Blood Input Function Research Articles

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.

A blood-free modeling approach for the quantification of the blood-to-brain tracer exchange in TSPO PET imaging.

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

Non-Invasive Quantification of the Brain [¹⁸F]FDG-PET Using Inferred Blood Input Function Learned From Total-Body Data With Physical Constraint.

Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for [Formula: see text] ( [Formula: see text]. In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.

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

Repeatability of [15O]H2O PET imaging for lower extremity skeletal muscle perfusion: a test–retest study

Background[15O]H2O PET/CT allows noninvasive quantification of tissue perfusion and can potentially play a future role in the diagnosis and treatment of peripheral artery disease. We aimed to evaluate the reliability of dynamic [15O]H2O PET imaging for measuring lower extremity skeletal muscle perfusion. Ten healthy participants underwent same-day test–retest study with six dynamic [15O]H2O PET scans of lower legs and feet. Manual volume-of-interests were drawn in skeletal muscles, and PET time activity curves were extracted. K1 values (mL/min/100 mL) were estimated using a single-tissue compartment model (1TCM), autoradiography (ARG), and parametric imaging with blood input functions obtained from separate heart scans.ResultsResting perfusion values in the muscle groups of the lower legs ranged from 1.18 to 5.38 mL/min/100 mL (ARG method). In the muscle groups of the feet, perfusion values ranged from 0.41 to 3.41 mL/min/100 mL (ARG method). Test–retest scans demonstrated a strong correlation and good repeatability for skeletal muscle perfusion with an intraclass correlation coefficient (ICC) of 0.88 and 0.87 and a repeatability coefficient of 34% and 53% for lower legs and feet, respectively. An excellent correlation was demonstrated when comparing volume-of-interest-based methods (1TCM and ARG) (lower legs: ICC = 0.96, feet: ICC = 0.99). Parametric images were in excellent agreement with the volume-of-interest-based ARG method (lower legs: ICC = 0.97, feet: ICC = 0.98).ConclusionParametric images and volume-of-interest-based methods demonstrated comparable resting perfusion values in the lower legs and feet of healthy individuals. The largest variation was seen between individuals, whereas a smaller variation was seen between muscle groups. Repeated measurements of resting blood flow yielded a strong overall correlation for all methods.

Accurate brain pharmacokinetic parametric imaging using the blood input function extracted from the cavernous sinus.

Brain pharmacokinetic parametric imaging based on dynamic positron emission tomography (PET) scan is valuable in the diagnosis of brain tumor and neurodegenerative diseases. For short-axis PET system, standard blood input function (BIF) of the descending aorta is not acquirable during the dynamic brain scan. BIF extracted from the intracerebral vascular is inaccurate, making the brain parametric imaging task challenging. This study introduces a novel technique tailored for brain pharmacokinetic parameter imaging in short-axis PET in which the head BIF (hBIF) is acquired from the cavernous sinus. The proposed method optimizes the hBIF within the Patlak model via data fitting, curve correction and Patlak graphical model rewriting. The proposed method was built and evaluated using dynamic PET datasets of 67 patients acquired by uEXPLORER PET/CT, among which 64 datasets were used for data fitting and model construction, and 3 were used for method testing; using cross-validation, a total of 15 patient datasets were finally used to test the model. The performance of the new method was evaluated via visual inspection, root-mean-square error (RMSE) measurements and VOI-based accuracy analysis using linear regression and Person's correlation coefficients (PCC). Compared to directly using the cavernous sinus BIF directly for parameter imaging, the new method achieves higher accuracy in parametric analysis, including the generation of Patlak plots closer to the standard plots, better visual effects and lower RMSE values in the Ki (P = 0.0012) and V (P = 0.0042) images. VOI-based analysis shows regression lines with slopes closer to 1 (P = 0.0019 for Ki ) and smaller intercepts (P = 0.0085 for V). The proposed method is capable of achieving accurate brain pharmacokinetic parametric imaging using cavernous sinus BIF with short-axis PET scan. This may facilitate the application of this imaging technology in the clinical diagnosis of brain diseases.

Automated dynamic motion correction improves repeatability and reproducibility of myocardial blood flow quantification with rubidium-82 PET imaging

BackgroundPatient motion reduces the accuracy of PET myocardial blood flow (MBF) measurements. This study evaluated the effect of automatic motion correction on test-retest repeatability and inter-observer variability in a clinically relevant population. MethodsPatients with known or suspected CAD underwent repeat rest 82Rb PET scans within minutes as part of their scheduled rest-stress perfusion study. Two trained observers evaluated the presence of heart motion in each scan. Global LV and per-vessel MBF were computed from the dynamic rest images before and after automatic motion correction. Test-retest and inter-observer variability were assessed using intra-class correlation and Bland-Altman analysis. Results140 pairs of test-retest scans were included, with visual motion noted in 18%. Motion correction decreased the global MBF values by 3.5% (0.80 ± 0.24 vs 0.82 ± 0.25 mL⋅min−1⋅g−1; P < 0.001) suggesting that the blood input function was underestimated in cases with patient motion. Test-retest repeatability of global MBF improved by 9.7% (0.25 vs 0.28 mL⋅min−1⋅g−1; P < 0.001) and inter-observer repeatability was improved by 7.1% (0.073 vs 0.079 mL⋅min−1⋅g−1; P = 0.012). There was a marked impact on both test-retest repeatability as well as inter-observer repeatability in the LCX territory, with improvements of 16.5% (0.30 vs 0.36 mL⋅min−1⋅g−1; P < 0.0000) and 18.4% (0.13 vs 0.16 mL⋅min−1⋅g−1; P < 0.001), respectively. ConclusionAutomatic motion correction improved test-retest repeatability and reduced differences between observers.

Direct dynamic tomographic reconstruction without explicit blood input function

With dynamic positron emission tomography (PET), we study the metabolic processes by monitoring the uptake of a radioactive tracer. The goal is to reconstruct the time-activity curves (TACs) of the voxels, from which the relevant kinetic parameters can be obtained. These curves are assumed to have the algebraic form of a pre-determined kinetic model. Plausible algebraic forms have a limited number of free parameters and thus can be used even for low-statistic measurements. The kinetic model typically involves the amount of radiotracer in the blood, which should be determined either by direct measurement or by extracting it from the PET data using image-derived or model-based methods. However, the direct measurement of the blood concentration is complicated, and the results of the image-derived and the model-based methods are also not reliable because of the partial volume effect and the unknown fraction of blood in the voxels. Moreover, in direct dynamic tomography, the kinetic model is fit from the very beginning of the reconstruction, thus the blood input function is needed even before the image-derived or model-based approaches can provide it. In this paper, we propose a method that is based on compartmental modeling and the Feng blood input function model defined by a fourth-order exponential equation with a pair of repeated eigenvalues, but does not require the blood input function and the fraction of blood parameters explicitly. Thus, the model can be used in cases when we wish to have the robustness and advantages of compartmental modeling, but no blood input function measurement is available. The test results show that the added error in the TACs of not knowing the blood input is reduced in comparison with the spline-based TAC representation. The method can be applied in reference tissue analysis and in image-derived input function approaches.

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 modeling 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 (Ki) 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 Ki 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.

Total-Body PET Multiparametric Imaging of Cancer Using a Voxelwise Strategy of Compartmental Modeling.

Quantitative dynamic PET with compartmental modeling has the potential to enable multiparametric imaging and more accurate quantification than static PET imaging. Conventional methods for parametric imaging commonly use a single kinetic model for all image voxels and neglect the heterogeneity of physiologic models, which can work well for single-organ parametric imaging but may significantly compromise total-body parametric imaging on a scanner with a long axial field of view. In this paper, we evaluate the necessity of voxelwise compartmental modeling strategies, including time delay correction (TDC) and model selection, for total-body multiparametric imaging. Methods: Ten subjects (5 patients with metastatic cancer and 5 healthy volunteers) were scanned on a total-body PET/CT system after injection of 370 MBq of 18F-FDG. Dynamic data were acquired for 60 min. Total-body parametric imaging was performed using 2 approaches. One was the conventional method that uses a single irreversible 2-tissue-compartment model with and without TDC. The second approach selects the best kinetic model from 3 candidate models for individual voxels. The differences between the 2 approaches were evaluated for parametric imaging of microkinetic parameters and the 18F-FDG net influx rate, KiResults: TDC had a nonnegligible effect on kinetic quantification of various organs and lesions. The effect was larger in lesions with a higher blood volume. Parametric imaging of Ki with the standard 2-tissue-compartment model introduced vascular-region artifacts, which were overcome by the voxelwise model selection strategy. Conclusion: The time delay and appropriate kinetic model vary in different organs and lesions. Modeling of the time delay of the blood input function and model selection improved total-body multiparametric imaging.

Dynamic FDG-PET in localization of focal epilepsy: A pilot study

Epilepsy surgery remains underutilized, in part because non-invasive methods of potential seizure foci localization are inadequate. We used high-resolution, parametric quantification from dynamic 2-[18F] fluoro-2-deoxy-d-glucose positron emission tomography (dFDG-PET) imaging to locate hypometabolic foci in patients whose standard clinical static PET images were normal. We obtained dFDG-PET brain images with simultaneous EEG in a one-hour acquisition on seven patients with no MRI evidence of focal epilepsy to record uptake and focal radiation decay. Images were attenuation- and motion-corrected and co-registered with high-resolution T1-weighted patient MRI and segmented into 18 regions of interest (ROI) per hemisphere. Tracer uptake was calibrated with a model corrected blood input function with partial volume (PV) corrections to generate tracer parametric maps compared between mean radiation values between hemispheres with z-scores. We identified ROI with the lowest negative z scores (<−1.65 SD) as hypometabolic. Dynamic 2-[18F] fluoro-2-deoxy-d-glucose positron emission tomography ( found focal regions of altered metabolism in all cases in which standard clinical FDG-PET found no abnormalities. This pilot study of dynamic FDG-PET suggests that further research is merited to evaluate whether glucose dynamics offer improved clinical utility for localization of epileptic foci over standard static techniques.

Dynamic 18F-FDG PET imaging of liver lesions: evaluation of a two-tissue compartment model with dual blood input function

BackgroundDynamic PET with kinetic modeling was reported to be potentially helpful in the assessment of hepatic malignancy. In this study, a kinetic modeling analysis was performed on hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) from dynamic FDG positron emission tomography/computer tomography (PET/CT) scans.MethodsA reversible two-tissue compartment model with dual blood input function, which takes into consideration the blood supply from both hepatic artery and portal vein, was used for accurate kinetic modeling of liver dynamic 18F-FDG PET imaging. The blood input functions were directly measured as the mean values over the VOIs on descending aorta and portal vein respectively. And the contribution of hepatic artery to the blood input function was optimization-derived in the process of model fitting. The kinetic model was evaluated using dynamic PET data acquired on 24 patients with identified hepatobiliary malignancy. 38 HCC or ICC identified lesions and 24 healthy liver regions were analyzed.ResultsResults showed significant differences in kinetic parameters {K}_{1}-{k}_{4}, blood supplying fraction {f}_{A}, and metabolic rate constant {K}_{i} between malignant lesions and healthy liver tissue. And significant differences were also observed in {K}_{1}, {k}_{3}, {f}_{A} and {K}_{i} between HCC and ICC lesions. Further investigations of the effect of SUV measurements on the derived kinetic parameters were conducted. And results showed comparable effectiveness of the kinetic modeling using either SUVmean or SUVmax measurements.ConclusionsDynamic 18F-FDG PET imaging with optimization-derived hepatic artery blood supply fraction dual-blood input function kinetic modeling can effectively distinguish malignant lesions from healthy liver tissue, as well as HCC and ICC lesions.

Model Corrected Blood Input Function to Compute Cerebral FDG Uptake Rates From Dynamic Total-Body PET Images of Rats in vivo.

Recently, we developed a three-compartment dual-output model that incorporates spillover (SP) and partial volume (PV) corrections to simultaneously estimate the kinetic parameters and model-corrected blood input function (MCIF) from dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) images of mouse heart in vivo. In this study, we further optimized this model and utilized the estimated MCIF to compute cerebral FDG uptake rates, Ki, from dynamic total-body FDG PET images of control Wistar–Kyoto (WKY) rats and compared to those derived from arterial blood sampling in vivo. Dynamic FDG PET scans of WKY rats (n = 5), fasted for 6 h, were performed using the Albira Si Trimodal PET/SPECT/CT imager for 60 min. Arterial blood samples were collected for the entire imaging duration and then fitted to a seven-parameter function. The 60-min list mode PET data, corrected for attenuation, scatter, randoms, and decay, were reconstructed into 23 time bins. A 15-parameter dual-output model with SP and PV corrections was optimized with two cost functions to compute MCIF. A four-parameter compartment model was then used to compute cerebral Ki. The computed area under the curve (AUC) and Ki were compared to that derived from arterial blood samples. Experimental and computed AUCs were 1,893.53 ± 195.39 kBq min/cc and 1,792.65 ± 155.84 kBq min/cc, respectively (p = 0.76). Bland–Altman analysis of experimental vs. computed Ki for 35 cerebral regions in WKY rats revealed a mean difference of 0.0029 min−1 (~13.5%). Direct (AUC) and indirect (Ki) comparisons of model computations with arterial blood sampling were performed in WKY rats. AUC and the downstream cerebral FDG uptake rates compared well with that obtained using arterial blood samples. Experimental vs. computed cerebral Ki for the four super regions including cerebellum, frontal cortex, hippocampus, and striatum indicated no significant differences.

In vivo Characterization of Four 18F-Labeled S1PR1 Tracers for Neuroinflammation.

The sphingosine-1-phosphate receptor 1 (S1PR1) is an important biomarker for imaging inflammation in the central nervous system (CNS). Herein, we report our recent evaluation of four 18F-labeled S1PR1 tracers (18F-TZ43113, 18F-TZ35104, 18F-TZ4877, and 18F-TZ4881) in a rat model of multiple sclerosis (MS). MicroPET studies of each tracer's uptake and kinetics were performed in an experimental autoimmune encephalomyelitis (EAE) rat model of MS to quantify upregulated S1PR1 expression in the lumbar spinal cord of EAE rats. Western blot analysis was conducted to confirm the differences in the expression of S1PR1 protein level between EAE and sham rats. Radiometabolite analysis was performed for the most promising candidate in rats. All four S1PR1 tracers detected increased S1PR1 levels in response to neuroinflammation in the lumbar spinal cord of EAE rats, which was supported by western blot results. The ranked order of tracer uptake in rat spinal cord was 18F-TZ4877 > 18F-TZ4881 > 18F-TZ35104 > 18F-TZ43113. 18F-TZ4877 had the highest uptake of the four tracers and showed good kinetic modeling fits in rat spinal cord using an image-based method of arterial blood input function. Radiometabolite analysis of 18F-TZ4877 showed good in vivo stability with no major radiometabolite accumulation in the ratbrain. Among these four new PET tracers, 18F-TZ4877 showed the most favorable profile for assessing S1PR1 expression in the EAE rat model of MS. Further characterization of these radiotracers in other models of neuroinflammation is warranted to identify a promising 18F-labeled tracer for imaging S1PR1 in vivo.

Design and Implementation of Automated Clinical Whole Body Parametric PET With Continuous Bed Motion

We described workflows and methodologies for fully automated parametric imaging using clinical positron emission tomography (PET) systems with continuous bed motion (CBM) which aim to enable physicians to practice parametric PET routinely. The key components of our implemented methods include automatic generation of image-derived blood input functions, accurate tracking of slice imaging time during CBM acquisitions, and parametric image formation with advanced algorithms. Locations of the left ventricle and the descending aorta were automatically detected from high resolution anatomical images and registered to dynamic PET images to generate blood input functions. A method to accurately track dynamic scans and calculate time information for whole body CBM parametric PET was implemented based on bed position tags. This approach of calculating time information based on finely sampled bed position tags can be applied to flexible scan modes with various scan speeds over different body regions. We applied the calculated slice time information for whole body parametric imaging using a linear Patlak model. Parametric images were reconstructed with a nested expectation maximization (EM) algorithm. We demonstrated that parametric images generated by our automated workflow can provide quantitative parameters in addition to standardized uptake value images. We presented results for a uni-direction dynamic scan from a Biograph mCT system and a bi-direction dynamic scan from a Biograph Vision system.

Kinetic models to improve the interpretation of the hepatic transport of 99mTc-mebrofenin

Imaging probes are needed to assess the interplay between liver uptake and biliary excretion, which controls the clearance of many compounds. 99mTc-mebrofenin (MEB) is a radiopharmaceutical for hepatobiliary scintigraphy (HBS) transported through the hepatocyte by OATP transporters at the sinusoidal (blood-liver) interface, and the MRP2 transporter at the canalicular (liver-bile) interface. Our aim was to develop a simple method to separately measure the activity of these two transporter systems in vivo. 99mTc-MEB was injected into control rats (CTL, n = 5) and after a bolus of rifampicin (RIF, 40 mg/kg IV, n = 5), an OATP/MRP2 inhibitor. Planar dynamic acquisitions were performed during 40 min. ROI were drawn over the heart, liver and intestine to obtain time-activity curves (TACs). Three quantification methods were tested: (i) calculation of the transfer rate constants between regions using integration plot analysis, (ii) estimation of the area under the TAC ratio (AUCR) between regions and (iii) compartmental modelling considering a mean volume for organs and a dual blood input function for the liver (hepatic artery and portal vein). (i) RIF significantly decreased the blood-to-liver transfer rate constant (0 to 2 min, linear part) (CTL = 1.5 ± 0.2 min−1, RIF = 0.3 ± 0.0 min−1). However, the liver-to-intestine transfer rate constant could not be accurately determined with this method. (ii) AUCRliver/blood (0 to 2 min) was significantly decreased by RIF (CTL = 2.0 ± 0.2, RIF = 0.8 ± 0.1). A significant decrease was also observed for AUCRintestine/liver (10 to 25 min, CTL = 1.4 ± 0.2, RIF = 0.6 ± 0.2). (iii) These results were confirmed by compartmental modeling, which identified a significant decrease for RIF-treated rats in k1 (blood to hepatocyte transport, CTL = 7.4 ± 1.6 min−1, RIF = 0.9 ± 0.1 min−1), k2 (hepatocyte to blood backflux, CTL = 0.15 ± 0.08 min−1, RIF = 0.01 ± 0.01 min−1) and k3 (hepatocyte to intrahepatic bile-duct transport, CTL = 0.15 ± 0.08 min−1, RIF = 0.05 ± 0.02 min−1) but not for k5 (intrahepatic bile-duct to intestine, CTL = 0.12 ± 0.03 min−1, RIF = 0.64 ± 0.88 min−1). 99mTc-MEB HBS can study the interplay between sinusoidal and canalicular transporters in mediating hepatobiliary clearance in vivo. AUCR is a simple parameter to describe the respective activity of both transporter systems. Compartmental modeling enriched the interpretation of the data and revealed the importance of an additional transport system responsible for the backflux of 99mTc-MEB from liver to blood, most likely MRP3.

In vivo imaging of synaptic loss in Alzheimer’s disease with [18F]UCB-H positron emission tomography

Loss of brain synapses is an early pathological feature of Alzheimer's disease. The current study assessed synaptic loss in vivo with positron emission tomography and an 18F-labelled radiotracer of the synaptic vesicle protein 2A, [18F]UCB-H. Twenty-four patients with mild cognitive impairment or Alzheimer's disease and positive [18F]Flutemetamol amyloid-PET were compared to 19 healthy controls. [18F]UCB-H brain uptake was quantified with Logan graphical analysis using an image-derived blood input function. SPM12 and regions-of-interest (ROI) analyses were used for group comparisons of regional brain distribution volumes and for correlation with cognitive measures. A significant decrease of [18F]UCB-H uptake was observed in several cortical areas (11 to 18% difference) and in the thalamus (16% difference), with the largest effect size in the hippocampus (31% difference). Reduced hippocampal uptake was related to patients' cognitive decline (ROI analysis) and unawareness of memory problems (SPM and ROI analyses). The findings thus highlight predominant synaptic loss in the hippocampus, confirming previous autopsy-based studies and a recent PET study with an 11C-labelled SV2A radiotracer. [18F]UCB-H PET allows to image in vivo synaptic changes in Alzheimer's disease and to relate them to patients' cognitive impairment.

A pitfall of white matter reference regions used in [18F] florbetapir PET: a consideration of kinetics.

Many studies have demonstrated the superiority of white matter (WM) reference regions (RR) in amyloid PET studies in comparison to cerebellar RR. However, the principle behind its improved measurement stability is yet to be elucidated. Our study aimed to determine the origin of WM stability; stability over cerebral blood flow and input function fluctuation or the greater statistical noise in the cerebellum due to its smaller size and its location in the axial periphery of the PET scanner bore. We conducted simulations of [[Formula: see text]F] florbetapir using in-house program varying [Formula: see text] and input function, and adding statistical noise. Our simulations revealed that WM RR were more susceptible to CBF variation and input function fluctuation than cerebellar RR. WM RR did not gave superior measurement stability unless cerebellar statistical noise exceeded 4.55 times that in WM, a figure often surpassed in traditional amyloid PET studies. The greater statistical noise in cerebellum is likely the etiology for improved measurement stability of WM RR. A longitudinal [[Formula: see text]F] florbetapir PET study should be conducted with a long bore PET. It can also be hypothesized that a second scan with the cerebellum in the axial center of a 3D PET, using a cerebellar RR to calculate changes in tracer concentration may improve the measurement stability of longitudinal [[Formula: see text]F] florbetapir studies.

Non-invasive determination of blood input function to compute rate of myocardial glucose uptake from dynamic FDG PET images of rat heart in vivo: comparative study between the inferior vena cava and the left ventricular blood pool with spill over and partial volume corrections

The purpose of this work was to compute blood input function from the inferior vena cava (IVC) with partial volume (PV) corrections and compare to that obtained from the left ventricular blood pool (LVBP) with spill-over (SP) and PV corrections. These were then used to compute and validate rates of myocardial 2-deoxy-2-[18F]fluoro-D-glucose (FDG) uptake (Ki) from dynamic positron emission tomography (PET) images of rat hearts in vivo in comparison to that obtained from invasive arterial blood sampling. Whole body 60 min dynamic FDG PET/CT imaging of n = 8 control Wistar Kyoto (WKY) rats were performed using Albira trimodal PET/CT/SPECT scanner. Image derived blood input function (IDIF) obtained from IVC corrected for PV averaging (IVC-PV) and IDIF from the left ventricular blood pool (LVBP) with SP and PV corrections (LVBP-SP-PV) were computed. Next, computed Ki (indirect comparison) in a 5-parameter (using IVC-PV) and a 15-parameter (using LVBP-SP-PV) 3-compartment models in WKY rat hearts in vivo were compared to that obtained using arterial blood sampling reported in literature in control Spraque Dawley (SD) rats. Using IVC-PV in a three-compartment five-parameter model resulted in a ~46% deviation in the mean computed Ki compared to that obtained with LVBP-SP-PV in a three-compartment 15-parameter model with a ~57% deviation in the mean computed Ki. The mean computed Ki in WKY rat hearts using the above methods, however, did not differ significantly to that obtained from invasive arterial blood sampling in SD rat hearts (p = 0.91 for IVC-PV and p = 0.58 for LVBP-SP-PV). Hence, Ki obtained in WKY rat hearts with input curve from IVC (IVC-PV) in a dynamic FDG PET scan is comparatively more repetitive to that obtained from the LVBP (LVBP-SP-PV). Ki computed using both the methods, however, agree well with each other and that obtained using arterial blood sampling.

Towards Improved Pharmacokinetic Models for the Analysis of Transporter-Mediated Hepatic Disposition of Drug Molecules with Positron Emission Tomography

Positron emission tomography (PET) imaging with radiolabeled drugs holds great promise to assess the influence of membrane transporters on hepatobiliary clearance of drugs. To exploit the full potential of PET, quantitative pharmacokinetic models are required. In this study, we evaluated the suitability of different compartment models to describe the hepatic disposition of [11C]erlotinib as a small-molecule model drug which undergoes transporter-mediated hepatobiliary excretion. We analyzed two different, previously published data sets in healthy volunteers, in which a baseline [11C]erlotinib PET scan was followed by a second PET scan either after oral intake of unlabeled erlotinib (300 mg) or after intravenous infusion of the prototypical organic anion-transporting polypeptide inhibitor rifampicin (600 mg). We assessed a three-compartment (3C) and a four-compartment (4C) model, in which either a sampled arterial blood input function or a mathematically derived dual input function (DIF), which takes the contribution of the portal vein to the liver blood supply into account, was used. Both models provided acceptable fits of the observed PET data in the liver and extrahepatic bile duct and gall bladder. Changes in model outcome parameters between scans were consistent with the involvement of basolateral hepatocyte uptake and canalicular efflux transporters in the hepatobiliary clearance of [11C]erlotinib. Our results demonstrated that inclusion of a DIF did not lead to substantial improvements in model fits. The models developed in this work represent a step forward in applying PET as a tool to assess the impact of hepatic transporters on drug disposition and their involvement in drug-drug interactions.