicated using PBIF but with lower statistical power.

CONCLUSION

A single arterial blood sample taken 75 minute post-injection contains enough information to individualize the IF in the groups of subjects studied; however, the higher variability produced requires an increase in sample size to reach the same effect size.

Standardized Input Function for 18F-FDG PET Studies in Mice: A Cautionary Study

Aim of the Study

The aim of this study was to assess the accuracy of a standardized arterial input function (SAIF) for positron emission tomography ¹⁸F-FDG studies in mice. In particular, we tested whether the same SAIF could be applied to populations of mice whose fasting conditions differed.

Methods

The SAIF was first created from a population of fasting mice (n = 11) and validated within this group using a correlation analysis and a leave-one-out procedure. Then, the SAIF was prospectively applied to a population of non-fasting mice (n = 16). The SAIFs were scaled using a single individual blood sample taken 25 min after injection. The metabolic rates of glucose (CMRglc) calculated with the SAIFs were compared with the reference values obtained by full arterial sampling (AIF).

Results

In both populations of mice, CMRglc values showed a very small bias but an important variability. The SAIF/AIF CMRglc ratio in the fasting mice was 0.97 ± 0.22 (after excluding a major outlier). The SAIF/AIF CMRglc ratio in the non-fasting mice was 1.04 ± 0.22. This variability was due to the presence of cases in which the SAIF poorly estimated the shape of the input function based on full arterial sampling.

Conclusion

Although SAIF allows the estimation of the ¹⁸F-FDG mice input function with negligible bias and independently from the fasting state, errors in individual mice (as high as 30–50%) cause an important variability. Alternative techniques, such as image-derived input function, might be a better option for mice PET studies.

Considerations in the Development of Reversibly Binding PET Radioligands for Brain Imaging

The development of reversibly binding radioligands for imaging brain proteins in vivo, such as enzymes, neurotransmitter transporters, receptors and ion channels, with positron emission tomography (PET) is keenly sought for biomedical studies of neuropsychiatric disorders and for drug discovery and development, but is recognized as being highly challenging at the medicinal chemistry level. This article aims to compile and discuss the main considerations to be taken into account by chemists embarking on programs of radioligand development for PET imaging of brain protein targets.

Characterization of brain mGluR5 binding in a pilot study of late-life major depressive disorder using positron emission tomography and [¹¹C]ABP688

The metabotropic glutamate receptor subtype 5 (mGluR5) has been implicated in the pathophysiology of mood and anxiety disorders and is a potential treatment target in major depressive disorder (MDD). This study compared brain mGluR5 binding in elderly patients suffering from MDD with that in elderly healthy volunteers using positron emission tomography (PET) and [(11)C]ABP688. Twenty elderly (mean age: 63.0 ± 6.3) subjects with MDD and twenty-two healthy volunteers in the same age range (mean age: 66.4 ± 7.3) were examined with PET after a single bolus injection of [(11)C]ABP688, with many receiving arterial sampling. PET images were analyzed on a region of interest and a voxel level to compare mGluR5 binding in the brain between the two groups. Differences in [(11)C]ABP688 binding between patients with early- and late-onset depression were also assessed. In contrast to a previously published report in a younger cohort, no significant difference in [(11)C]ABP688 binding was observed between elderly subjects with MDD and healthy volunteers. [(11)C]ABP688 binding was also similar between subgroups with early- or late-onset depression. We believe this is the first study to examine mGluR5 expression in depression in the elderly. Although future work is required, results suggest potential differences in the pathophysiology of elderly depression versus depression earlier in life.

Image-derived input function with factor analysis and a-priori information

BACKGROUND

Quantitative PET studies often require the cumbersome and invasive procedure of arterial cannulation to measure the input function. This study sought to minimize the number of necessary blood samples by developing a factor-analysis-based image-derived input function (IDIF) methodology for dynamic PET brain studies.

MATERIALS AND METHODS

IDIF estimation was performed as follows: (a) carotid and background regions were segmented manually on an early PET time frame; (b) blood-weighted and tissue-weighted time-activity curves (TACs) were extracted with factor analysis; (c) factor analysis results were denoised and scaled using the voxels with the highest blood signal; (d) using population data and one blood sample at 40 min, whole-blood TAC was estimated from postprocessed factor analysis results; and (e) the parent concentration was finally estimated by correcting the whole-blood curve with measured radiometabolite concentrations. The methodology was tested using data from 10 healthy individuals imaged with [(11)C](R)-rolipram. The accuracy of IDIFs was assessed against full arterial sampling by comparing the area under the curve of the input functions and by calculating the total distribution volume (VT).

RESULTS

The shape of the image-derived whole-blood TAC matched the reference arterial curves well, and the whole-blood area under the curves were accurately estimated (mean error 1.0±4.3%). The relative Logan-V(T) error was -4.1±6.4%. Compartmental modeling and spectral analysis gave less accurate V(T) results compared with Logan.

CONCLUSION

A factor-analysis-based IDIF for [(11)C](R)-rolipram brain PET studies that relies on a single blood sample and population data can be used for accurate quantification of Logan-V(T) values.

Differential effects of antidepressants escitalopram versus lithium on Gs alpha membrane relocalization

Background

Plasma membrane localization can play a significant role in the ultimate function of certain proteins. Specific membrane domains like lipid rafts have been shown to be inhibitory domains to a number of signaling proteins, including Gsα, and chronic antidepressant treatment facilitates Gs signaling by removing Gsα form lipid rafts. The intent of this study is to compare the effects of the selective serotnin reuptake inhibitor, escitalopram, with that of the mood stabilizing drug, lithium.

Results

There are a number of mechanisms of action proposed for lithium as a mood stabilizing agent, but the interactions between G proteins (particularly Gs) and mood stabilizing drugs are not well explored. Of particular interest was the possibility that there was some effect of mood stabilizers on the association between Gsα and cholesterol-rich membrane microdomains (lipid rafts), similar to that seen with long-term antidepressant treatment. This was examined by biochemical and imaging (fluorescence recovery after photobleaching: FRAP) approaches. Results indicate that escitalopram was effective at liberating Gsα from lipid rafts while lithium was not.

Conclusions

There are a number of drug treatments for mood disorders and yet there is no unifying hypothesis for a cellular or molecular basis of action. It is evident that there may in fact not be a single mechanism, but rather a number of different mechanisms that converge at a common point. The results of this study indicate that the mood stabilizing agent, lithium, and the selective serotonin reuptake inhibitor, escitalopram, act on their cellular targets through mutually exclusive pathways. These results also validate the hypothesis that translocation of Gsα from lipid rafts could serve as a biosignature for antidepressant action.

Imaging of monoamine oxidase-A in the human brain with [11C]befloxatone: quantification strategies and correlation with mRNA transcription maps

INTRODUCTION

[C]Befloxatone is a highly specific, reversible, and selective radioligand for brain PET imaging of monoamine oxidase-A and can be quantified by a two-tissue compartment model (2TCM) and an arterial input function. The aims of the present study were the following: (a) to assess whether in-vivo protein concentration, as measured by [C]befloxatone total distribution volume (VT), is correlated with post-mortem mRNA expression; (b) to replicate in a population of tobacco smokers the results of a recent study on healthy nonsmokers, which showed that spectral analysis (SA) provides a highly accurate estimation of [C]befloxatone-VT at the voxel level; and (c) to validate the use of an input function that would not require arterial sampling.

MATERIALS AND METHODS

Healthy male nonsmokers (n=7) and smokers (n=8) were imaged with PET and [C]befloxatone. Binding was quantified at the regional and voxel level with the Logan plot, multilinear analysis (MA1), and SA. VT values were compared with the reference values obtained by 2TCM at the regional level. [C]Befloxatone binding was compared with mRNA transcription maps from the Allen Human Brain Atlas. A less-invasive input function was obtained with a population-based input function (PBIF) scaled with arterialized venous samples.

RESULTS

mRNA expression was highly correlated with in-vivo 2TCM-VT values both for nonsmokers (R=0.873; P<0.0001) and for smokers (R=0.851; P<0.0001). At the regional level, both Logan and MA1 showed a moderate negative bias (-5 to -10%) compared with the reference VT values. With the exception of a single outlying individual, SA showed little bias and variability (+4.4±3.5%). Although variability was higher than at the regional level, SA provided the most accurate VT estimations at the voxel level: all but one participant had an error of less than 20%. Parametric Logan and MA1 analyses gave highly biased or unusable results. PBIF provided good results in all participants in whom the arterialization of venous blood was successful (all errors of about 10% or less).

CONCLUSION

[C]Befloxatone binding is strongly correlated with the values of mRNA transcription measured in post-mortem brains. At the voxel level, SA is the best available choice for [C]befloxatone quantification, although a higher variability must be expected. When an arterial input function is not technically feasible, a PBIF scaled with arterialized venous samples may provide an acceptable alternative, provided an optimal arterialization can be achieved.

Automated reference region extraction and population-based input function for brain [(11)C]TMSX PET image analyses

[(11)C]TMSX ([7-N-methyl-(11)C]-(E)-8-(3,4,5-trimethoxystyryl)-1,3,7-trimethylxanthine) is a selective adenosine A2A receptor (A2AR) radioligand. In the central nervous system (CNS), A2AR are linked to dopamine D2 receptor function in striatum, but they are also important modulators of inflammation. The golden standard for kinetic modeling of brain [(11)C]TMSX positron emission tomography (PET) is to obtain arterial input function via arterial blood sampling. However, this method is laborious, prone to errors and unpleasant for study subjects. The aim of this work was to evaluate alternative input function acquisition methods for brain [(11)C]TMSX PET imaging. First, a noninvasive, automated method for the extraction of gray matter reference region using supervised clustering (SCgm) was developed. Second, a method for obtaining a population-based arterial input function (PBIF) was implemented. These methods were created using data from 28 study subjects (7 healthy controls, 12 multiple sclerosis patients, and 9 patients with Parkinson's disease). The results with PBIF correlated well with original plasma input, and the SCgm yielded similar results compared with cerebellum as a reference region. The clustering method for extracting reference region and the population-based approach for acquiring input for dynamic [(11)C]TMSX brain PET image analyses appear to be feasible and robust methods, that can be applied in patients with CNS pathology.

Image-derived input function derived from a supervised clustering algorithm: methodology and validation in a clinical protocol using [11C](R)-rolipram

Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [¹¹C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter- and intra-operator variability. To overcome this limitation, we developed a fully automated technique for deriving IDIF with a supervised clustering algorithm (SVCA). To validate this technique, 25 healthy controls and 26 patients with moderate to severe major depressive disorder (MDD) underwent T1-weighted brain magnetic resonance imaging (MRI) and a 90-minute [¹¹C](R)-rolipram PET scan. For each subject, metabolite-corrected input function was measured from the radial artery. SVCA templates were obtained from 10 additional healthy subjects who underwent the same MRI and PET procedures. Cluster-IDIF was obtained as follows: 1) template mask images were created for carotid and surrounding tissue; 2) parametric image of weights for blood were created using SVCA; 3) mask images to the individual PET image were inversely normalized; 4) carotid and surrounding tissue time activity curves (TACs) were obtained from weighted and unweighted averages of each voxel activity in each mask, respectively; 5) partial volume effects and radiometabolites were corrected using individual arterial data at four points. Logan-distribution volume (V_T/f_P) values obtained by cluster-IDIF were similar to reference results obtained using arterial data, as well as those obtained using manual-IDIF; 39 of 51 subjects had a V_T/f_P error of <5%, and only one had error >10%. With automatic voxel selection, cluster-IDIF curves were less noisy than manual-IDIF and free of operator-related variability. Cluster-IDIF showed widespread decrease of about 20% [¹¹C](R)-rolipram binding in the MDD group. Taken together, the results suggest that cluster-IDIF is a good alternative to full arterial input function for estimating Logan-V_T/f_P in [¹¹C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.

Retest imaging of [11C]NOP-1A binding to nociceptin/orphanin FQ peptide (NOP) receptors in the brain of healthy humans

[(11)C]NOP-1A is a novel high-affinity PET ligand for imaging nociceptin/orphanin FQ peptide (NOP) receptors. Here, we report reproducibility and reliability measures of binding parameter estimates for [(11)C]NOP-1A binding in the brain of healthy humans. After intravenous injection of [(11)C]NOP-1A, PET scans were conducted twice on eleven healthy volunteers on the same (10/11 subjects) or different (1/11 subjects) days. Subjects underwent serial sampling of radial arterial blood to measure parent radioligand concentrations. Distribution volume (VT; a measure of receptor density) was determined by compartmental (one- and two-tissue) modeling in large regions and by simpler regression methods (graphical Logan and bilinear MA1) in both large regions and voxel data. Retest variability and intraclass correlation coefficient (ICC) of VT were determined as measures of reproducibility and reliability respectively. Regional [(11)C]NOP-1A uptake in the brain was high, with a peak radioactivity concentration of 4-7 SUV (standardized uptake value) and a rank order of putamen>cingulate cortex>cerebellum. Brain time-activity curves fitted well in 10 of 11 subjects by unconstrained two-tissue compartmental model. The retest variability of VT was moderately good across brain regions except cerebellum, and was similar across different modeling methods, averaging 12% for large regions and 14% for voxel-based methods. The retest reliability of VT was also moderately good in most brain regions, except thalamus and cerebellum, and was similar across different modeling methods averaging 0.46 for large regions and 0.48 for voxels having gray matter probability >20%. The lowest retest variability and highest retest reliability of VT were achieved by compartmental modeling for large regions, and by the parametric Logan method for voxel-based methods. Moderately good reproducibility and reliability measures of VT for [(11)C]NOP-1A make it a useful PET ligand for comparing NOP receptor binding between different subject groups or under different conditions in the same subject.

Voxelwise quantification of [(11)C](R)-rolipram PET data: a comparison between model-based and data-driven methods

This study compared model-based and data-driven methods to assess the best methodology for generating precise and accurate parametric maps of the parameters of interest in [(11)C](R)-rolipram brain positron-emission tomography studies. Parametric images were generated using (1) a two-tissue compartmental model (2TCM) solved with the hierarchical basis function method (H-BFM) linear estimator; (2) data-driven spectral-based methods: standard spectral analysis (std SA) and rank-shaping SA (RS); and (3) the Logan graphical plot. Nonphysiologic VT estimates were eliminated and the remaining ones were compared with the reference values, i.e., those obtained with a voxelwise 2TCM solved with a nonlinear estimator. With regard to voxelwise VT estimates, H-BFM showed the best agreement with weighted nonlinear least square (WNLLS) values and the lowest percentage of mean relative difference (1±1%). All methods showed comparable variability in the relative differences. H-BFM provided the best correlation with WNLLS (y=1.034x-0.013; R(2)=0.973). Despite a slight bias, the other three methods also showed good agreement and high correlation (R(2)>0.96). H-BFM yielded the most reliable voxelwise quantification of [(11)C](R)-rolipram as well as the complete description of the tracer kinetic. The Logan plot represents a valid alternative if only VT estimation is required. Its marginally higher bias was outweighed by a low computational time, ease of implementation, and robustness.

PET radiopharmaceuticals for probing enzymes in the brain

Biologically important processes in normal brain function and brain disease involve the action of various protein-based receptors, ion channels, transporters and enzymes. The ability to interrogate the location, abundance and activity of these entities in vivo using non-invasive molecular imaging can provide unprecedented information about the spatio-temporal dynamics of brain function. Indeed, positron emission tomography (PET) imaging is transforming our understanding of the central nervous system and brain disease. Great emphasis has historically been placed on developing radioligands for the non-invasive detection of neuroreceptors. In contrast, relatively few enzymes have been amenable to examination by PET imaging procedures based upon trapping or accumulation of enzymatic products, because only a subset of enzymes have sufficient catalytic rate to produce measureable accumulation within the practical time-limit of PET recordings. However, high affinity inhibitors are now serving as tracers for enzymes, particularly for measuring the abundance of enzymes mediating intracellular signal transduction in the brain, which offer a rich diversity of potential targets for drug discovery. The purpose of this review is to summarize well-known radiotracers for brain enzymes, and draw attention to recent developments in PET radiotracers for imaging signal transduction pathways in the brain. The review is organized by target class and focuses on structural chemistry of the best-established radiotracers identified in each class.

Population-based input function modeling for [(18)F]FMPEP-d 2, an inverse agonist radioligand for cannabinoid CB1 receptors: validation in clinical studies

Background

Population-based input function (PBIF) may be a valid alternative to full blood sampling for quantitative PET imaging. PBIF is typically validated by comparing its quantification results with those obtained via arterial sampling. However, for PBIF to be employed in actual clinical research studies, its ability to faithfully capture the whole spectrum of results must be assessed. The present study validated a PBIF for [¹⁸F]FMPEP-d₂, a cannabinoid CB₁ receptor radioligand, in healthy volunteers, and also attempted to utilize PBIF to replicate three previously published clinical studies in which the input function was acquired with arterial sampling.

Methods

The PBIF was first created and validated with data from 42 healthy volunteers. This PBIF was used to assess the retest variability of [¹⁸F]FMPEP-d₂, and then to quantify CB₁ receptors in alcoholic patients (n = 18) and chronic daily cannabis smokers (n = 29). Both groups were scanned at baseline and after 2–4 weeks of monitored drug abstinence.

Results

PBIF yielded accurate results in the 42 healthy subjects (average Logan-distribution volume (V_T) was 13.3±3.8 mL/cm³ for full sampling and 13.2±3.8 mL/cm³ for PBIF; R² = 0.8765, p<0.0001) and test-retest results were comparable to those obtained with full sampling (variability: 16%; intraclass correlation coefficient: 0.89). PBIF accurately replicated the alcoholism study, showing a widespread ∼20% reduction of CB₁ receptors in alcoholic subjects, without significant change after abstinence. However, a small PBIF-V_T bias of −9% was unexpectedly observed in cannabis smokers. This bias led to substantial errors, including a V_T decrease in regions that had shown no downregulation in the full input function. Simulated data showed that the original findings could only have been replicated with a PBIF bias between −6% and +4%.

Conclusions

Despite being initially well validated in healthy subjects, PBIF may misrepresent clinical protocol results and be a source of variability between different studies and institutions.