Glucose metabolism in brain tumours can be estimated using [18F]2-fluorodeoxyglucose positron emission tomography and a population-derived input function scaled using a single arterialised venous blood sample

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.

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.

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.

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.

Methodological considerations in quantification of 3'-deoxy-3'-[18F]fluorothymidine uptake measured with positron emission tomography in patients with non-small cell lung cancer

PURPOSE

To investigate the effect of image-derived input functions (IDIF), input function corrections and volume of interest (VOI) size in quantification of [(18)F]FLT uptake in non-small cell lung cancer (NSCLC) patients.

PROCEDURES

Twenty-three NSCLC patients were scanned on a HR+ scanner. IDIFs were defined over the aorta and left ventricle. Activity concentration and metabolite fraction were measured in venous blood samples. Venous blood samples at 30, 40 and 60 min after injection were used to calibrate the IDIF time-activity curves. Adaptive thresholds were used for VOI definition. Full kinetic analysis and simplified measures were performed.

RESULTS

Non-linear regression analysis showed better fits for the irreversible model compared to the reversible model in the majority. Calibrated and metabolite corrected plus plasma-to-blood ratio corrected input function resulted in high correlations between SUV and Patlak K i (Pearson correlation coefficients 0.86-0.96, p value < 0.001). No significant differences in correlation between SUV and Patlak K i were observed with variation of IDIF structure or VOI size.

CONCLUSIONS

Plasma-to-blood ratio correction, metabolite correction and calibration improved the correlation between SUV and Patlak K i significantly, indicating the need for these corrections when K i is used to validate semi-quantitative measures, such as SUV.

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.

Minimally invasive input function for 2-18F-fluoro-A-85380 brain PET studies

PURPOSE

Quantitative neuroreceptor positron emission tomography (PET) studies often require arterial cannulation to measure input function. While population-based input function (PBIF) would be a less invasive alternative, it has only rarely been used in conjunction with neuroreceptor PET tracers. The aims of this study were (1) to validate the use of PBIF for 2-(18)F-fluoro-A-85380, a tracer for nicotinic receptors; (2) to compare the accuracy of measures obtained via PBIF to those obtained via blood-scaled image-derived input function (IDIF) from carotid arteries; and (3) to explore the possibility of using venous instead of arterial samples for both PBIF and IDIF.

METHODS

Ten healthy volunteers underwent a dynamic 2-(18)F-fluoro-A-85380 brain PET scan with arterial and, in seven subjects, concurrent venous serial blood sampling. PBIF was obtained by averaging the normalized metabolite-corrected arterial input function and subsequently scaling each curve with individual blood samples. IDIF was obtained from the carotid arteries using a blood-scaling method. Estimated Logan distribution volume (V(T)) values were compared to the reference values obtained from arterial cannulation.

RESULTS

For all subjects, PBIF curves scaled with arterial samples were similar in shape and magnitude to the reference arterial input function. The Logan V(T) ratio was 1.00 ± 0.05; all subjects had an estimation error <10%. IDIF gave slightly less accurate results (V(T) ratio 1.03 ± 0.07; eight of ten subjects had an error <10%). PBIF scaled with venous samples yielded inaccurate results (V(T) ratio 1.13 ± 0.13; only three of seven subjects had an error <10%). Due to arteriovenous differences at early time points, IDIF could not be calculated using venous samples.

CONCLUSION

PBIF scaled with arterial samples accurately estimates Logan V(T) for 2-(18)F-fluoro-A-85380. Results obtained with PBIF were slightly better than those obtained with IDIF. Due to arteriovenous concentration differences, venous samples cannot be substituted for arterial samples.

Influence of dietary state and insulin on myocardial, skeletal muscle and brain [F]-fluorodeoxyglucose kinetics in mice

BACKGROUND

We evaluated the effect of insulin stimulation and dietary changes on myocardial, skeletal muscle and brain [(18)F]-fluorodeoxyglucose (FDG) kinetics and uptake in vivo in intact mice.

METHODS

Mice were anesthetized with isoflurane and imaged under different conditions: non-fasted (n = 7; "controls"), non-fasted with insulin (2 IU/kg body weight) injected subcutaneously immediately prior to FDG (n = 6), fasted (n = 5), and fasted with insulin injection (n = 5). A 60-min small-animal PET with serial blood sampling and kinetic modeling was performed.

RESULTS

We found comparable FDG standardized uptake values (SUVs) in myocardium in the non-fasted controls and non-fasted-insulin injected group (SUV 45-60 min, 9.58 ± 1.62 vs. 9.98 ± 2.44; p = 0.74), a lower myocardial SUV was noted in the fasted group (3.48 ± 1.73; p < 0.001). In contrast, the FDG uptake rate constant (K(i)) for myocardium increased significantly by 47% in non-fasted mice by insulin (13.4 ± 3.9 ml/min/100 g vs. 19.8 ± 3.3 ml/min/100 g; p = 0.030); in fasted mice, a lower myocardial K(i) as compared to controls was observed (3.3 ± 1.9 ml/min/100 g; p < 0.001). Skeletal muscle SUVs and K(i) values were increased by insulin independent of dietary state, whereas in the brain, those parameters were not influenced by fasting or administration of insulin. Fasting led to a reduction in glucose metabolic rate in the myocardium (19.41 ± 5.39 vs. 3.26 ± 1.97 mg/min/100 g; p < 0.001), the skeletal muscle (1.06 ± 0.34 vs. 0.34 ± 0.08 mg/min/100 g; p = 0.001) but not the brain (3.21 ± 0.53 vs. 2.85 ±0.25 mg/min/100 g; p = 0.19).

CONCLUSIONS

Changes in organ SUVs, uptake rate constants and metabolic rates induced by fasting and insulin administration as observed in intact mice by small-animal PET imaging are consistent with those observed in isolated heart/muscle preparations and, more importantly, in vivo studies in larger animals and in humans. When assessing the effect of insulin on the myocardial glucose metabolism of non-fasted mice, it is not sufficient to just calculate the SUV - dynamic imaging with kinetic modeling is necessary.

Image-derived input function in dynamic human PET/CT: methodology and validation with 11C-acetate and 18F-fluorothioheptadecanoic acid in muscle and 18F-fluorodeoxyglucose in brain

Purpose

Despite current advances in PET/CT systems, blood sampling still remains the standard method to obtain the radiotracer input function for tracer kinetic modelling. The purpose of this study was to validate the use of image-derived input functions (IDIF) of the carotid and femoral arteries to measure the arterial input function (AIF) in PET imaging. The data were obtained from two different research studies, one using ¹⁸F-FDG for brain imaging and the other using ¹¹C-acetate and ¹⁸F-fluoro-6-thioheptadecanoic acid (¹⁸F-FTHA) in femoral muscles.

Methods

The method was validated with two phantom systems. First, a static phantom consisting of syringes of different diameters containing radioactivity was used to determine the recovery coefficient (RC) and spill-in factors. Second, a dynamic phantom built to model bolus injection and clearance of tracers was used to establish the correlation between blood sampling, AIF and IDIF. The RC was then applied to the femoral artery data from PET imaging studies with ¹¹C-acetate and ¹⁸F-FTHA and to carotid artery data from brain imaging with ¹⁸F-FDG. These IDIF data were then compared to actual AIFs from patients.

Results

With ¹¹C-acetate, the perfusion index in the femoral muscle was 0.34±0.18 min⁻¹ when estimated from the actual time–activity blood curve, 0.29±0.15 min⁻¹ when estimated from the corrected IDIF, and 0.66±0.41 min⁻¹ when the IDIF data were not corrected for RC. A one-way repeated measures (ANOVA) and Tukey’s test showed a statistically significant difference for the IDIF not corrected for RC (p<0.0001). With ¹⁸F-FTHA there was a strong correlation between Patlak slopes, the plasma to tissue transfer rate calculated using the true plasma radioactivity content and the corrected IDIF for the femoral muscles (vastus lateralis r=0.86, p=0.027; biceps femoris r=0.90, p=0.017). On the other hand, there was no correlation between the values derived using the AIF and those derived using the uncorrected IDIF. Finally, in the brain imaging study with ¹⁸F-FDG, the cerebral metabolic rate of glucose (CMRglc) measured using the uncorrected IDIF was consistently overestimated. The CMRglc obtained using blood sampling was 13.1±3.9 mg/100 g per minute and 14.0±5.7 mg/100 g per minute using the corrected IDIF (r²=0.90).

Conclusion

Correctly obtained, carotid and femoral artery IDIFs can be used as a substitute for AIFs to perform tracer kinetic modelling in skeletal femoral muscles and brain analyses.

Simultaneous estimation of input functions: an empirical study

In neuroreceptor mapping, methods for the estimation of distribution volume require determination of a metabolite-corrected arterial input function. In application, this may be accomplished by collecting arterial blood samples during scanning, adjusting these measurements according to a separate metabolite analysis, and then modeling the resulting concentration data. Although many groups do this routinely, it is invasive and requires considerable effort. Furthermore, both the plasma and the metabolite data are noisy, and thus estimation of kinetic parameters can be affected by this variability. One promising alternative to full-input function modeling is the simultaneous estimation (SIME) approach, in which kinetic parameters and common input function parameters are estimated using results obtained from several regions at once. We investigate the performance of this approach on data from four different radioligands, using various kinetic models, comparing the results with those obtained by estimation using full-input function modeling. Results indicate that SIME provides a promising alternative for all the radioligands considered.