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

Dual-time-point dynamic 68Ga-PSMA-11 PET/CT for parametric imaging generation in prostate cancer

PURPOSE

To investigate the optimal dual-time-point (DTP) approaches using dynamic 68Ga-PSMA-11 PET/CT imaging to generate parametric images for prostate cancer patients.

METHODS

Fifteen patients with prostate cancer were intravenously administered 68Ga-PSMA-11 of 181.9 ± 47.2 MBq, followed by an immediate 60 min dynamic PET/CT scan. List-mode data were reconstructed into 25 timeframes (6 × 10 s, 8 × 30 s, and 11 × 300 s) and corrected for motion and partial volume effect. DTP parametric images were generated using different interval time points of 5 min and 10 min, with a minimum of 30 min time interval. Net influx rates (Ki) were calculated through the fitting of a single irreversible two-tissue compartmental model. Intraclass correlation coefficient (ICC) values between DTP protocols and 60 min Ki were obtained. Lesion-to-background ratios (LBRs) of Ki and standardized uptake value (SUV) images in each DTP protocol were determined.

RESULTS

The DTP protocol of 5-10 min with a 40-45 min interval showed the highest ICC of 0.988 compared with the 60 min Ki, whereas the ICC values for the intervals of 0-5 min with 55-60 min and 0-10 min with 50-60 min were 0.941. The LBRs of the 60 min Ki, 5-10 min with 40-45 min Ki, 0-5 min with 55-60 min Ki, 0-10 min with 50-60 min Ki, SUVmean, and SUVmax images were 29.53 ± 27.33, 13.05 ± 15.28, 45.15 ± 53.11, 45.52 ± 70.31, 19.77 ± 23.43, and 25.06 ± 30.07, respectively.

CONCLUSION

The 0-5 min with 55-60 min DTP parametric imaging exhibits a comparable Ki to 60 min parametric imaging and remarkable image quality and contrast than SUV imaging, enhancing prostate cancer diagnosis while maintaining time efficiency.

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET scans. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.

Unique advantages of dynamic l-[11C]methionine PET/CT for assessing the rate of skeletal muscle protein synthesis: A pilot trial in young men

Although the standard method to evaluate skeletal muscle protein synthesis (MPS) is muscle biopsy, the method is invasive and problematic for multisite use. We conducted a small pilot study in volunteers to investigate changes in MPS according to skeletal muscle site using a noninvasive method in which 6 healthy young men were given yogurt (containing 20 g milk protein) or water, and 1 h later, l-[11C]methionine ([11C]Met) was administered intravenously. Dynamic PET/CT imaging of their thighs was performed for 60 min. The influx constant Ki of [11C]Met in skeletal muscle protein was calculated as an index of MPS using a Patlak plot, and found to be 0.6%-28% higher after ingesting yogurt than after water in 5 of the 6 volunteer participants, but it was 34% lower in the remaining participant. Overall, this indicated no significant increase in Ki after ingesting milk protein. However, when the quadriceps and hamstring muscles were analyzed separately, we found a significant difference in Ki. This demonstrates the potential of visualizing MPS by calculating the Ki for each voxel and reconstructing it as an image, which presents unique advantages of [11C]Met PET/CT for evaluating MPS, such as site-specificity and visualization.

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.

Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging

Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation.

METHODS

The conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored.

RESULTS

The estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method.

CONCLUSION

The proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.

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.

Evaluation of Non-Invasive Methods for (R)-[11C]PK11195 PET Image Quantification in Multiple Sclerosis

This study aims to evaluate non-invasive PET quantification methods for (R)-[11C]PK11195 uptake measurement in multiple sclerosis (MS) patients and healthy controls (HC) in comparison with arterial input function (AIF) using dynamic (R)-[11C]PK11195 PET and magnetic resonance images. The total volume of distribution (VT) and distribution volume ratio (DVR) were measured in the gray matter, white matter, caudate nucleus, putamen, pallidum, thalamus, cerebellum, and brainstem using AIF, the image-derived input function (IDIF) from the carotid arteries, and pseudo-reference regions from supervised clustering analysis (SVCA). Uptake differences between MS and HC groups were tested using statistical tests adjusted for age and sex, and correlations between the results from the different quantification methods were also analyzed. Significant DVR differences were observed in the gray matter, white matter, putamen, pallidum, thalamus, and brainstem of MS patients when compared to the HC group. Also, strong correlations were found in DVR values between non-invasive methods and AIF (0.928 for IDIF and 0.975 for SVCA, p < 0.0001). On the other hand, (R)-[11C]PK11195 uptake could not be differentiated between MS patients and HC using VT values, and a weak correlation (0.356, p < 0.0001) was found between VTAIF and VTIDIF. Our study shows that the best alternative for AIF is using SVCA for reference region modeling, in addition to a cautious and appropriate methodology.

Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [18F]FDG PET

BACKGROUND

Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data.

METHODS

Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach.

RESULTS

Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns.

CONCLUSIONS

A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.

High Tyrosol and Hydroxytyrosol Intake Reduces Arterial Inflammation and Atherosclerotic Lesion Microcalcification in Healthy Older Populations

Aging is an important risk factor for cardiovascular diseases and convincing data have shown that chronic low-grade inflammation, which develops with advanced age, contributes significantly to cardiovascular risk. The present study aimed to use 18F-FDG/18F-NaF-PET/CT imaging to, respectively, gauge arterial inflammation and microcalcification in a healthy elderly population and to assess the potential benefits of a tyrosol- and hydroxytyrosol-rich diet on these two markers of atherosclerotic plaque fragility. Eleven healthy participants (mean age 75 ± 5.67 years) were supplemented for 6 months with high polyphenol-rich extra virgin olive oil (HP-EVOO), extra virgin olive oil (EVOO), or refined olive oil (ROO). The participants underwent PET/CT imaging with 18F-FDG and 18F-NaF radiotracers at baseline and after 6 months. 18F-FDG and 18F-NaF uptakes were quantified using standardized uptake values (SUV) and were categorized based on artery calcification and olive oil type. A total of 324 slices of the aortas of the imaged participants were analyzed for arterial inflammation and 327 slices were analyzed for microcalcification. 18F-FDG uptake was significantly higher in the non-calcified segments than in the calcified segments (SUVmax = 2.70 ± 0.62 and SUVmax = 2.54 ± 0.44, respectively, p < 0.042). Conversely, the non-calcified segments displayed significantly lower 18F-NaF uptake than the calcified segments (SUVmax = 1.90 ± 0.37 and 2.09 ± 0.24, respectively, p < 0.0001). The 6-month supplementation with HP-EVOO induced a significant reduction in 18F-FDG uptake in both the non-calcified (2.93 ± 0.23 to 2.75 ± 0.38, p < 0.004) and calcified segments of the aortas (2.25 ± 0.29 to 2.15 ± 0.19, p < 0.02). 18F-NaF uptake was also significantly lower in patients supplemented with HP-EVOO (SUVmax = 1.98 ± 0.33 at baseline compared to 1.85 ± 0.28, after the 6-month supplementation, p < 0.004), whereas no significant effect was observed with EVOO. Conversely, participants supplemented with ROO displayed a significant increase in 18F-NaF uptake (SUVmax = 1.78 ± 0.34 to 1.95 ± 0.34, p < 0.0001). The present study confirmed that a phenolic-compound-rich diet reduces both arterial inflammation and atherosclerotic lesion microcalcification and demonstrated that 18F-FDG/18F-NaF-PET/CT imaging is a valuable approach for assessing age-related arterial damage.

Impact of patient characteristic factors on the dynamics of liver glucose metabolism: Evaluation of multiparametric imaging with dynamic whole-body 18 F-fluorodeoxyglucose-positron emission tomography

AIMS

To assess the impact of various patient characteristics on the dynamics of liver glucose metabolism using automated multiparametric imaging with whole-body dynamic 18 F-fluorodeoxyglucose (FDG)-positron emission tomography (PET).

MATERIALS AND METHODS

We retrospectively enrolled 540 patients who underwent whole-body dynamic FDG-PET. Three quantitative indices representing hepatic glucose metabolism [mean standardized uptake value normalized by lean body mass (SULmean), metabolic glucose rate (kinetic index) and distribution volume (DV)] were measured from multiparametric PET images produced automatically based on the Patlak plot model. Patient characteristics including age, sex, body mass index, fasting time, blood glucose level, and the presence of diabetes mellitus (DM) or hepatic steatosis (HS) were collected. We examined the correlations between the characteristic factors and three quantitative indices using multiple regression analysis.

RESULTS

The success rate of kinetic analysis using multiparametric PET imaging was 93.3% (504/540). Hepatic SULmean was significantly correlated with age (p < .001), sex (p < .001) and blood glucose level (p = .002). DV was significantly correlated with age (p = .033), sex (p < .001), body mass index (p = .002), fasting time (p = .043) and the presence of HS (p = .002). The kinetic index was significantly correlated with age (p < .001) and sex (p = .004). In the comparison of the healthy, DM and HS groups, patients with DM had a significantly increased SULmean, whereas patients with HS had a significantly decreased DV.

CONCLUSIONS

Our results showed that liver glucose metabolism was influenced by various patient characteristic factors. Multiparametric FDG-PET imaging can be used to analyse the kinetics of liver glucose metabolism in routine clinical practice.

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

BACKGROUND

Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans.

METHODS

A Flangeless Esser PET Phantom with eight hollow spheres (4-25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data.

RESULTS

In general, RC and CVRC decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the Ki.

CONCLUSION

In a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300-500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .

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.

Non-calcified active atherosclerosis plaque detection with 18F-NaF and 18F-FDG PET/CT dynamic imaging

Arterial inflammation is an indicator of atheromatous plaque vulnerability to detach and to obstruct blood vessels in the heart or in the brain thus causing heart attack or stroke. To date, it is difficult to predict the plaque vulnerability. This study was aimed to assess the behavior of 18F-sodium fluoride (18F-NaF) and 18F-fluorodeoxyglucose (18F-FDG) uptake in the aorta and iliac arteries as a function of plaque density on CT images. We report metabolically active artery plaques associated to inflammation in the absence of calcification. 18 elderly volunteers were recruited and imaged with computed tomography (CT) and positron emission tomography (PET) with 18F-NaF and 18F-FDG. A total of 1338 arterial segments were analyzed, 766 were non-calcified and 572 had calcifications. For both 18F-NaF and 18F-FDG, the mean SUV values were found statistically significantly different between non-calcified and calcified artery segments. Clustering CT non-calcified segments, excluding blood, resulted in two clusters C1 and C2 with a mean density of 30.63 ± 5.06 HU in C1 and 43.06 ± 4.71 HU in C2 (P < 0.05), and their respective SUV were found statistically different in 18F-NaF and 18F-FDG. The 18F-NaF images showed plaques not detected on CT images, where the 18F-FDG SUV values were high in comparison to artery walls without plaques. The density on CT images alone corresponding to these plaques could be further investigated to see whether it can be an indicator of the active plaques.

Stimulation of the beta-2-adrenergic receptor with salbutamol activates human brown adipose tissue

While brown adipose tissue (BAT) is activated by the beta-3-adrenergic receptor (ADRB3) in rodents, in human brown adipocytes, the ADRB2 is dominantly present and responsible for noradrenergic activation. Therefore, we performed a randomized double-blinded crossover trial in young lean men to compare the effects of single intravenous bolus of the ADRB2 agonist salbutamol without and with the ADRB1/2 antagonist propranolol on glucose uptake by BAT, assessed by dynamic 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography-computed tomography scan (i.e., primary outcome). Salbutamol, compared with salbutamol with propranolol, increases glucose uptake by BAT, without affecting the glucose uptake by skeletal muscle and white adipose tissue. The salbutamol-induced glucose uptake by BAT positively associates with the increase in energy expenditure. Notably, participants with high salbutamol-induced glucose uptake by BAT have lower body fat mass, waist-hip ratio, and serum LDL-cholesterol concentration. In conclusion, specific ADRB2 agonism activates human BAT, which warrants investigation of ADRB2 activation in long-term studies (EudraCT: 2020-004059-34).

A deep neural network for parametric image reconstruction on a large axial field-of-view PET

PURPOSE

The PET scanners with long axial field of view (AFOV) having ~ 20 times higher sensitivity than conventional scanners provide new opportunities for enhanced parametric imaging but suffer from the dramatically increased volume and complexity of dynamic data. This study reconstructed a high-quality direct Patlak Ki image from five-frame sinograms without input function by a deep learning framework based on DeepPET to explore the potential of artificial intelligence reducing the acquisition time and the dependence of input function in parametric imaging.

METHODS

This study was implemented on a large AFOV PET/CT scanner (Biograph Vision Quadra) and twenty patients were recruited with ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) dynamic scans. During training and testing of the proposed deep learning framework, the last five-frame (25 min, 40-65 min post-injection) sinograms were set as input and the reconstructed Patlak Ki images by a nested EM algorithm on the vendor were set as ground truth. To evaluate the image quality of predicted Ki images, mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) were calculated. Meanwhile, a linear regression process was applied between predicted and true Ki means on avid malignant lesions and tumor volume of interests (VOIs).

RESULTS

In the testing phase, the proposed method achieved excellent MSE of less than 0.03%, high SSIM, and PSNR of ~ 0.98 and ~ 38 dB, respectively. Moreover, there was a high correlation (DeepPET: [Formula: see text]= 0.73, self-attention DeepPET: [Formula: see text]=0.82) between predicted Ki and traditionally reconstructed Patlak Ki means over eleven lesions.

CONCLUSIONS

The results show that the deep learning-based method produced high-quality parametric images from small frames of projection data without input function. It has much potential to address the dilemma of the long scan time and dependency on input function that still hamper the clinical translation of dynamic PET.

Non-invasive kinetic modelling approaches for quantitative analysis of brain PET studies

Pharmacokinetic modelling with arterial sampling is the gold standard for analysing dynamic PET data of the brain. However, the invasive character of arterial sampling prevents its widespread clinical application. Several methods have been developed to avoid arterial sampling, in particular reference region methods. Unfortunately, for some tracers or diseases, no suitable reference region can be defined. For these cases, other potentially non-invasive approaches have been proposed: (1) a population based input function (PBIF), (2) an image derived input function (IDIF), or (3) simultaneous estimation of the input function (SIME). This systematic review aims to assess the correspondence of these non-invasive methods with the gold standard. Studies comparing non-invasive pharmacokinetic modelling methods with the current gold standard methods using an input function derived from arterial blood samples were retrieved from PubMed/MEDLINE (until December 2021). Correlation measurements were extracted from the studies. The search yielded 30 studies that correlated outcome parameters (V_T, DVR, or BP_ND for reversible tracers; K_i or CMR_glu for irreversible tracers) from a potentially non-invasive method with those obtained from modelling using an arterial input function. Some studies provided similar results for PBIF, IDIF, and SIME-based methods as for modelling with an arterial input function (R² = 0.59-1.00, R² = 0.71-1.00, R² = 0.56-0.96, respectively), if the non-invasive input curve was calibrated with arterial blood samples. Even when the non-invasive input curve was calibrated with venous blood samples or when no calibration was applied, moderate to good correlations were reported, especially for the IDIF and SIME (R² = 0.71-1.00 and R² = 0.36-0.96, respectively). Overall, this systematic review illustrates that non-invasive methods to generate an input function are still in their infancy. Yet, IDIF and SIME performed well, not only with arterial blood calibration, but also with venous or no blood calibration, especially for some tracers without plasma metabolites, which would potentially make these methods better suited for clinical application. However, these methods should still be properly validated for each individual tracer and application before implementation.

Development of a minimally invasive simultaneous estimation method for quantifying translocator protein binding with [18F]FEPPA positron emission tomography

BACKGROUND

The purpose of this study was to assess the feasibility of using a minimally invasive simultaneous estimation method (SIME) to quantify the binding of the 18-kDa translocator protein tracer [¹⁸F]FEPPA. Arterial sampling was avoided by extracting an image-derived input function (IDIF) that was metabolite-corrected using venous blood samples. The possibility of reducing scan duration to 90 min from the recommended 2-3 h was investigated by assuming a uniform non-displaceable distribution volume (V_ND) to simplify the SIME fitting.

RESULTS

SIME was applied to retrospective data from healthy volunteers and was comprised of both high-affinity binders (HABs) and mixed-affinity binders (MABs). Estimates of global V_ND and regional total distribution volume (V_T) from SIME were not significantly different from values obtained using a two-tissue compartment model (2CTM). Regional V_T estimates were greater for HABs compared to MABs for both the 2TCM and SIME, while the SIME estimates had lower inter-subject variability (41 ± 17% reduction). Binding potential (BP_ND) values calculated from regional V_T and brain-wide V_ND estimates were also greater for HABs, and reducing the scan time from 120 to 90 min had no significant effect on BP_ND. The feasibility of using venous metabolite correction was evaluated in a large animal model involving a simultaneous collection of arterial and venous samples. Strong linear correlations were found between venous and arterial measurements of the blood-to-plasma ratio and the remaining [¹⁸F]FEPPA fraction. Lastly, estimates of BP_ND and the specific distribution volume (i.e., V_S = V_T - V_ND) from a separate group of healthy volunteers (90 min scan time, venous-scaled IDIFs) agreed with estimates from the retrospective data for both genotypes.

CONCLUSIONS

The results of this study demonstrate that accurate estimates of regional V_T, BP_ND and V_S can be obtained by applying SIME to [¹⁸F]FEPPA data. Furthermore, the application of SIME enabled the scan time to be reduced to 90 min, and the approach worked well with IDIFs that were scaled and metabolite-corrected using venous blood samples.

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.

Investigation of image-derived input functions for non-invasive quantification of myelin density using [11C]MeDAS PET

Multiple sclerosis (MS) is an inflammatory demyelinating disease. Current treatments are focussed on immune suppression to modulate pathogenic activity that causes myelin damage. New treatment strategies are needed to prevent demyelination and promote remyelination. Development of such myelin repair therapies require a sensitive and specific biomarker for efficacy evaluation. Recently, it has been shown that quantification of myelin density is possible using [¹¹C]MeDAS PET. This method, however, requires arterial blood sampling to generate an arterial input function (AIF). As the invasive nature of arterial sampling will reduce clinical applicability, the purpose of this study was to assess whether an image-derived input function (IDIF) can be used as an alternative way to facilitate its routine clinical use. Six healthy controls and 11 MS patients underwent MRI and [¹¹C]MeDAS PET with arterial blood sampling. The application of both population-based whole blood-to-plasma conversion and metabolite corrections were assessed for the AIF. Next, summed images of the early time frames (0-70 s) and the frame with the highest blood-brain contrast were used to generate IDIFs. IDIFs were created using either the hottest 2, 4, 6 or 12 voxels, or an isocontour of the hottest 10% voxels of the carotid artery. This was followed by blood-to-plasma conversion and metabolite correction of the IDIF. The application of a population-based metabolite correction of the AIF resulted in high correlations of tracer binding (K_i) within subjects, but variable bias across subjects. All IDIFs had a sharper and higher peak in the blood curves than the AIF, most likely due to dispersion during blood sampling. All IDIF methods resulted in similar high correlations within subjects (r = 0.95-0.98), but highly variable bias across subjects (mean slope=0.90-1.09). Therefore, both the use of population based blood-plasma and metabolite corrections and the generation of the image-derived whole-blood curve resulted in substantial bias in [¹¹C]MeDAS PET quantification, due to high inter-subject variability. Consequently, when unbiased quantification of [¹¹C]MeDAS PET data is required, individual AIF needs to be used.

Attenuation correction for PET/MRI to measure tracer activity surrounding total knee arthroplasty

Background

Positron emission tomography (PET) in combination with magnetic resonance imaging (MRI) could allow inflammatory complications near total knee arthroplasty (TKA) to be studied early in their development. However, attenuation of the PET signal by the metal TKA implants imparts substantial error into measurements of tracer activity, and conventional MR-based attenuation correction (AC) methods have large signal voids in the vicinity of metal implants.

Purpose

To evaluate a segmentation-based AC approach to measure tracer uptake from PET/MRI scans near TKA implants.

Methods

A TKA implant (Triathlon, Stryker, Mahwah, USA) was implanted into a cadaver. Four vials were filled with [¹⁸F]fluorodeoxyglucose with known activity concentration (4.68 MBq total, 0.76 MBq/mL) and inserted into the knee. Images of the knee were acquired using a 3T PET/MRI system (Biograph mMR, Siemens Healthcare, Erlangen, Germany). Models of the implant components were registered to the MR data using rigid-body transformations and the other tissue classes were manually segmented. These segments were used to create the segmentation-based map and complete the AC. Percentage error of the resulting measured activities was calculated by comparing the measured and known amounts of activity in each vial.

Results

The original AC resulted in a percentage error of 64.1% from the known total activity. Errors in the individual vial activities ranged from 40.2 to 82.7%. Using the new segmentation-based AC, the percentage error of the total activity decreased to 3.55%. Errors in the individual vials were less than 15%.

Conclusions

The segmentation-based AC technique dramatically reduced the error in activity measurements that result from PET signal attenuation by the metal TKA implant. This approach may be useful to enhance the reliability of PET/MRI measurements for numerous applications.

caliPER: A software for blood-free parametric Patlak mapping using PET/MRI input function

Routine clinical use of absolute PET quantification techniques is limited by the need for serial arterial blood sampling for input function and more importantly by the lack of automated pharmacokinetic analysis tools that can be readily implemented in clinic with minimal effort. PET/MRI provides the ability for absolute quantification of PET probes without the need for serial arterial blood sampling using image-derived input functions (IDIFs). Here we introduce caliPER, a modular and scalable software for simplified pharmacokinetic modelling of PET probes with irreversible uptake or binding based on PET/MR IDIFs and Patlak Plot analysis. caliPER generates regional values or parametric maps of net influx rate (K_i) using reconstructed dynamic PET images and anatomical MRI aligned to PET for IDIF vessel delineation. We evaluated the performance of caliPER for blood-free region-based and pixel-wise Patlak analyses of [¹⁸F] FDG by comparing caliPER IDIF to serial arterial blood input functions and its application in imaging brain glucose hypometabolism in Frontotemporal dementia. IDIFs corrected for partial volume errors including spill-out and spill-in effects were similar to arterial blood input functions with a general bias of around 6-8%, even for arteries <5 mm. The K_i and cerebral metabolic rate of glucose estimated using caliPER IDIF were similar to estimates using arterial blood sampling (<2%) and within limits of whole brain values reported in literature. Overall, caliPER is a promising tool for irreversible PET tracer quantification and can simplify the ability to perform parametric analysis in clinical settings without the need for blood sampling.

Phantom Validation of a Conservation of Activity-Based Partial Volume Correction Method for Arterial Input Function in Dynamic PET Imaging

Dynamic PET (dPET) imaging can be utilized to perform kinetic modelling of various physiologic processes, which are exploited by the constantly expanding range of targeted radiopharmaceuticals. To date, dPET remains primarily in the research realm due to a number of technical challenges, not least of which is addressing partial volume effects (PVE) in the input function. We propose a series of equations for the correction of PVE in the input function and present the results of a validation study, based on a purpose built phantom. ¹⁸F-dPET experiments were performed using the phantom on a set of flow tubes representing large arteries, such as the aorta (1" 2.54 cm ID), down to smaller vessels, such as the iliac arteries and veins (1/4" 0.635 cm ID). When applied to the dPET experimental images, the PVE correction equations were able to successfully correct the image-derived input functions by as much as 59 ± 35% in the presence of background, which resulted in image-derived area under the curve (AUC) values within 8 ± 9% of ground truth AUC. The peak heights were similarly well corrected to within 9 ± 10% of the scaled DCE-CT curves. The same equations were then successfully applied to correct patient input functions in the aorta and internal iliac artery/vein. These straightforward algorithms can be applied to dPET images from any PET-CT scanner to restore the input function back to a more clinically representative value, without the need for high-end Time of Flight systems or Point Spread Function correction algorithms.

Non-invasive quantification of cerebral glucose metabolism using Gjedde-Patlak plot and image-derived input function from the aorta

INTRODUCTION

We aimed at evaluating a Gjedde-Patlak plot and non-invasive image-derived input functions (IDIF) from the aorta to quantify cerebral glucose metabolic rate (CMRglc) in comparison to the reference standard based on sampling the arterial input function (AIF).

METHOD

Six healthy subjects received 200 MBq [¹⁸F]FDG simultaneously with the initiation of a three-part dynamic PET recording consisting of a 15 min-recording of the aorta, a 40 min-recording of the brain and finally 2 min-recording of the aorta. Simultaneously, the arterial ¹⁸F concentration was measured via arterial cannulation. Regions of interest were drawn in the aorta and the brain and time-activity curves extracted. The IDIF was obtained by fitting a triple exponential function to the aorta time-activity curve after the initial peak including the late aorta frame, thereby interpolating the arterial blood activity concentration during the brain scan. CMRglc was calculated from Gjedde-Patlak plots using AIF and IDIF, respectively and the predictive value was examined. Results from frontal cortex, insula, hippocampus and cerebellum were compared by paired t-test and agreement between the methods was analyzed by Bland-Altman plot statistics.

RESULTS

There was a strong linear relationship and an excellent agreement between the methods (mean±SD of CMRglcIDIF (μmol 100 g^-1 min^-1), mean difference, mean relative difference, 95% limits of agreement): frontal cortex: 30.8 ± 3.3, 0.5, 2.2%, [-1,6:2.5], insula: 25.4 ± 2.2, 0.4, 2.4%, [-1.4:2.2], hippocampus: 16.9 ± 1.2, 0.4, 3.8%, [-1.1:2.0] and cerebellum: 23.4 ± 1.9, 0.5, 3.1%, [-1.4:2.5]).

CONCLUSION

We found excellent agreement between CMRglc obtained with an IDIF from the aorta and the reference standard with AIF. A non-invasive three-part dynamic [¹⁸F]FDG PET recording is feasible as a non-invasive alternative for reliable quantification of cerebral glucose metabolism in all scanner systems. This is useful in patients with presumed global cerebral changes owing to systemic disease or for the monitoring of treatment effects.

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.

Functional characterization of human brown adipose tissue metabolism

Brown adipose tissue (BAT) has long been described according to its histological features as a multilocular, lipid-containing tissue, light brown in color, that is also responsive to the cold and found especially in hibernating mammals and human infants. Its presence in both hibernators and human infants, combined with its function as a heat-generating organ, raised many questions about its role in humans. Early characterizations of the tissue in humans focused on its progressive atrophy with age and its apparent importance for cold-exposed workers. However, the use of positron emission tomography (PET) with the glucose tracer [18F]fluorodeoxyglucose ([18F]FDG) made it possible to begin characterizing the possible function of BAT in adult humans, and whether it could play a role in the prevention or treatment of obesity and type 2 diabetes (T2D). This review focuses on the in vivo functional characterization of human BAT, the methodological approaches applied to examine these features and addresses critical gaps that remain in moving the field forward. Specifically, we describe the anatomical and biomolecular features of human BAT, the modalities and applications of non-invasive tools such as PET and magnetic resonance imaging coupled with spectroscopy (MRI/MRS) to study BAT morphology and function in vivo, and finally describe the functional characteristics of human BAT that have only been possible through the development and application of such tools.

Cross-validation of a non-invasive positron detector to measure the arterial input function for pharmacokinetic modelling in dynamic positron emission tomography

Kinetic modeling of positron emission tomography (PET) data can assess index rate of uptake, metabolism and predict disease progression more accurately than conventional static PET. However, it requires knowledge of the time-course of the arterial blood radioactivity concentration, called the arterial input function (AIF). The gold standard to acquire the AIF is by invasive means. The purpose of this study was to validate a previously developed dual readout scintillating fiber-based non-invasive positron detector, hereinafter called non-invasive detector (NID), developed to determine the AIF for dynamic PET measured from the human radial artery. The NID consisted of a 3 m long plastic scintillating fiber with each end coupled to a 5 m long transmission fiber followed by a silicon photomultiplier. The scintillating fiber was enclosed inside the grooves of a plastic cylindrical shell. Two sets of experiments were performed to test the NID against a previously validated microfluidic positron detector. A closed-loop microfluidic system combined with a wrist phantom was used. During the first experiment, the three PET radioisotopes ¹⁸F, ¹¹C and ⁶⁸Ga were tested. After optimizing the detector, a second series of tests were performed using only ¹⁸F and ¹¹C. The maximum pulse amplitude to electronic noise ratio was 52 obtained with ¹¹C. Linear regressions showed a linear relation between the two detectors. These preliminary results show that the NID can accurately detect positrons from a patient's wrist and has the potential to non-invasively measure the AIF during a dynamic PET scan. The accuracy of these measurements needs to be determined.

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.

Kinetic modeling of 68Ga-PSMA-11 and validation of simplified methods for quantification in primary prostate cancer patients

BACKGROUND

The positron emission tomography (PET) ligand ⁶⁸Ga-Glu-urea-Lys(Ahx)-HBED-CC (⁶⁸Ga-PSMA-11) targets the prostate-specific membrane antigen (PSMA), upregulated in prostate cancer cells. Although ⁶⁸Ga-PSMA-11 PET is widely used in research and clinical practice, full kinetic modeling has not yet been reported nor have simplified methods for quantification been validated. The aims of our study were to quantify ⁶⁸Ga-PSMA-11 uptake in primary prostate cancer patients using compartmental modeling with arterial blood sampling and to validate the use of standardized uptake values (SUV) and image-derived blood for quantification.

RESULTS

Fifteen patients with histologically proven primary prostate cancer underwent a 60-min dynamic ⁶⁸Ga-PSMA-11 PET scan of the pelvis with axial T1 Dixon, T2, and diffusion-weighted magnetic resonance (MR) images acquired simultaneously. Time-activity curves were derived from volumes of interest in lesions, normal prostate, and muscle, and mean SUV calculated. In total, 18 positive lesions were identified on both PET and MR. Arterial blood activity was measured by automatic arterial blood sampling and manual blood samples were collected for plasma-to-blood ratio correction and for metabolite analysis. The analysis showed that ⁶⁸Ga-PSMA-11 was stable in vivo. Based on the Akaike information criterion, ⁶⁸Ga-PSMA-11 kinetics were best described by an irreversible two-tissue compartment model. The rate constants K₁ and k₃ and the net influx rate constants K_i were all significantly higher in lesions compared to normal tissue (p < 0.05). K_i derived using image-derived blood from an MR-guided method showed excellent agreement with K_i derived using arterial blood sampling (intraclass correlation coefficient = 0.99). SUV correlated significantly with K_i with the strongest correlation of scan time-window 30-45 min (rho 0.95, p < 0.001). Both K_i and SUV correlated significantly with serum prostate specific antigen (PSA) level and PSA density.

CONCLUSIONS

⁶⁸Ga-PSMA-11 kinetics can be described by an irreversible two-tissue compartment model. An MR-guided method for image-derived blood provides a non-invasive alternative to blood sampling for kinetic modeling studies. SUV showed strong correlation with K_i and can be used in routine clinical settings to quantify ⁶⁸Ga-PSMA-11 uptake.

Image-Derived Input Functions for Quantification of A1 Adenosine Receptors Availability in Mice Brains Using PET and [18F]CPFPX

Purpose

In vivo imaging for the A₁ adenosine receptors (A₁ARs) with positron emission tomography (PET) using 8-cyclopentyl-3-(3-[¹⁸F]fluoropropyl)-1-propylxan- thine ([¹⁸F]CPFPX) has become an important tool for studying physiological processes quantitatively in mice. However, the measurement of arterial input functions (AIFs) on mice is a method with restricted applicability because of the small total blood volume and the related difficulties in withdrawing blood. Therefore, the aim of this study was to extract an appropriate [¹⁸F]CPFPX image-derived input function (IDIF) from dynamic PET images of mice.

Procedures

In this study, five mice were scanned with [¹⁸F]CPFPX for 60 min. Arterial blood samples (n = 7 per animal) were collected from the femoral artery and corrected for metabolites. To generate IDIFs, three different approaches were selected: (A) volume of interest (VOI) placed over the heart (cube, 10 mm); (B) VOI set over abdominal vena cava/aorta region with a cuboid (5 × 5 × 15 mm); and (C) with 1 × 1 × 1 mm voxels on five consecutive slices. A calculated scaling factor (α) was used to correct for partial volume effect; the method of obtaining the total metabolite correction of [¹⁸F]CPFPX for IDIFs was developed. Three IDIFs were validated by comparison with AIF. Validation included the following: visual performance; computing area under the curve (AUC) ratios (IDIF/AIF) of whole-blood curves and parent curves; and the mean distribution volume (V_T) ratios (IDIF/AIF) of A₁ARs calculated by Logan plot and two-tissue compartment model.

Results

Compared with the AIF, the IDIF with VOI over heart showed the best performance among the three IDIFs after scaling by 1.77 (α) in terms of visual analysis, AUC ratios (IDIF/AIF; whole-blood AUC ratio, 1.03 ± 0.06; parent curve AUC ratio, 1.01 ± 0.10) and V_T ratios (IDIF/AIF; Logan V_T ratio, 1.00 ± 0.17; two-tissue compartment model V_T ratio, 1.00 ± 0.13) evaluation. The A₁ARs distribution of average parametric images was in good accordance to autoradiography of the mouse brain.

Conclusion

The proposed study provides evidence that IDIF with VOI over heart can replace AIF effectively for quantification of A₁ARs using PET and [¹⁸F]CPFPX in mice brains.

Determination of a pharmacokinetic model for [11C]-acetate in brown adipose tissue

Background

[¹¹C]-acetate positron emission tomography is used to assess oxidative metabolism in various tissues including the heart, tumor, and brown adipose tissue. For brown adipose tissue, a monoexponential decay model is commonly employed. However, no systematic assessment of kinetic models has been performed to validate this model or others.

The monoexponential decay model and various compartmental models were applied to data obtained before and during brown adipose tissue activation by cold exposure in healthy men. Quality of fit was assessed visually and by analysis of residuals, including the Akaike information criterion. Stability and accuracy of compartmental models were further assessed through simulations, along with sensitivity and identifiability of kinetic parameters.

Results

Differences were noted in the arterial input function between the warm and cold conditions. These differences are not taken into account by the monoexponential decay model. They are accounted for by compartmental models, but most models proved too complex to be stable. Two and three-tissue models with no more than four distinct kinetic parameters, including blood volume fraction, provided the best compromise between fit quality and stability/accuracy.

Conclusion

For healthy men, a three-tissue model with four kinetic parameters, similar to a heart [¹¹C]-palmitate model seems the most appropriate based on model stability and its ability to describe the main [¹¹C]-acetate pathways in BAT cells. Further studies are required to validate this model in women and people with metabolic disorders.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0497-6) contains supplementary material, which is available to authorized users.

Assessment of artery calcification in atherosclerosis with dynamic 18F-FDG-PET/CT imaging in elderly subjects

Glucose metabolism in atherosclerotic arteries has been shown to be an indicator of inflammation, which might be a precursor of plaque rupture. In this prospective study, we assessed the correlation between artery calcification and glucose metabolism by means of 18F-FDG PET/CT imaging in elderly subjects. Nineteen elderly subjects, with age ranging from 65 to 85 years, underwent CT and dynamic 18F-FDG-PET imaging. The artery calcification was determined with a threshold of 130 Hounsfield units. Intensity of calcification and ratio of calcification area to total artery area were classified in four sequential classes from CT images. The CT artery images were also classified as having single or multi-spot calcifications. Their respective glucose metabolism was assessed with fractional uptake rate (FUR). Factor analysis was used in this study to separate blood images from tissue to extract the blood time activity curves for FUR calculations. The artery images in PET data were corrected for partial volume effect. The total arterial segments analyzed were 1332, with 1085 without calcification (81%), 247 (19%) with calcification, and 94 segments were having multi-spot of calcifications. There was a statistically significant difference in FUR values between non-calcified to calcified segments and between subjects under medication to non-medication when comparing the subjects based on calcification area. No statistically significant differences of FUR were found between single spot as a function of intensity, while in the multi-spots, there was a statistically significant difference for all artery segments. Metabolism activity varies for non-calcified to calcified segments. Based on the metabolic activity represented by FUR, calcifications in multi-spots have different effects than in single spots.

Noninvasive evaluation of fat-carbohydrate metabolic switching in heart and contracting skeletal muscle

The ability of heart and skeletal muscle (SM) to switch between fat and carbohydrate oxidation is of high interest in the study of metabolic diseases and exercise physiology. Positron emission tomography (PET) imaging with the glucose analog 2-[¹⁸F]fluoro-2-deoxy-glucose (¹⁸F-FDG) provides a noninvasive means to quantitate glucose metabolic rates. However, evaluation of fatty acid oxidation (FAO) rates by PET has been limited by the lack of a suitable FAO probe. We have developed a metabolically trapped oleate analog, ( Z)-18-[¹⁸F]fluoro-4-thia-octadec-9-enoate (¹⁸F-FTO), and investigated the feasibility of using ¹⁸F-FTO and ¹⁸F-FDG to measure FAO and glucose uptake, respectively, in heart and SM of rats in vivo. To enhance the metabolic rates in SM, the vastus lateralis (VL) muscle was electrically stimulated in fasted rats for 30 min before and 30 min following radiotracer injection. The responses of radiotracer uptake patterns to pharmacological inhibition of FAO were assessed by pretreatment of the rats with the carnitine palmitoyl-transferase-1 (CPT-1) inhibitor sodium 2-[5-(4-chlorophenyl)-pentyl]oxirane-2-carboxylate (POCA). Small-animal PET images and biodistribution data with ¹⁸F-FTO and ¹⁸F-FDG demonstrated profound metabolic switching for energy provision in the myocardium from exogenous fatty acids to glucose in control and CPT-1-inhibited rats, respectively. Uptake of both radiotracers was low in unstimulated SM. In stimulated VL muscle, ¹⁸F-FTO and ¹⁸F-FDG uptakes were increased 4.4- and 28-fold, respectively, and CPT-1 inhibition only affected ¹⁸F-FTO uptake (66% decrease). ¹⁸F-FTO is a FAO-dependent PET probe that may allow assessment of energy substrate metabolic switching in conjunction with ¹⁸F-FDG and other metabolic probes.

Recent advances in the detection of brown adipose tissue in adult humans: a review

The activation of brown adipose tissue (BAT) is associated with reductions in circulating lipids and glucose in rodents and contributes to energy expenditure in humans indicating the potential therapeutic importance of targetting this tissue for the treatment of a variety of metabolic disorders. In order to evaluate the therapeutic potential of human BAT, a variety of methodologies for assessing the volume and metabolic activity of BAT are utilized. Cold exposure is often utilized to increase BAT activity but inconsistencies in the characteristics of the exposure protocols make it challenging to compare findings. The metabolic activity of BAT in response to cold exposure has most commonly been measured by static positron emission tomography of ¹⁸F-fluorodeoxyglucose in combination with computed tomography (¹⁸F-FDG PET-CT) imaging, but recent studies suggest that under some conditions this may not always reflect BAT thermogenic activity. Therefore, recent studies have used alternative positron emission tomography and computed tomography (PET-CT) imaging strategies and radiotracers that may offer important insights. In addition to PET-CT, there are numerous emerging techniques that may have utility for assessing BAT metabolic activity including magnetic resonance imaging (MRI), skin temperature measurements, near-infrared spectroscopy (NIRS) and contrast ultrasound (CU). In this review, we discuss and critically evaluate the various methodologies used to measure BAT metabolic activity in humans and provide a contemporary assessment of protocols which may be useful in interpreting research findings and guiding the development of future studies.

Free fatty acid flux measured using [1-11C]palmitate positron emission tomography and [U-13C]palmitate in humans

PET radiopharmaceuticals can noninvasively measure free fatty acid (FFA) tissue uptake. Investigators often use PET scan-derived data to calculate FFA flux. We tested whether the [1-¹¹C]palmitate PET measures of palmitate flux provide results equivalent to a continuous infusion of [U-¹³C]palmitate. Nine volunteers participated in study 1 to evaluate whether a rapidly (10-20 s) given bolus of [1-¹¹C]palmitate affects calculated flux results. Thirty volunteers participated in study 2, which was identical to study 1 except that the [1-¹¹C]palmitate bolus was given over 1 min. Volunteers in both studies also received a continuous intravenous infusion of [U-¹³C]palmitate. Plasma palmitate concentrations and enrichment were measured by liquid chromatography-mass spectrometry. The PET/CT images were analyzed on a workstation running PMOD. Palmitate flux was estimated using PET time-activity curve (TAC) data from regions of interest in the left ventricle (LV) and aorta both with and without hybrid TACs that employed the ¹¹CO₂-corrected data for the first 5 min and the ¹¹CO₂-corrected blood radioactivity for the remainder of the PET scan. Palmitate flux in study 1 measured with PET [1-¹¹C]palmitate and [U-¹³C]palmitate were not correlated, and the PET [1-¹¹C]palmitate flux was significantly less than the [U-¹³C]palmitate measured flux. In study 2, the palmitate flux using PET [1-¹¹C]palmitate hybrid LV models provided closer mean estimates of [U-¹³C]palmitate measured flux. The best PET calculation approaches predicted 64% of the interindividual variance in [U-¹³C]palmitate measured flux. Palmitate kinetics measured using [1-¹¹C]palmitate/PET do not provide the same palmitate kinetic results as the continuous infusion [U-¹³C]palmitate approach.

Molecular imaging of postprandial metabolism

Disordered postprandial metabolism of energy substrates is one of the main defining features of prediabetes and contributes to the development of several chronic diseases associated with obesity, such as type 2 diabetes and cardiovascular diseases. Postprandial energy metabolism has been studied using classical isotopic tracer approaches that are limited by poor access to splanchnic metabolism and highly dynamic and complex exchanges of energy substrates involving multiple organs and systems. Advances in noninvasive molecular imaging modalities, such as PET and MRI/magnetic resonance spectroscopy (MRS), have recently allowed important advances in our understanding of postprandial energy metabolism in humans. The present review describes some of these recent advances, with particular focus on glucose and fatty acid metabolism in the postprandial state, and discusses current gaps in knowledge and new perspectives of application of PET and MRI/MRS for the investigation and treatment of human metabolic diseases.

11C-L-methyl methionine dynamic PET/CT of skeletal muscle: response to protein supplementation compared to L-[ring 13C6] phenylalanine infusion with serial muscle biopsy

Objective

The objective of this study was to determine if clinical dynamic PET/CT imaging with ¹¹C-L-methyl-methionine (¹¹C-MET) in healthy older women can provide an estimate of tissue-level post-absorptive and post-prandial skeletal muscle protein synthesis that is consistent with the more traditional method of calculating fractional synthesis rate (FSR) of muscle protein synthesis from skeletal muscle biopsies obtained during an infusion of L-[ring ¹³C₆] phenylalanine (¹³C₆-Phe).

Methods

Healthy older women (73 ± 5 years) completed both dynamic PET/CT imaging with ¹¹C-MET and a stable isotope infusion of ¹³C₆-Phe with biopsies to measure the skeletal muscle protein synthetic response to 25 g of a whey protein supplement. Graphical estimation of the Patlak coefficient K_i from analysis of the dynamic PET/CT images was employed as a measure of incorporation of 11 C-MET in the mid-thigh muscle bundle.

Results

Post-prandial values [mean ± standard error of the mean (SEM)] were higher than post-absorptive values for both K_i (0.0095 ± 0.001 vs. 0.00785 ± 0.001 min⁻¹, p < 0.05) and FSR (0.083 ± 0.008 vs. 0.049 ± 0.006%/h, p < 0.001) in response to the whey protein supplement. The percent increase in K_i and FSR in response to the whey protein supplement was significantly correlated (r = 0.79, p = 0.015).

Conclusions

Dynamic PET/CT imaging with ¹¹C-MET provides an estimate of the post-prandial anabolic response that is consistent with a traditional, invasive stable isotope, and muscle biopsy approach. These results support the potential future use of ¹¹C-MET imaging as a non-invasive method for assessing conditions affecting skeletal muscle protein synthesis.

Inhibition of Intracellular Triglyceride Lipolysis Suppresses Cold-Induced Brown Adipose Tissue Metabolism and Increases Shivering in Humans

Indirect evidence from human studies suggests that brown adipose tissue (BAT) thermogenesis is fueled predominantly by fatty acids hydrolyzed from intracellular triglycerides (TGs). However, no direct experimental evidence to support this assumption currently exists in humans. The aim of this study was to determine the role of intracellular TG in BAT thermogenesis, in cold-exposed men. Using positron emission tomography with ¹¹C-acetate and ¹⁸F-fluorodeoxyglucose, we showed that oral nicotinic acid (NiAc) administration, an inhibitor of intracellular TG lipolysis, suppressed the cold-induced increase in BAT oxidative metabolism and glucose uptake, despite no difference in BAT blood flow. There was a commensurate increase in shivering intensity and shift toward a greater reliance on glycolytic muscle fibers without modifying total heat production. Together, these findings show that intracellular TG lipolysis is critical for BAT thermogenesis and provides experimental evidence for a reciprocal role of BAT thermogenesis and shivering in cold-induced thermogenesis in humans.

Dietary fatty acid metabolism of brown adipose tissue in cold-acclimated men

In rodents, brown adipose tissue (BAT) plays an important role in producing heat to defend against the cold and can metabolize large amounts of dietary fatty acids (DFA). The role of BAT in DFA metabolism in humans is unknown. Here we show that mild cold stimulation (18 °C) results in a significantly greater fractional DFA extraction by BAT relative to skeletal muscle and white adipose tissue in non-cold-acclimated men given a standard liquid meal containing the long-chain fatty acid PET tracer, 14(R,S)-[¹⁸F]-fluoro-6-thia-heptadecanoic acid (¹⁸FTHA). However, the net contribution of BAT to systemic DFA clearance is comparatively small. Despite a 4-week cold acclimation increasing BAT oxidative metabolism 2.6-fold, BAT DFA uptake does not increase further. These findings show that cold-stimulated BAT can contribute to the clearance of DFA from circulation but its contribution is not as significant as the heart, liver, skeletal muscles or white adipose tissues.

Brown adipose tissue (BAT) takes up and burns fatty acids for thermogenesis in mice. Here the authors use PET to show that, in humans, cold stimulation increases BAT dietary fatty acid uptake from plasma and oxidative metabolism, although, unlike mice, human BAT takes up less fatty acids than other metabolic tissues.

Cerebral metabolic rate of glucose quantification with the aortic image-derived input function and Patlak method: numerical and patient data evaluation

PURPOSE

Quantitative maps of cerebral metabolic rate of glucose (CMRGlu) from fluorine-18 fluorodeoxyglucose-PET are useful in brain studies, but are challenging to acquire because of technical constraints, which hinder their use in clinical routine. Aortic image-derived input functions (IDIFs) combined with Sokoloff's method were proposed as a suitable solution. However, Sokoloff's method requires the use of standard kinetic constants, which may produce biased estimates. Patlak's method would be more appropriate, but concern can arise when used with an aortic IDIF from unavailability of a complete brain curve acquired starting from injection. The aim of this study was to develop a CMRGlu quantification technique that combines Patlak's method with aortic IDIFs in a clinical setting.

MATERIALS AND METHODS

A simple acquisition protocol for aortic IDIF measurement was developed and applied on a sample of patients with different degrees of hypometabolism (one healthy control, four patients with a neurodegenerative condition, and one coma patient). CMRGlu estimates in vivo were obtained with both the Sokoloff method and the Patlak method. Computer simulations were performed to assess the causes of bias affecting Sokoloff and Patlak estimates and interpret the results obtained in patients.

RESULTS

Simulations showed that Sokoloff's method is less stable than Patlak's method as the extent of bias changed across different physiological states, potentially leading to misinterpretation of clinical data. In clinical patients, Sokoloff and Patlak estimates were correlated on the whole, but deviations emerged for critical physiological states.

CONCLUSION

CMRGlu quantification with the Patlak method and aortic IDIF is feasible, easy to implement in clinical practice, and superior to Sokoloff's method from a personalized medicine perspective.

Fully automated calculation of image-derived input function in simultaneous PET/MRI in a sheep model

Background

Obtaining the arterial input function (AIF) from image data in dynamic positron emission tomography (PET) examinations is a non-invasive alternative to arterial blood sampling. In simultaneous PET/magnetic resonance imaging (PET/MRI), high-resolution MRI angiographies can be used to define major arteries for correction of partial-volume effects (PVE) and point spread function (PSF) response in the PET data. The present study describes a fully automated method to obtain the image-derived input function (IDIF) in PET/MRI. Results are compared to those obtained by arterial blood sampling.

Methods

To segment the trunk of the major arteries in the neck, a high-resolution time-of-flight MRI angiography was postprocessed by a vessel-enhancement filter based on the inertia tensor. Together with the measured PSF of the PET subsystem, the arterial mask was used for geometrical deconvolution, yielding the time-resolved activity concentration averaged over a major artery. The method was compared to manual arterial blood sampling at the hind leg of 21 sheep (animal stroke model) during measurement of blood flow with O15-water. Absolute quantification of activity concentration was compared after bolus passage during steady state, i.e., between 2.5- and 5-min post injection. Cerebral blood flow (CBF) values from blood sampling and IDIF were also compared.

Results

The cross-calibration factor obtained by comparing activity concentrations in blood samples and IDIF during steady state is 0.98 ± 0.10. In all examinations, the IDIF provided a much earlier and sharper bolus peak than in the time course of activity concentration obtained by arterial blood sampling. CBF using the IDIF was 22 % higher than CBF obtained by using the AIF yielded by blood sampling.

Conclusions

The small deviation between arterial blood sampling and IDIF during steady state indicates that correction of PVE and PSF is possible with the method presented. The differences in bolus dynamics and, hence, CBF values can be explained by the different sampling locations (hind leg vs. major neck arteries) with differences in delay/dispersion. It will be the topic of further work to test the method on humans with the perspective of replacing invasive blood sampling by an IDIF using simultaneous PET/MRI.

Selective Impairment of Glucose but Not Fatty Acid or Oxidative Metabolism in Brown Adipose Tissue of Subjects With Type 2 Diabetes

Spontaneous glucose uptake by brown adipose tissue (BAT) is lower in overweight or obese individuals and in diabetes. However, BAT metabolism has not been previously investigated in patients with type 2 diabetes during controlled cold exposure. Using positron emission tomography with (11)C-acetate, (18)F-fluoro-deoxyglucose ((18)FDG), and (18)F-fluoro-thiaheptadecanoic acid ((18)FTHA), a fatty acid tracer, BAT oxidative metabolism and perfusion and glucose and nonesterified fatty acid (NEFA) turnover were determined in men with well-controlled type 2 diabetes and age-matched control subjects under experimental cold exposure designed to minimize shivering. Despite smaller volumes of (18)FDG-positive BAT and lower glucose uptake per volume of BAT compared with young healthy control subjects, cold-induced oxidative metabolism and NEFA uptake per BAT volume and an increase in total body energy expenditure did not differ in patients with type 2 diabetes or their age-matched control subjects. The reduction in (18)FDG-positive BAT volume and BAT glucose clearance were associated with a reduction in BAT radiodensity and perfusion. (18)FDG-positive BAT volume and the cold-induced increase in BAT radiodensity were associated with an increase in systemic NEFA turnover. These results show that cold-induced NEFA uptake and oxidative metabolism are not defective in type 2 diabetes despite reduced glucose uptake per BAT volume and BAT "whitening."

Effect of Sex and Impaired Glucose Tolerance on Organ-Specific Dietary Fatty Acid Metabolism in Humans

Oral 14(R,S)-[(18)F]-fluoro-6-thia-heptadecanoic acid was used to determine whether an increase in cardiac dietary fatty acid (DFA) metabolism in impaired glucose tolerance (IGT) is different in men and women. Myocardial DFA partitioning after 6 h was higher in IGT versus control subjects (P = 0.006) in both men (2.14 [95% CI 1.70-2.18] vs. 1.28 standard uptake value [SUV] units [0.80-1.76]) and women (1.95 [1.57-2.33] vs. 1.64 SUV units [1.32-1.96]) without difference between sexes. Myocardial DFA fractional uptake (Ki) between time 90 and 120 min postprandially was also higher in IGT versus control subjects (P < 0.001) in men (0.063 [0.032-0.095] vs. 0.016 min(-1) [0.007-0.025]) and women (0.050 [0.024-0.077] vs. 0.030 min(-1) [0.013-0.047]) without significant sex difference. Men had higher net myocardial DFA uptake between time 90 and 120 min driven by higher chylomicron-triglyceride (TG) levels. IGT-associated increased cardiac DFA partitioning was directly related to obesity in women, whereas it was associated with IGT per se in men. We conclude that early cardiac DFA uptake is higher in men driven by change in postprandial chylomicron-TG level but that increase in 6-h postprandial cardiac DFA partitioning nevertheless occurs with IGT both in men and women.

Quantitative amyloid imaging using image-derived arterial input function

Amyloid PET imaging is an indispensable tool widely used in the investigation, diagnosis and monitoring of Alzheimer’s disease (AD). Currently, a reference region based approach is used as the mainstream quantification technique for amyloid imaging. This approach assumes the reference region is amyloid free and has the same tracer influx and washout kinetics as the regions of interest. However, this assumption may not always be valid. The goal of this work is to evaluate an amyloid imaging quantification technique that uses arterial region of interest as the reference to avoid potential bias caused by specific binding in the reference region. 21 participants, age 58 and up, underwent Pittsburgh compound B (PiB) PET imaging and MR imaging including a time-of-flight (TOF) MR angiography (MRA) scan and a structural scan. FreeSurfer based regional analysis was performed to quantify PiB PET data. Arterial input function was estimated based on coregistered TOF MRA using a modeling based technique. Regional distribution volume (V_T) was calculated using Logan graphical analysis with estimated arterial input function. Kinetic modeling was also performed using the estimated arterial input function as a way to evaluate PiB binding (DVR_kinetic) without a reference region. As a comparison, Logan graphical analysis was also performed with cerebellar cortex as reference to obtain DVR_REF. Excellent agreement was observed between the two distribution volume ratio measurements (r>0.89, ICC>0.80). The estimated cerebellum V_T was in line with literature reported values and the variability of cerebellum V_T in the control group was comparable to reported variability using arterial sampling data. This study suggests that image-based arterial input function is a viable approach to quantify amyloid imaging data, without the need of arterial sampling or a reference region. This technique can be a valuable tool for amyloid imaging, particularly in population where reference normalization may not be accurate.

Image derived input function for [18F]-FEPPA: application to quantify translocator protein (18 kDa) in the human brain

In [18F]-FEPPA positron emission topography (PET) imaging, automatic blood sampling system (ABSS) is currently the gold standard to obtain the blood time activity curve (TAC) required to extract the input function (IF). Here, we compare the performance of two image-based methods of IF extraction to the ABSS gold standard method for the quantification of translocator protein (TSPO) in the human brain. The IFs were obtained from a direct delineation of the internal carotid signal (CS) and a new concept of independent component analysis (ICA). PET scans were obtained from 18 healthy volunteers. The estimated total distribution volume (V(T)) by CS-IF and ICA-IF were compared to the reference V(T) obtained by ABSS-IF in the frontal and temporal cortex, cerebellum, striatum and thalamus regions. The V(T) values estimated using ICA-IF were more reliable than CS-IF for all brain regions. Specifically, the slope regression in the frontal cortex with ICA-IF was r² = 0.91 (p<0.05), and r² = 0.71 (p<0.05) using CS-IF.

Contributions of white and brown adipose tissues and skeletal muscles to acute cold-induced metabolic responses in healthy men

KEY POINTS

Both brown adipose tissue (BAT) and skeletal muscle activation contribute to the metabolic response of acute cold exposure in healthy men even under minimal shivering. Activation of adipose tissue intracellular lipolysis is associated with BAT metabolic response upon acute cold exposure in healthy men. Although BAT glucose uptake per volume of tissue is important, the bulk of glucose turnover during cold exposure is mediated by skeletal muscle metabolic activation even when shivering is minimized.

ABSTRACT

Cold exposure stimulates the sympathetic nervous system (SNS), triggering the activation of cold-defence responses and mobilizing substrates to fuel the thermogenic processes. Although these processes have been investigated independently, the physiological interaction and coordinated contribution of the tissues involved in producing heat or mobilizing substrates has never been investigated in humans. Using [U-(13)C]-palmitate and [3-(3)H]-glucose tracer methodologies coupled with positron emission tomography using (11)C-acetate and (18)F-fluorodeoxyglucose, we examined the relationship between whole body sympathetically induced white adipose tissue (WAT) lipolysis and brown adipose tissue (BAT) metabolism and mapped the skeletal muscle shivering and metabolic activation pattern during a mild, acute cold exposure designed to minimize shivering response in 12 lean healthy men. Cold-induced increase in whole-body oxygen consumption was not independently associated with BAT volume of activity, BAT oxidative metabolism, or muscle metabolism or shivering intensity, but depended on the sum of responses of these two metabolic tissues. Cold-induced increase in non-esterified fatty acid (NEFA) appearance rate was strongly associated with the volume of metabolically active BAT (r = 0.80, P = 0.005), total BAT oxidative metabolism (r = 0.70, P = 0.004) and BAT glucose uptake (r = 0.80, P = 0.005), but not muscle glucose metabolism. The total glucose uptake was more than one order of magnitude greater in skeletal muscles compared to BAT during cold exposure (674 ± 124 vs. 12 ± 8 μmol min(-1), respectively, P < 0.001). Glucose uptake demonstrated that deeper, centrally located muscles of the neck, back and inner thigh were the greatest contributors of muscle glucose uptake during cold exposure due to their more important shivering response. In summary, these results demonstrate for the first time that the increase in plasma NEFA appearance from WAT lipolysis is closely associated with BAT metabolic activation upon acute cold exposure in healthy men. In humans, muscle glucose utilization during shivering contributes to a much greater extent than BAT to systemic glucose utilization during acute cold exposure.

Kinetic modeling in PET imaging of hypoxia

Tumor hypoxia is associated with increased therapeutic resistance leading to poor treatment outcome. Therefore the ability to detect and quantify intratumoral oxygenation could play an important role in future individual personalized treatment strategies. Positron Emission Tomography (PET) can be used for non-invasive mapping of tissue oxygenation in vivo and several hypoxia specific PET tracers have been developed. Evaluation of PET data in the clinic is commonly based on visual assessment together with semiquantitative measurements e.g. standard uptake value (SUV). However, dynamic PET contains additional valuable information on the temporal changes in tracer distribution. Kinetic modeling can be used to extract relevant pharmacokinetic parameters of tracer behavior in vivo that reflects relevant physiological processes. In this paper, we review the potential contribution of kinetic analysis for PET imaging of hypoxia.

[(11)C]-Acetoacetate PET imaging: a potential early marker for cardiac heart failure

The ketone body acetoacetate could be used as an alternate nutrient for the heart, and it also has the potential to improve cardiac function in an ischemic-reperfusion model or reduce the mitochondrial production of oxidative stress involved in cardiotoxicity. In this study, [(11)C]-acetoacetate was investigated as an early marker of intracellular damage in heart failure.

METHODS

A rat cardiotoxicity heart failure model was induced by doxorubicin, Dox(+). [(14)C]-Acetoacetate, a non-positron (β-) emitting radiotracer, was used to characterize the arterial blood input function and myocardial mitochondrial uptake. Afterward, [(11)C]-acetoacetate (β+) myocardial PET images were obtained for kinetic analysis and heart function assessment in control Dox(-) (n=15) and treated Dox(+) (n=6) rats. The uptake rate (K1) and myocardial clearance rate (k2or kmono) were extracted.

RESULTS

[(14)C]-Acetoacetate in the blood was increased in Dox(+), from 2 min post-injection until the last withdrawal point when the heart was harvested, as well as the uptake in the heart and myocardial mitochondria (unpaired t-test, p <0.05). PET kinetic analysis of [(11)C]-acetoacetate showed that rate constants K1, k2 and kmono were decreased in Dox(+) (p <0.05) combined with a reduction of 24% of the left ventricular ejection fraction (p <0.001).

CONCLUSION

Radioactive acetoacetate ex vivo analysis [(14)C], and in vivo kinetic [(11)C] studies provided evidence that [(11)C]-acetoacetate can assess heart failure Dox(+). Contrary to myocardial flow reserve (rest-stress protocol), [(11)C]-acetoacetate can be used to assess reduced kinetic rate constants without requirement of hyperemic stress response. The proposed [(11)C]-acetoacetate cardiac radiotracer in the investigation of heart disease is novel and paves the way to a potential role for [(11)C]-acetoacetate in cardiac pathophysiology.