Arterial Input Function Research Articles

Transfer function analysis assesses resting cerebral perfusion metrics using hypoxia-induced deoxyhemoglobin as a contrast agent.

Introduction: Use of contrast in determining hemodynamic measures requires the deconvolution of an arterial input function (AIF) selected over a voxel in the middle cerebral artery to calculate voxel wise perfusion metrics. Transfer function analysis (TFA) offers an alternative analytic approach that does not require identifying an AIF. We hypothesised that TFA metrics Gain, Lag, and their ratio, Gain/Lag, correspond to conventional AIF resting perfusion metrics relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF), respectively. Methods: 24 healthy participants (17 M) and 1 patient with steno-occlusive disease were recruited. We used non-invasive transient hypoxia-induced deoxyhemoglobin as an MRI contrast. TFA and conventional AIF analyses were used to calculate averages of whole brain and smaller regions of interest. Results: Maps of these average metrics had colour scales adjusted to enhance contrast and identify areas of high congruence. Regional gray matter/white matter (GM/WM) ratios for MTT and Lag, rCBF and Gain/Lag, and rCBV and Gain were compared. The GM/WM ratios were greater for TFA metrics compared to those from AIF analysis indicating an improved regional discrimination. Discussion: Resting perfusion measures generated by The BOLD analysis resulting from a transient hypoxia induced variations in deoxyhemoglobin analyzed by TFA are congruent with those analyzed by conventional AIF analysis.

Delay of Aortic Arterial Input Function Time Improves Detection of Malignant Vertebral Body Lesions on Dynamic Contrast-Enhanced MRI Perfusion.

Dynamic contrast-enhanced MRI (DCE) is an emerging modality in the study of vertebral body malignancies. DCE-MRI analysis relies on a pharmacokinetic model, which assumes that contrast uptake is simultaneous in the feeding of arteries and tissues of interest. While true in the highly vascularized brain, the perfusion of the spine is delayed. This delay of contrast reaching vertebral body lesions can affect DCE-MRI analyses, leading to misdiagnosis for the presence of active malignancy in the bone marrow. To overcome the limitation of delayed contrast arrival to vertebral body lesions, we shifted the arterial input function (AIF) curve over a series of phases and recalculated the plasma volume values (Vp) for each phase shift. We hypothesized that shifting the AIF tracer curve would better reflect actual contrast perfusion, thereby improving the accuracy of Vp maps in metastases. We evaluated 18 biopsy-proven vertebral body metastases in which standard DCE-MRI analysis failed to demonstrate the expected increase in Vp. We manually delayed the AIF curve for multiple phases, defined as the scan-specific phase temporal resolution, and analyzed DCE-MRI parameters with the new AIF curves. All patients were found to require at least one phase-shift delay in the calculated AIF to better visualize metastatic spinal lesions and improve quantitation of Vp. Average normalized Vp values were 1.78 ± 1.88 for zero phase shifts (P0), 4.72 ± 4.31 for one phase shift (P1), and 5.59 ± 4.41 for two phase shifts (P2). Mann-Whitney U tests obtained p-values = 0.003 between P0 and P1, and 0.0004 between P0 and P2. This study demonstrates that image processing analysis for DCE-MRI in patients with spinal metastases requires a careful review of signal intensity curve, as well as a possible adjustment of the phase of aortic AIF to increase the accuracy of Vp.

Design of a generic method for single dual-tracer PET imaging acquisition in clinical routine

Using different tracers in positron emission tomography (PET) imaging can bring complementary information on tumor heterogeneities. Ideally, PET images of different tracers should be acquired simultaneously to avoid the bias induced by movement and physiological changes between sequential acquisitions. Previous studies have demonstrated the feasibility of recovering separated PET signals or parameters of two or more tracers injected (quasi-)simultaneously in a single acquisition. In this study, a generic framework in the context of dual-tracer PET acquisition is proposed where no strong kinetic assumptions nor specific tuning of parameters are required. The performances of the framework were assessed through simulations involving the combination of [18F]FCH and [18F]FDG injections, two protocols (90 and 60 min acquisition durations) and various activity ratios between the two injections. Preclinical experiments with the same radiotracers were also conducted. Results demonstrate the ability of the method both to extract separated arterial input functions (AIF) from noisy image-derived input function and to separate the dynamic signals and further estimate kinetic parameters. The compromise between bias and variance associated with the estimation of net influx rate K i shows that it is preferable to use the second injected radiotracer with twice the activity of the first for both 90 min [18F]FCH+[18F]FDG and 60 min [18F]FDG+[18F]FCH protocols. In these optimal settings, the weighted mean-squared-error of the estimated AIF was always less than 7%. The K i bias was similar to the one of single-tracer acquisitions; below 5%. Compared to single-tracer results, the variance of K i was twice more for 90 min dual-tracer scenario and four times more for the 60 min scenario. The generic design of the method makes it easy to use for other pairs of radiotracers and even for more than two tracers. The absence of strong kinetic assumptions and tuning parameters makes it suitable for a possible use in clinical routine.

The arterial input function: Spatial dependence within the imaging volume and its influence on 3D quantitative dynamic contrast-enhanced MRI for head and neck cancer

PurposeTo evaluate the dependence of the arterial input function (AIF) on the imaging z-axis and its effect on 3D DCE MRI pharmacokinetic parameters as mediated by the SPGR signal equation and Extended Tofts-Kermode model. TheoryFor SPGR-based 3D DCE MRI acquisition of the head and neck, inflow effects within vessels violate the assumptions underlying the SPGR signal model. Errors in the SPGR-based AIF estimate propagate through the Extended Tofts-Kermode model to affect the output pharmacokinetic parameters. Materials and methods3D DCE-MRI data were acquired for six newly diagnosed HNC patients in a prospective single arm cohort study. AIF were selected within the carotid arteries at each z-axis location. A region of interest (ROI) was placed in normal paravertebral muscle and the Extended Tofts-Kermode model solved for each pixel within the ROI for each AIF. Results were compared to those obtained with a published population average AIF. ResultsDue to inflow effect, the AIF showed extreme variation in their temporal shapes. Ktrans was most sensitive to the initial bolus concentration and showed more variation over the muscle ROI with AIF taken from the upstream portion of the carotid. kep was less sensitive to the peak bolus concentration and showed less variation for AIF taken from the upstream portion of the carotid. ConclusionInflow effects may introduce an unknown bias to SPGR-based 3D DCE pharmacokinetic parameters. Variation in the computed parameters depends on the selected AIF location. In the context of high flow, measurements may be limited to relative rather than absolute quantitative parameters.

PET AIF estimation when available ROI data is impacted by dispersive and/or background effects

Objective. Blood pool region of interest (ROI) data extracted from the field of view of a PET scanner can be impacted by both dispersive and background effects. This circumstance compromises the ability to correctly extract the arterial input function (AIF) signal. The paper explores a novel approach to addressing this difficulty. Approach. The method involves representing the AIF in terms of the whole-body impulse response (IR) to the injection profile. Analysis of a collection/population of directly sampled arterial data sets allows the statistical behaviour of the tracer’s impulse response to be evaluated. It is proposed that this information be used to develop a penalty term for construction of a data-adaptive method of regularisation estimator of the AIF when dispersive and/or background effects maybe impacting the blood pool ROI data. Main results. Computational efficiency of the approach derives from the linearity of the impulse response representation of the AIF and the ability to substantially rely on quadratic programming techniques for numerical implementation. Data from eight different tracers, used in PET cancer imaging studies, are considered. Sample image-based AIF extractions for brain studies with: 18F-labeled fluoro-deoxyglucose and fluoro-thymidine (FLT), 11C-labeled carbon dioxide (CO2) and 15O-labeled water (H2O) are presented. Results are compared to the true AIF based on direct arterial sampling. Formal numerical simulations are used to evaluate the performance of the AIF extraction method when the ROI data has varying amounts of contamination, in comparison to a direct approach that ignores such effects. It is found that even with quite small amounts of contamination, the mean squared error of the regularised AIF is significantly better than the error associated with direct use of the ROI data. Significance. The proposed IR-based AIF extraction scheme offers a practical methodological approach for situations where the available image ROI data may be contaminated by background and/or dispersion effects.

Pilot imaging of the colony stimulating factor 1 receptor in the brains of virally-suppressed individuals with HIV.

Neuroimmune activation is a putative driver of cognitive impairment in people with HIV (PWH), even in the age of modern antiretroviral therapy. Nevertheless, imaging of the microglial marker, the 18 kDa translocator protein (TSPO), with positron emission tomography (PET) in treated PWH has yielded inconclusive findings. One potential reason for the varied TSPO results is a lack of cell-type specificity of the TSPO target. [ 11 C]CPPC, 5-cyano- N -(4-(4-[ 11 C]methylpiperazin-1-yl)-2-(piperidin-1-yl)phenyl) furan-2-carboxaminde, is a radiotracer for use with PET to image the colony stimulating factor 1 receptor (CSF1R). The CSF1R is expressed on microglia and central nervous system macrophages, with little expression on other cell types. We used [ 11 C]CPPC PET in virally-suppressed- (VS)-PWH and HIV-uninfected individuals to estimate the effect sizes of higher CSF1R in the brains of VS-PWH. Sixteen VS-PWH and 15 HIV-uninfected individuals completed [ 11 C]CPPC PET. [ 11 C]CPPC binding (V T ) in nine regions was estimated using a one-tissue compartmental model with a metabolite-corrected arterial input function, and compared between groups. Regional [ 11 C]CPPC V T did not significantly differ between groups after age- and sex- adjustment [unstandardized beta coefficient ( B ) = 1.84, standard error (SE) = 1.18, P = 0.13]. The effect size was moderate [Cohen's d = 0.56, 95% confidence interval (CI) -0.16, 1.28), with strongest trend of higher V T in VS-PWH in striatum and parietal cortex (each P = 0.04; Cohen's d = 0.71 and 0.72, respectively). A group difference in [ 11 C]CPPC V T was not observed between VS-PWH and HIV-uninfected individuals in this pilot, although the observed effect sizes suggest the study was underpowered to detect regional group differences in binding.

Diagnostic value of dynamic 18F-FDG PET/CT imaging in non-small cell lung cancer and FDG hypermetabolic lymph nodes.

We aimed to investigate the diagnostic value of dynamic 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography-computed tomography in non-small cell lung cancer and fluoro-D-glucose hypermetabolic lymph nodes. Patients who made an active appointment for positron emission tomography-computed tomography were randomly enrolled by referring to previous imaging data and clinical information. Finally, 34 histopathologically confirmed non-small cell lung cancers (18 adenocarcinoma and 16 squamous cell carcinoma cases) were prospectively studied using dynamic and static 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography-computed tomography imaging (the diagnostic study has not yet been registered on a clinical trial platform). In dynamic positron emission tomography images, a volume of interest, defined by the thoracic aorta, was selected for estimating the arterial input function. Patlak and irreversible two-tissue compartment model analyses were performed based on the pixel points to obtain first-order characteristic kinetic parameters for each lesion and hypermetabolic lymph node. The first-order characteristic kinetic parameters were obtained based on the basic data of dynamic positron emission tomography images in the corresponding model and the lesion delineation of region-of-interest based on computed tomography images, such as V_Median (the median gray intensity of V), k3_Entropy, VB_Entropy, K1_Uniformity, and ki_Uniformity. The first-order characteristic kinetic parameters were also modeled by logistic regression for the differential diagnosis of non-small cell lung cancer and hypermetabolic lymph nodes. Maximum and mean standard uptake values (SUVmax and SUVmean, respectively) were obtained from static positron emission tomography images. The diagnostic efficacy of the parameters was evaluated using the receiver operating characteristic curve and the DeLong test. There was a significant difference in the V_Median values of adenocarcinoma and squamous cell carcinoma. The regression models for K1, k3, and V provided good predictions of adenocarcinoma and squamous cell carcinoma typology. Significant differences were observed in k3_Entropy, VB_Entropy, K1_Uniformity, and ki_Uniformity between benign and malignant lymph nodes. The regression model of Ki, VB, and k3 could make a good prediction for identifying benign and malignant lymph nodes. Dynamic 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography-computed tomography imaging showed high diagnostic value in the staging of non-small cell lung cancer and fluoro-D-glucose hypermetabolic lymph nodes, and can be of great use in non-small cell lung cancer lymph node staging and surgical decision-making.

Detecting CTP truncation artifacts in acute stroke imaging from the arterial input and the vascular output functions.

Current guidelines for CT perfusion (CTP) in acute stroke suggest acquiring scans with a minimal duration of 60-70 s. But even then, CTP analysis can be affected by truncation artifacts. Conversely, shorter acquisitions are still widely used in clinical practice and may, sometimes, be sufficient to reliably estimate lesion volumes. We aim to devise an automatic method that detects scans affected by truncation artifacts. Shorter scan durations are simulated from the ISLES'18 dataset by consecutively removing the last CTP time-point until reaching a 10 s duration. For each truncated series, perfusion lesion volumes are quantified and used to label the series as unreliable if the lesion volumes considerably deviate from the original untruncated ones. Afterwards, nine features from the arterial input function (AIF) and the vascular output function (VOF) are derived and used to fit machine-learning models with the goal of detecting unreliably truncated scans. Methods are compared against a baseline classifier solely based on the scan duration, which is the current clinical standard. The ROC-AUC, precision-recall AUC and the F1-score are measured in a 5-fold cross-validation setting. The best performing classifier obtained an ROC-AUC of 0.982, precision-recall AUC of 0.985 and F1-score of 0.938. The most important feature was the AIFcoverage, measured as the time difference between the scan duration and the AIF peak. When using the AIFcoverage to build a single feature classifier, an ROC-AUC of 0.981, precision-recall AUC of 0.984 and F1-score of 0.932 were obtained. In comparison, the baseline classifier obtained an ROC-AUC of 0.954, precision-recall AUC of 0.958 and F1-Score of 0.875. Machine learning models fed with AIF and VOF features accurately detected unreliable stroke lesion measurements due to insufficient acquisition duration. The AIFcoverage was the most predictive feature of truncation and identified unreliable short scans almost as good as machine learning. We conclude that AIF/VOF based classifiers are more accurate than the scans' duration for detecting truncation. These methods could be transferred to perfusion analysis software in order to increase the interpretability of CTP outputs.

Measurement of Cerebral Perfusion Indices from the Early Phase of [18F]MK6240 Dynamic Tau PET Imaging.

6-(fluoro-18F)-3-(1H-pyrrolo[2,3-c]pyridin-1-yl)isoquinolin-5-amine ([18F]MK6240) has high affinity and selectivity for hyperphosphorylated tau and readily crosses the blood-brain barrier. This study investigated whether the early phase of [18F]MK6240 can be used to provide a surrogate index of cerebral perfusion. Methods: Forty-nine subjects who were cognitively normal (CN), had mild cognitive impairment (MCI), or had Alzheimer's disease (AD) underwent paired dynamic [18F]MK6240 and [11C]Pittsburgh compound B (PiB) PET, as well as structural MRI to obtain anatomic information. Arterial blood samples were collected in a subset of 24 subjects for [18F]MK6240 scans to derive metabolite-corrected arterial input functions. Regional time-activity curves were extracted using atlases available in the Montreal Neurologic Institute template space and using FreeSurfer. The early phase of brain time-activity curves was analyzed using a 1-tissue-compartment model to obtain a robust estimate of the rate of transfer from plasma to brain tissue, K 1 (mL⋅cm-3⋅min-1), and the simplified reference tissue model 2 was investigated for noninvasive estimation of the relative delivery rate, R 1 (unitless). A head-to-head comparison with R 1 derived from [11C]PiB scans was performed. Grouped differences in R 1 were evaluated among CN, MCI, and AD subjects. Results: Regional K 1 values suggested a relatively high extraction fraction. R 1 estimated noninvasively from simplified reference tissue model 2 agreed well with R 1 calculated indirectly from the blood-based compartment modeling (r = 0.99; mean difference, 0.024 ± 0.027), suggesting that robust estimates were obtained. R 1 measurements obtained with [18F]MK6240 correlated strongly and overall agreed well with those obtained from [11C]PiB (r = 0.93; mean difference, -0.001 ± 0.068). Statistically significant differences were observed in regional R 1 measurements among CN, MCI, and AD subjects, notably in the temporal and parietal cortices. Conclusion: Our results provide evidence that the early phase of [18F]MK6240 images may be used to derive a useful index of cerebral perfusion. The early and late phases of a [18F]MK6240 dynamic acquisition may thus offer complementary information about the pathophysiologic mechanisms of the disease.

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.

Brain exposure of osimertinib in patients with epidermal growth factor receptor mutation non-small cell lung cancer and brain metastases: A positron emission tomography and magnetic resonance imaging study.

Brain metastases (BMs) are associated with poor prognosis in epidermal growth factor receptor mutation-positive (EGFRm) non-small cell lung cancer (NSCLC). Osimertinib is a third-generation, irreversible, EGFR-tyrosine kinase inhibitor that potently and selectively inhibits EGFR-sensitizing and T790M resistance mutations with efficacy in EGFRm NSCLC including central nervous system (CNS) metastases. The open-label phase I positron emission tomography (PET) and magnetic resonance imaging (MRI) study (ODIN-BM) assessed [11 C]osimertinib brain exposure and distribution in patients with EGFRm NSCLC and BMs. Three dynamic 90-min [11 C]osimertinib PET examinations were acquired together with metabolite-corrected arterial plasma input functions at: baseline, after first oral osimertinib 80 mg dose, and after greater than or equal to 21 days of osimertinib 80 mg q.d. treatment. Contrast-enhanced MRI was performed at screening and after 25-35 days of osimertinib 80 mg q.d.; treatment effect was assessed per CNS Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and per volumetric changes in total BM using a novel analysis approach. Four patients (aged 51-77 years) completed the study. At baseline, ~1.5% injected radioactivity reached the brain (IDmax[brain] ) 22 min (median, Tmax[brain] ) after injection. Total volume of distribution (VT ) in whole brain was numerically higher compared with the BM regions. After a single oral osimertinib 80 mg dose, there was no consistent decrease in VT in whole brain or BMs. After greater than or equal to 21 days' daily treatment, VT in whole brain and BMs were numerically higher versus baseline. MRI revealed 56%-95% reduction in total BMs volume after 25-35 days of osimertinib 80 mg q.d. treatment. The [11 C]osimertinib crossed the blood-brain and brain-tumor barriers and had a high, homogeneous brain distribution in patients with EGFRm NSCLC and BMs.

Fluid Mechanics Approach to Perfusion Quantification: Vasculature Computational Fluid Dynamics Simulation, Quantitative Transport Mapping (QTM) Analysis of Dynamics Contrast Enhanced MRI, and Application in Nonalcoholic Fatty Liver Disease Classification.

We quantify liver perfusion using quantitative transport mapping (QTM) method that is free of arterial input function (AIF). QTM method is validated in a vasculature computational fluid dynamics (CFD) simulation and is applied for processing dynamic contrast enhanced (DCE) MRI images in differentiating liver with nonalcoholic fatty liver disease (NAFLD) from healthy controls using pathology reference in a preclinical rabbit model. QTM method was validated on a liver perfusion simulation based on fluid dynamics using a rat liver vasculature model and the mass transport equation. In the NAFLD grading task, DCE MRI images of 7 adult rabbits with methionine choline-deficient diet-induced nonalcoholic steatohepatitis (NASH), 8 adult rabbits with simple steatosis (SS) were acquired and processed using QTM method and dual-input two compartment Kety's method respectively. Statistical analysis was performed on six perfusion parameters: velocity magnitude | u | derived from QTM, liver arterial blood flow LBFa, liver venous blood flow LBFv, permeability Ktrans, blood volume Vp and extravascular space volume Ve averaged in liver ROI. In the simulation, QTM method successfully reconstructed blood flow, reduced error by 48% compared to Kety's method. In the preclinical study, only QTM |u| showed significant difference between high grade NAFLD group and low grade NAFLD group. QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with Kety's method, QTM method showed higher accuracy and better differentiation in NAFLD classification task. We propose to apply QTM method in liver DCE MRI perfusion quantification.

Computed Tomography Perfusion Imaging in Acute Ischemic Stroke: Accurate Interpretation Matters.

Computed Tomography Perfusion Imaging in Acute Ischemic Stroke: Accurate Interpretation Matters.

A simplified method to correct saturation of arterial input function for cardiac magnetic resonance first-pass perfusion imaging: validation with simultaneously acquired PET

BackgroundFirst-pass perfusion imaging in magnetic resonance imaging (MRI) is an established method to measure myocardial blood flow (MBF). An obstacle for accurate quantification of MBF is the saturation of blood pool signal intensity used for arterial input function (AIF). The objective of this project was to validate a new simplified method for AIF estimation obtained from single-bolus and single sequence perfusion measurements. The reference MBF was measured simultaneously on 13N-ammonia positron emission tomography (PET). MethodsSixteen patients with clinically confirmed myocardial ischemia were imaged in a clinical whole-body PET-MRI system. PET perfusion imaging was performed in a 10-min acquisition after the injection of 10 mCi of 13N-ammonia. The MRI perfusion acquisition started simultaneously with the start of the PET acquisition after the injection of a 0.075 mmol/kg gadolinium contrast agent. Cardiac stress imaging was initiated after the administration of regadenoson 20 s prior to PET-MRI scanning. The saturation part of the MRI AIF data was modeled as a gamma variate curve, which was then estimated for a true AIF by minimizing a cost function according to various boundary conditions. A standard AHA 16-segment model was used for comparative analysis of absolute MBF from PET and MRI. ResultsOverall, there were 256 segments in 16 patients, mean resting perfusion for PET was 1.06 ± 0.34 ml/min/g and 1.04 ± 0.30 ml/min/g for MRI (P = 0.05), whereas mean stress perfusion for PET was 2.00 ± 0.74 ml/min/g and 2.12 ± 0.76 ml/min/g for MRI (P < 0.01). Linear regression analysis in MBF revealed strong correlation (r = 0.91, slope = 0.96, P < 0.001) between PET and MRI. Myocardial perfusion reserve, calculated from the ratio of stress MBF over resting MBF, also showed a strong correlation between MRI and PET measurements (r = 0.82, slope = 0.81, P < 0.001). ConclusionThe results demonstrated the feasibility of the simplified AIF estimation method for the accurate quantification of MBF by MRI with single sequence and single contrast injection. The MRI MBF correlated strongly with PET MBF obtained simultaneously. This post-processing technique will allow easy transformation of clinical perfusion imaging data into quantitative information.

Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images.

When no arterial input function is available, quantification of dynamic PET images requires a previous step devoted to the extraction of a reference time-activity curve (TAC). Factor analysis is often applied for this purpose. This paper introduces a novel approach that conducts a new kind of nonlinear factor analysis relying on a compartment model, and computes the kinetic parameters of specific binding tissues jointly. To this end, it capitalizes on data-driven parametric imaging methods to provide a physical description of the underlying PET data, directly relating the specific binding with the kinetics of the non-specific binding in the corresponding tissues. This characterization is introduced into the factor analysis formulation to yield a novel nonlinear unmixing model designed for PET image analysis. This model also explicitly introduces global kinetic parameters that allow for a direct estimation of a binding potential that represents the ratio at equilibrium of specifically bound radioligand to the concentration of nondisplaceable radioligand in each non-specific binding tissue. The performance of the method is evaluated on synthetic and real data to demonstrate its potential interest.

In Vivo Imaging and Kinetic Modeling of Novel Glycogen Synthase Kinase-3 Radiotracers [11C]OCM-44 and [18F]OCM-50 in Non-Human Primates.

Glycogen synthase kinase 3 (GSK-3) is a potential therapeutic target for a range of neurodegenerative and psychiatric disorders. The goal of this work was to evaluate two leading GSK-3 positron emission tomography (PET) radioligands, [11C]OCM-44 and [18F]OCM-50, in non-human primates to assess their potential for clinical translation. A total of nine PET scans were performed with the two radiotracers using arterial blood sampling in adult rhesus macaques. Brain regional time-activity curves were extracted and fitted with one- and two-tissue compartment models using metabolite-corrected arterial input functions. Target selectivity was assessed after pre-administration of the GSK-3 inhibitor PF-04802367 (PF-367, 0.03-0.25 mg/kg). Both radiotracers showed good brain uptake and distribution throughout grey matter. [11C]OCM-44 had a free fraction in the plasma of 3% at baseline and was metabolized quickly. The [11C]OCM-44 volume of distribution (VT) values in the brain increased with time; VT values from models fitted to truncated 60-min scan data were 1.4-2.9 mL/cm3 across brain regions. The plasma free fraction was 0.6% for [18F]OCM-50 and VT values (120-min) were 0.39-0.87 mL/cm3 in grey matter regions. After correcting for plasma free fraction increases during blocking scans, reductions in regional VT indicated >80% target occupancy by 0.1 mg/kg of PF-367 for both radiotracers, supporting target selectivity in vivo. [11C]OCM-44 and [18F]OCM-50 warrant further evaluation as radioligands for imaging GSK-3 in the brain, though radio-metabolite accumulation may confound image analysis.

18F]F13640: a selective agonist PET radiopharmaceutical for imaging functional 5-HT1A receptors in humans

PurposeF13640 (a.k.a. befiradol, NLX-112) is a highly selective 5-HT1A receptor ligand that was selected as a PET radiopharmaceutical-candidate based on animal studies. Due to its high efficacy agonist properties, [18F]F13640 binds preferentially to functional 5-HT1A receptors, which are coupled to intracellular G-proteins. Here, we characterize brain labeling of 5-HT1A receptors by [18F]F13640 in humans and describe a simplified model for its quantification.MethodsPET/CT and PET-MRI scans were conducted in a total of 13 healthy male volunteers (29 ± 9 years old), with arterial input functions (AIF) (n = 9) and test–retest protocol (n = 8). Several kinetic models were compared (one tissue compartment model, two-tissue compartment model, and Logan); two models with reference region were also evaluated: simplified reference tissue model (SRTM) and the logan reference model (LREF).Results[18F]F13640 showed high uptake values in raphe nuclei and cortical regions. SRTM and LREF models showed a very high correlation with kinetic models using AIF. As concerns test–retest parameters and the prolonged binding kinetics of [18F]F13640, better reproducibility, and reliability were found with the LREF method. Cerebellum white matter and frontal lobe white matter stand out as suitable reference regions.ConclusionThe favorable brain labeling and kinetic profile of [18F]F13640, its high receptor specificity and its high efficacy agonist properties open new perspectives for studying functionally active 5-HT1A receptors, unlike previous radiopharmaceuticals that act as antagonists. [18F]F13640’s kinetic properties allow injection outside of the PET scanner with delayed acquisitions, facilitating the design of innovative longitudinal protocols in neurology and psychiatry.Trial Registration.Trial Registration EudraCT 2017–002,722-21.

Imaging Nociceptin Opioid Peptide Receptors in Alcohol Use Disorder With [11C]NOP-1A and Positron Emission Tomography: Findings From a Second Cohort

BackgroundNociceptin, which binds to the nociceptin opioid peptide receptor (NOP), regulates stress and reward in addiction. In a previous [11C]NOP-1A positron emission tomography (PET) study, we found no differences in NOP in non–treatment-seeking individuals with alcohol use disorder (AUD) relative to healthy control subjects Here, we evaluated NOP in treatment-seeking individuals with AUD to document its relationship with relapse to alcohol. Methods[11C]NOP-1A distribution volume (VT) was measured in recently abstinent individuals with AUD and healthy control subjects (n = 27/group) using an arterial input function–based kinetic analysis in brain regions that regulate reward and stress behaviors. Recent heavy drinking before PET was quantified using hair ethyl glucuronide (≥30 pg/mg was defined as heavy drinking). To document relapse, 22 subjects with AUD were followed with urine ethyl glucoronide tests (3/week) for 12 weeks after PET, where they were incentivized with money to abstain. ResultsThere were no differences in [11C]NOP-1A VT between individuals with AUD and healthy control subjects. Individuals with AUD who drank heavily before the study had significantly lower VT than those with no recent heavy drinking history. Significant negative correlations between VT and the number of drinking days and the number of drinks consumed per drinking day in the 30 days before enrollment were also present. Individuals with AUD who relapsed (and dropped out) had significantly lower VT than those who abstained for 12 weeks. ConclusionsLower NOP VT in heavy drinking AUD predicted relapse to alcohol during a 12-week follow-up period. The results of this PET study support the need to investigate medications that act at NOP to prevent relapse in individuals with AUD.

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.

Preliminary Assessment of Reference Region Quantification and Reduced Scanning Times for [18F]SynVesT-1 PET in Parkinson's Disease.

Synaptic density in the central nervous system can be measured in vivo using PET with [18F]SynVesT-1. While [18F]SynVesT-1 has been proven to be a powerful radiopharmaceutical for PET imaging of neurodegenerative disorders such as Parkinson's disease (PD), its currently validated acquisition and quantification protocols are invasive and technically challenging in these populations due to the arterial sampling and relatively long scanning times. The objectives of this work were to evaluate a noninvasive (reference tissue) quantification method for [18F]SynVesT-1 in PD patients and to determine the minimum scan time necessary for accurate quantification. [18F]SynVesT-1 PET scans were acquired in 5 patients with PD and 3 healthy control subjects for 120 min with arterial blood sampling. Quantification was performed using the one-tissue compartment model (1TCM) with arterial input function, as well as with the simplified reference tissue model (SRTM) to estimate binding potential (BPND) using centrum semiovale (CS) as a reference region. The SRTM2 method was used with k2' fixed to either a sample average value (0.037 min-1) or a value estimated first through coupled fitting across regions for each participant. Direct SRTM estimation and the Logan reference region graphical method were also evaluated. There were no significant group differences in CS volume, radiotracer uptake, or efflux (ps > 0.47). Each fitting method produced BPND estimates in close agreement with those derived from the 1TCM (subject R2s > 0.98, bias < 10%), with no difference in bias between the control and PD groups. With SRTM2, BPND estimates from truncated scan data as short as 80 min produced values in excellent agreement with the data from the full 120 min scans (bias < 6%). While these are preliminary results from a small sample of patients with PD (n = 5), this work suggests that accurate synaptic density quantification may be performed without blood sampling and with scan time under 90 minutes. If further validated, these simplified procedures for [18F]SynVesT-1 PET quantification can facilitate its application as a clinical research imaging technology and allow for larger study samples and include a broader scope of patients including those with neurodegenerative diseases.