Tissue Time-activity Curves Research Articles

Combined MR-assisted motion and partial volume effects corrections - impact on PET data quantification.

Our goal in this study was to characterize the combined effect of MR-assisted motion correction (MC) and partial volume effects correction (PVEC) on the estimation of [11C]NNC112 binding potential (BP) in healthy volunteers. 29 subjects were scanned on the Siemens 3T MR-BrainPET scanner prototype. Emission data were acquired in list mode format for 90-minutes following the i.v. administration of ~8 mCi of [11C]NNC112. The head attenuation map was obtained from the MPRAGE data using an atlas-based method. Head motion estimates were derived from the MR data and used to correct the PET data in LOR space before image reconstruction [1]. PVEC was applied to the motion corrected data using the region-based voxel-wise (RBV) method [2] and regions of interest (ROIs) defined from the MPRAGE images using FreeSurfer and the measured point-spread function [3]. BPnd for each of the ROIs was estimated in PMOD using the simplified reference tissue kinetic (SRTM) model and the cerebellum as a reference tissue. Maximum translations of up to 9 mm and rotations of up to 12 degrees have been observed in this group of subjects (Figure ​(Figure1).1). Less variability in the tissue time activity curves (TACs) was noted after MC (the curves before and after MC for a representative subject are shown in Figure ​Figure2).2). The percentage changes in BPnd after MC and PVEC revealed both under- and overestimation in the ROIs analyzed (Figure ​(Figure3).3). The cumulative effect exceeded 100% for some of the structures analyzed. Figure 1 Maximum translations (left) and rotations (right) measured in 29 healthy volunteers scanned for 90 minutes Figure 2 TACs in the left and right caudate and cerebellar cortices before (left) and after (right) MR-assisted motion correction Figure 3 Percent change in NNC112 BPnd after motion and partial volume effects corrections Significant motion and PVE were observed in all the subjects, biasing the PET estimates. The combined effect is difficult to predict, depending on the size and location of the structure of interest and patient compliance. Without addressing these issues, the value of the BPnd’s derived from these data is questionable.

Quantification of myocardial blood flow using (201)Tl SPECT and population-based input function.

Thallium-201 ((201)Tl) single photon emission computed tomography (SPECT) is an important tool in the diagnosis of ischemic heart disease. Absolute quantification of myocardial blood flow (MBF) has the potential to provide more useful information on myocardial perfusion than semi-quantitative assessments. This study aimed to validate the quantification of MBF using (201)Tl cardiac SPECT based on a population-averaged input function (STD-IF) and one-point blood sample technique. (201)Tl emission and computed tomography (CT)-based attenuation scans were performed on 11 healthy volunteers at rest using a SPECT/CT scanner. Individual input functions (IND-IFs) during the emission scans were based on arterial blood samples. The STD-IF technique was validated as follows: (1) optimal time to calibrate a STD-IF was determined to minimize differences between the calibrated STD-IF and the IND-IFs. (2) Tissue time-activity curves (TTACs) were generated based on a single-tissue compartment model for MBFtrue=0.5, 1.0, 1.5, and 2.0mL/min/g, a constant distribution volume of 45mL/mL, and IND-IFs. The pseudo STD-IF for each subject was generated using the leave-one-out technique. Using the optimal calibration time and the pseudo STD-IFs, MBF values were estimated on the TTACs with an autoradiography method. Optimal mid-scan time (MST) with a fixed duration of 20min was determined to minimize intersubject variation in estimated MBF errors, and (3) Global and regional MBF values estimated with pseudo STD-IFs were compared to those with IND-IFs using the optimal calibration time and MST. The optimal calibration time and MST were both 20min after (201)Tl injection. Global MBF determined using both IND-IFs and pseudo STD-IF showed significant correlations with rate-pressure products, R (2)=0.645; p<0.01 and R (2)=0.303; p<0.05, respectively. The mean percent error in regional MBF using pseudo STD-IFs was 0.69±7.80% (-12.80 to 14.25%). No significant difference was observed between regional MBF values using IND-IFs and pseudo STD-IFs. This study demonstrated that the proposed technique based on a STD-IF and one-point blood sample provided hemodynamically reasonable global MBF values and the regional MBF values comparable to those with IND-IFs.

Image-Derived Input Function Derived from a Supervised Clustering Algorithm: Methodology and Validation in a Clinical Protocol Using [11C](R)-Rolipram

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

Influence of O-Methylated Metabolite Penetrating the Blood–Brain Barrier to Estimation of Dopamine Synthesis Capacity in Human L-[β-11C]DOPA PET

O-methyl metabolite (L-[β-(11)C]OMD) of (11)C-labeled L-3,4-dihydroxyphenylalanine (L-[β-(11)C]DOPA) can penetrate into brain tissue through the blood-brain barrier, and can complicate the estimation of dopamine synthesis capacity by positron emission tomography (PET) study with L-[β-(11)C]DOPA. We evaluated the impact of L-[β-(11)C]OMD on the estimation of the dopamine synthesis capacity in a human L-[β-(11)C]DOPA PET study. The metabolite correction with mathematical modeling of L-[β-(11)C]OMD kinetics in a reference region without decarboxylation and further metabolism, proposed by a previous [(18)F]FDOPA PET study, were implemented to estimate radioactivity of tissue L-[β-(11)C]OMD in 10 normal volunteers. The component of L-[β-(11)C]OMD in tissue time-activity curves (TACs) in 10 regions were subtracted by the estimated radioactivity of L-[β-(11)C]OMD. To evaluate the influence of omitting blood sampling and metabolite correction, relative dopamine synthesis rate (kref) was estimated by Gjedde-Patlak analysis with reference tissue input function, as well as the net dopamine synthesis rate (Ki) by Gjedde-Patlak analysis with the arterial input function and TAC without and with metabolite correction. Overestimation of Ki was observed without metabolite correction. However, the kref and Ki with metabolite correction were significantly correlated. These data suggest that the influence of L-[β-(11)C]OMD is minimal for the estimation of kref as dopamine synthesis capacity.

18F]Altanserin and small animal PET: Impact of multidrug efflux transporters on ligand brain uptake and subsequent quantification of 5-HT2A receptor densities in the rat brain

The selective 5-hydroxytryptamine type 2a receptor (5-HT(2A)R) radiotracer [(18)F]altanserin is a promising ligand for in vivo brain imaging in rodents. However, [(18)F]altanserin is a substrate of P-glycoprotein (P-gp) in rats. Its applicability might therefore be constrained by both a differential expression of P-gp under pathological conditions, e.g. epilepsy, and its relatively low cerebral uptake. The aim of the present study was therefore twofold: (i) to investigate whether inhibition of multidrug transporters (MDT) is suitable to enhance the cerebral uptake of [(18)F]altanserin in vivo and (ii) to test different pharmacokinetic, particularly reference tissue-based models for exact quantification of 5-HT(2A)R densities in the rat brain. Eighteen Sprague-Dawley rats, either treated with the MDT inhibitor cyclosporine A (CsA, 50 mg/kg, n=8) or vehicle (n=10) underwent 180-min PET scans with arterial blood sampling. Kinetic analyses of tissue time-activity curves (TACs) were performed to validate invasive and non-invasive pharmacokinetic models. CsA application lead to a two- to threefold increase of [(18)F]altanserin uptake in different brain regions and showed a trend toward higher binding potentials (BP(ND)) of the radioligand. MDT inhibition led to an increased cerebral uptake of [(18)F]altanserin but did not improve the reliability of BP(ND) as a non-invasive estimate of 5-HT(2A)R. This finding is most probable caused by the heterogeneous distribution of P-gp in the rat brain and its incomplete blockade in the reference region (cerebellum). Differential MDT expressions in experimental animal models or pathological conditions are therefore likely to influence the applicability of imaging protocols and have to be carefully evaluated.

Magnetic resonance imaging and proton spectroscopy in the Huntington disease

Magnetic resonance imaging and proton spectroscopy in the Huntington disease

FDG kinetic modeling in small rodent brain PET: optimization of data acquisition and analysis

BackgroundKinetic modeling of brain glucose metabolism in small rodents from positron emission tomography (PET) data using 2-deoxy-2-[18 F]fluoro-d-glucose (FDG) has been highly inconsistent, due to different modeling parameter settings and underestimation of the impact of methodological flaws in experimentation. This article aims to contribute toward improved experimental standards. As solutions for arterial input function (IF) acquisition of satisfactory quality are becoming available for small rodents, reliable two-tissue compartment modeling and the determination of transport and phosphorylation rate constants of FDG in rodent brain are within reach.MethodsData from mouse brain FDG PET with IFs determined with a coincidence counter on an arterio-venous shunt were analyzed with the two-tissue compartment model. We assessed the influence of several factors on the modeling results: the value for the fractional blood volume in tissue, precision of timing and calibration, smoothing of data, correction for blood cell uptake of FDG, and protocol for FDG administration. Kinetic modeling with experimental and simulated data was performed under systematic variation of these parameters.ResultsBlood volume fitting was unreliable and affected the estimation of rate constants. Even small sample timing errors of a few seconds lead to significant deviations of the fit parameters. Data smoothing did not increase model fit precision. Accurate correction for the kinetics of blood cell uptake of FDG rather than constant scaling of the blood time-activity curve is mandatory for kinetic modeling. FDG infusion over 4 to 5 min instead of bolus injection revealed well-defined experimental input functions and allowed for longer blood sampling intervals at similar fit precisions in simulations.ConclusionsFDG infusion over a few minutes instead of bolus injection allows for longer blood sampling intervals in kinetic modeling with the two-tissue compartment model at a similar precision of fit parameters. The fractional blood volume in the tissue of interest should be entered as a fixed value and kinetics of blood cell uptake of FDG should be included in the model. Data smoothing does not improve the results, and timing errors should be avoided by precise temporal matching of blood and tissue time-activity curves and by replacing manual with automated blood sampling.

Quantitative positron emission tomography imaging of angiogenesis in rats with forelimb ischemia using 68Ga-NOTA-c(RGDyK)

Gallium-68-labeled 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA)-cyclic Arg-Gly-Asp-D-Tyr-Lys (c(RGDyK)) was developed for αvβ3 targeting, and is a promising agent for imaging of cancer and disorders related to angiogenesis. In this study, we performed kinetic analysis of (68)Ga-NOTA-c(RGDyK) in rats with surgically induced forelimb ischemia, and immunohistochemical analysis was also performed to assess αvβ3 immuno-staining level. Animal models were created by excision of the left brachial vessels, and a sham operation was performed on the right brachial region under 2 % isoflurane anesthesia. Using an animal positron emission tomography/computed tomography (PET/CT) scanner, a list mode PET scan (120 min) was started with the injection of (68)Ga-NOTA-c(RGDyK) via the tail vein at 3, 5 and 7 days after ischemic surgery. Volumes of interest were drawn on the left ventricle, sham operation, control, and ischemic regions. Compartmental and two graphical analyses (Logan and RE plots) were performed for kinetic parameter estimation. The immunohistochemical analysis was also performed after the last PET scan, and cell components were scored on a six point scale for quantification of immuno-staining level (0-negative to 5-very high). A 3-compartment model with reversible binding best described the tissue time-activity curves. The distribution volume of the ischemic region was significantly higher than that of the sham operation (P < 10(-6)) and control region (P < 10(-9)). Both the Logan and RE plots showed high correlation with compartmental analysis (R(2) = 0.96 and 0.95 for Logan and RE, respectively). The temporal changes in distribution volume and binding potential were not significant. The immuno-staining level of the ischemic region was significantly higher than that of sham operation (P < 10(-4)) and control region (P < 10(-8)). Kinetic modeling studies with dynamic (68)Ga-NOTA-c(RGDyK) PET scan are feasible based on an image-derived input function in a rat ischemia model. The kinetic modeling analysis performed in this study will be useful for the quantitative evaluation of (68)Ga-NOTA-c(RGDyK) binding to αvβ3 in angiogenic tissues.

11C]-(R)PK11195 tracer kinetics in the brain of glioma patients and a comparison of two referencing approaches

PurposeTranslocator protein (TSPO) is a biomarker of neuroinflammation that can be imaged by PET using [11C]-(R)PK11195. We sought to characterize the [11C]-(R)PK11195 kinetics in gliomas of different histotypes and grades, and to compare two reference tissue input functions (supervised cluster analysis versus cerebellar grey matter) for the estimation of [11C]-(R)PK11195 binding in gliomas and surrounding brain structures.MethodsTwenty-three glioma patients and ten age-matched controls underwent structural MRI and dynamic [11C]-(R)PK11195 PET scans. Tissue time–activity curves (TACs) were extracted from tumour regions as well as grey matter (GM) and white matter (WM) of the brains. Parametric maps of binding potential (BPND) were generated with the simplified reference tissue model using the two input functions, and were compared with each other. TSPO expression was assessed in tumour tissue sections by immunohistochemistry.ResultsThree types of regional kinetics were observed in individual tumour TACs: GM-like kinetics (n = 6, clearance of the tracer similar to that in cerebellar GM), WM-like kinetics (n = 8, clearance of the tracer similar to that in cerebral WM) and a form of mixed kinetics (n = 9, intermediate rate of clearance). Such kinetic patterns differed between low-grade astrocytomas (WM-like kinetics) and oligodendrogliomas (GM-like and mixed kinetics), but were independent of tumour grade. There was good agreement between parametric maps of BPND derived from the two input functions in all controls and 10 of 23 glioma patients. In 13 of the 23 patients, BPND values derived from the supervised cluster input were systematically smaller than those using the cerebellar input. Immunohistochemistry confirmed that TSPO expression increased with tumour grade.ConclusionThe three types of [11C]-(R)PK11195 kinetics in gliomas are determined in part by tracer delivery, and indicated that kinetic analysis is a valuable tool in the study of gliomas with the potential for in vivo discrimination between low-grade astrocytomas and oligodendrogliomas. Supervised cluster and cerebellar input functions produced consistent BPND estimates in approximately half of the gliomas investigated, but had a systematic difference in the remainder. The cerebellar input is preferred based on theoretical and practical considerations.

Initial human experience with Rubidium‐82 renal PET/CT imaging

Preclinical data have shown that Rubidium-82 chloride ((82)Rb) is a radiotracer with high first pass extraction and slow washout in the kidneys. The goal of this study was to investigate the feasibility of human kidney imaging with (82)Rb positron emission tomography (PET) and obtain quantitative data of its uptake non-invasively. Eight healthy volunteers underwent dynamic PET/CT imaging with (82)Rb. A preprogrammed pump was used to insure reproducible injections. Tissue time activity curves were generated from the renal cortex. An input function was derived from the left ventricular blood pool (LVBP), the descending thoracic aorta and the abdominal aorta. Renal blood flow was estimated by applying a two-compartment kinetic model. Results obtained with different input functions were compared. Radiotracer accumulation was rapid and reached a plateau within 15-30 s after the bolus entered the kidneys. The derived K1 and k2 parameters were reproducible using input functions obtained from diverse vascular locations. K1 averaged 1.98 ± 0.14 mL/min/g. The average k2 was 0.35 ± 0.11/min. Correlation between K1 values obtained from the LVBP from different bed positions when the kidneys and abdominal aorta were in the same field of view was excellent (R = 0.95). Non-invasive quantitative human kidney imaging with (82)Rb PET is feasible. Advantages of renal PET with (82)Rb include excellent image quality with high image resolution and contrast. (82)Rb has potential as a clinical renal imaging agent in humans.

Dynamic Observation of 18F-FDG Uptake After Oral Administration in a Healthy Subject

PET with (18)F-FDG is a widely used imaging modality in cancer patients. Traditionally, (18)F-FDG is administrated intravenously. However, patients with difficult venous access are not rare in clinical practice. The purpose of the current study was to investigate the dynamic process of (18)F-FDG distribution after oral administration in order to determine the optimal imaging acquisition time in human subjects. On the basis of tissue time-activity curves, we determined the time that was required to reach 90% of the maximal uptake in target organs. In a 50-y-old healthy subject with oral (18)F-FDG administration, we found that (18)F-FDG uptake maximized at 60 min for most organs except for the gray matter of the brain, which continued to accumulate (18)F-FDG after 60 min. Time to 90% was 22 min for liver, 36 min for kidneys, 48 min for myocardium, 50 min for bladder, 56 min for sigmoid colon, and greater than 61 min for gray matter of the brain. We suggest that PET images be acquired at around 60 min after oral (18)F-FDG administration for most organs. For the brain, a longer interval is required before acquisition.

Non linear mixed effects analysis in PET PK-receptor occupancy studies

The characterisation of a pharmacokinetic-receptor occupancy (PK-RO) relationship derived from a PET study is typically modelled in a conventional non-linear least squares (NLLS) framework. In the present work, we explore the application of a non-linear mixed effects approach (NLME) and compare this with NLLS estimation (using both naive pooled data and two-stage approaches) in the context of a direct PK-RO relationship described by an Emax model, using simulated data sets. Target and reference tissue time-activity curves were simulated using a two-tissue compartmental model and an arterial plasma input function for a typical PET study (12 subjects in 3 dose groups with 3 scans each). A range of different PET scenarios was considered to evaluate the impact of between-subject variability and reference region availability. The PET outcome measures derived from the simulations were then used to estimate the parameters of the PK-RO model. The performance of the two approaches was compared in terms of parameters estimates (square mean error SME, root mean square error RMSE) and prediction of the exposure-occupancy relationship. In general, both NLME and NLLS estimation methods provided unbiassed and precise population estimates for the Emax model parameters, although a slight bias was observed for the individual-NLLS method due to a few outliers. The increased value of NLME over NLLS was most notable in the estimation of the between-subject variability (BSV), especially in the case of a more complex PK-RO model when no reference region was available (maximum SME and RMSE values related to BSV of EC₅₀ of 27.6% and 86.5% from NLME versus 264.6% and 689.5% from NLLS). Overall, the NLME approach provided a more robust estimation and produced less-biassed estimates of the population means and variances than either the NLLS approach for the simulations considered.

Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Data to Constrain a Positron Emission Tomography Kinetic Model: Theory and Simulations

We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS). Then we simulate whole tissue TACs over a range of physiologically relevant kinetic parameter values and show that using appropriate DCE-MRI data can separate the PET TAC into the three components with accuracy that is noise dependent. The simulations show that accurate blood, EES, and EIS TACs can be obtained as evidenced by concordance correlation coefficients >0.9 between the true and estimated TACs. Additionally, provided that the estimated DCE-MRI parameters are within 10% of their true values, the errors in the PET kinetic parameters are within approximately 20% of their true values. The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies.

Dynamic Cardiac PET Imaging: Extraction of Time-Activity Curves Using ICA and a Generalized Gaussian Distribution Model

Kinetic modeling of metabolic and physiologic cardiac processes in small animals requires an input function (IF) and a tissue time-activity curves (TACs). In this paper, we present a mathematical method based on independent component analysis (ICA) to extract the IF and the myocardium's TACs directly from dynamic positron emission tomography (PET) images. The method assumes a super-Gaussian distribution model for the blood activity, and a sub-Gaussian distribution model for the tissue activity. Our appreach was applied on 22 PET measurement sets of small animals, which were obtained from the three most frequently used cardiac radiotracers, namely: desoxy-fluoro-glucose ((18)F-FDG), [(13)N]-ammonia, and [(11)C]-acetate. Our study was extended to PET human measurements obtained with the Rubidium-82 ((82) Rb) radiotracer. The resolved mathematical IF values compare favorably to those derived from curves extracted from regions of interest (ROI), suggesting that the procedure presents a reliable alternative to serial blood sampling for small-animal cardiac PET studies.

Compartmental modeling and simplified quantification of [11C]sertraline distribution in human brain

Sertraline hydrochloride (Zoloft®, Pfizer) is an antidepressant drug of the selective serotonin reuptake inhibitor (SSRI). The aims of this study were evaluating its in vivo distribution and kinetic models in human brain. Also, this study was to determine optimal scan duration of dynamic positron emission tomography (PET) for accurate [¹¹C]sertraline kinetic parameters and the feasibility of semi-quantitative approach for assessing distribution volume ratio (DVR). [¹¹C]sertraline dynamic PET and magnetic resonance imaging (MRI) scans were performed in 5 healthy males. Blood sampling were collected for the input function. Tissue time-activity curves (TAC) were obtained in 7 brain regions using MRI. Goodness-of-fit for TAC using simple tissue compartment model (2C2P) and 3-compartment models with irreversible (3C3P) and reversible (3C4P) were compared. Total distribution volume (DV) for each region of interest (ROI) and DVR were calculated. Also, ratio between the standard uptake value (SUV) of each ROI and that of cerebellum (SUVr) was computed and correlated with the DVR. Akaike information criteria analysis showed that the 2C2P was the most suitable model. Average values of K₁ (mL/min/g) and k₂ (1/min) were 0.54 and 0.012 in putamen. PET scan time longer than 50 min was required for the accurate estimation of DV. SUVr in 50-90 min was well correlated with DVR.

In Vivo Measurement of the Affinity and Density of Metabotropic Glutamate Receptor Subtype 1 in Rat Brain Using 18F-FITM in Small-Animal PET

Metabotropic glutamate receptor subtype 1 (mGluR1) is a crucial molecular target in the central nervous system disorders. 4-(18)F-fluoro-N-[4-[6-(isopropylamino)pyrimidin-4-yl]-1,3-thiazol-2-yl]-N-methylbenzamide ((18)F-FITM) has been recently developed as a useful PET ligand for mGluR1 imaging in our laboratory. In this study, we aimed to measure the affinity and density of mGluR1 using PET with (18)F-FITM in rat brain under the in vivo conditions. Binding potentials (BP(ND)) and amounts of specific binding (bound ligand concentration) at equilibrium state in brain regions were noninvasively estimated using the equilibrium analysis combined with the receptor-blocked approach (EA RBA) for kinetic analysis of (18)F-FITM PET results in place of reference tissue methods. Using BP(ND) and specific binding values of rats treated with multidose ligand, we performed Scatchard analyses for in vivo measurements of mGluR1 density (maximum number of binding sites, or B(max)) and ligand affinity (dissociation constant, or K(d)) in brain regions, respectively. The pretreatment of rats with unlabeled FITM (1 mg/kg) occupied an mGluR1 binding site of (18)F-FITM by more than 99% and did not affect the input function. Hence, we used the tissue time-activity curve for receptor-blocked rats as representative of the nondisplaceable (free and nonspecific binding of radioligand) compartment. The BP(ND) based on EA RBA showed a high correlation with the BP(ND) based on invasive Logan plot graphical analysis in the thalamus, hippocampus, striatum, and cingulate cortex. The K(d) (nM) and B(max) (pmol/mL) obtained by the Scatchard analyses with the multidose ligand assays were 2.1 and 36.3, respectively, for the thalamus; 2.1 and 27.5, respectively, for the hippocampus; 1.5 and 22.2, respectively, for the striatum; and 1.5 and 20.5, respectively, for the cingulate cortex with a high confidence. Our study is the first to our knowledge to measure the in vivo affinity (K(d) and binding potential) of (18)F-FITM and mGluR1 density (B(max)) with a high correlation to in vitro values in rat brain regions. This measurement using PET with (18)F-FITM would be a useful index for research about mGluR1 functions in central nervous system disorders and development of new pharmaceuticals.

Quantification of Dopamine Transporter in Human Brain Using PET with 18F-FE-PE2I

(18)F-(E)-N-(3-iodoprop-2E-enyl)-2β-carbofluoroethoxy-3β-(4-methylphenyl)nortropane ((18)F-FE-PE2I) is a new PET radioligand with a high affinity and selectivity for the dopamine transporter (DAT). In nonhuman primates, (18)F-FE-PE2I showed faster kinetics and less production of radiometabolites that could potentially permeate the blood-brain barrier than did (11)C-PE2I. The aims of this study were to examine the quantification of DAT using (18)F-FE-PE2I and to assess the effect of radiometabolites of (18)F-FE-PE2I on the quantification in healthy humans. A 90-min dynamic PET scan was obtained for 10 healthy men after intravenous injection of (18)F-FE-PE2I. Kinetic compartment model analysis with a metabolite-corrected arterial input function was performed. The effect of radiometabolites on the quantification was evaluated by time-stability analyses. The simplified reference tissue model (SRTM) method with the cerebellum as a reference region was evaluated as a noninvasive method of quantification. After the injection of (18)F-FE-PE2I, the whole-brain radioactivity showed a high peak (∼3-5 standardized uptake value) and fast washout. The radioactive uptake of (18)F-FE-PE2I in the brain was according to the relative density of the DAT (striatum > midbrain > thalamus). The cerebellum showed the lowest uptake. Tissue time-activity curves were well described by the 2-tissue-compartment model (TCM), as compared with the 1-TCM, for all subjects in all regions. Time stability analysis showed stable estimation of total distribution volume with 60-min or longer scan durations, indicating the small effect of radiometabolites. Binding potentials in the striatum and midbrain were well estimated by the SRTM method, with modest intersubject variability. Although the SRTM method yielded a slight underestimation and overestimation in regions with high and low DAT densities, respectively, binding potentials by the SRTM method were well correlated to the estimates by the indirect kinetic method with 2-TCM. (18)F-FE-PE2I is a promising PET radioligand for quantifying DAT. The binding potentials could be reliably estimated in both the striatum and midbrain using both the indirect kinetic and SRTM methods with a scan duration of 60 min. Although radiometabolites of (18)F-FE-PE2I in plasma possibly introduced some effects on the radioactivity in the brain, the effects on estimated binding potential were likely to be small.

Extraction of time activity curves from gated FDG-PET images for small animals’ heart studies

We introduce a new approach to extract the input function and the tissue time activity curve from dynamic ECG-gated 18F-FDG PET images. These curves are mandatory to model the myocardium metabolic rate of glucose for heart studies. The proposed method utilizes coupled active contours to track the myocardium and the blood pool deformations. Furthermore, a statistical approach is developed to model the blood and tissue activities and to correct for spillovers. The developed algorithm offers a reliable alternative to serial blood sampling for small animal cardiac PET studies. Indeed, the calculated MMRG value differs by 1.54% only from the reference value.

WE-C-217BCD-12: Irreversible Two-Tissue Compartment Model Fitting for Dynamic 18F- FDG PET: A Practical Comparison of Methods Using Simulated Time- Activity Data.

To compare techniques of fitting simulated Dynamic 18F-FDG PET time-activity curves to the irreversible two-tissue compartment model. The precision and accuracy of various algorithms were assessed in the presence of measurement error and with a practical focus on CPU time. Dynamic PET analysis is often applied to multiple individual voxels, leading to concerns regarding time efficiency, yet a reluctance to compromise on accuracy. This study evaluates selected fitting algorithms in terms of the precision/bias of fitted parameters and run-time. As a standard for comparison, biological parameters were fixed at typical values (K1=0.2, k2=0.25, k3=0.05, K&#7522;=0.3334). Tissue time-activity curves were generated using the model equation and by incorporating activity/time dependent Gaussian noise. The following algorithms were then applied: Genetic, Conjugate Gradient (CG), Gradient Descent (GD), Simulated Annealing (SA), Levenberg-Marquardt (LMQ), Gauss-Newton (GN), and Limited-BFGS (L-BFGS). Non-iterative, problem specific approaches were also considered: Patlak analysis (K? only), and Blomqvist linearization (BL). Parameter accuracy and precision were quantified with relative errors (REs). At typical noise levels, maximal REs were >60% (resulting in |Î'K?|>10%) for GD and SA, <20% for BL (|Î'K?|<6%) and Patlak (|Î'K?|<4%), and <15% (|Î'K?|<2%) with other methods. On average, the highest fidelity parameter estimates (rate-constant REs<2.5%, |Î'K?|<0.15%) were attained with L-BFGS, GN, and LMQ. In contrast, the non-iterative methods provided poorer estimates (RE between 1-7%), though run-time (<0.05 ms) was 100-1000x less. Using BL results as initial estimates for L- BFGS (BL+L-BFGS), GN (BL+GN) or LMQ (BL+LMQ) yielded rapid convergence (<0.1 ms), while maintaining superiority with respect to parameter bias. Poor choice of a fitting algorithm may lead to significant errors in estimated kinetic parameters and/or unpractically high computation times. Thus, our recommendation is the use of BL+L-BFGS, BL+GN or BL+LMQ, which is contrary to the gold standards, Patlak and LMQ (alone), commonly seen in the literature.

Double-input compartmental modeling and spectral analysis for the quantification of positron emission tomography data in oncology

In positron emission tomography (PET) studies involving organs different from the brain, ignoring the metabolite contribution to the tissue time-activity curves (TAC), as in the standard single-input (SI) models, may compromise the accuracy of the estimated parameters. We employed here double-input (DI) compartmental modeling (CM), previously used for [11C]thymidine, and a novel DI spectral analysis (SA) approach on the tracers 5-[18F]fluorouracil (5-[18F]FU) and [18F]fluorothymidine ([18F]FLT). CM and SA were performed initially with a SI approach using the parent plasma TAC as an input function. These methods were then employed using a DI approach with the metabolite plasma TAC as an additional input function. Regions of interest (ROIs) corresponding to healthy liver, kidneys and liver metastases for 5-[18F]FU and to tumor, vertebra and liver for [18F]FLT were analyzed. For 5-[18F]FU, the improvement of the fit quality with the DI approaches was remarkable; in CM, the Akaike information criterion (AIC) always selected the DI over the SI model. Volume of distribution estimates obtained with DI CM and DI SA were in excellent agreement, for both parent 5-[18F]FU (R2 = 0.91) and metabolite [18F]FBAL (R2 = 0.99). For [18F]FLT, the DI methods provided notable improvements but less substantial than for 5-[18F]FU due to the lower rate of metabolism of [18F]FLT. On the basis of the AIC values, agreement between [18F]FLT Ki estimated with the SI and DI models was good (R2 = 0.75) for the ROIs where the metabolite contribution was negligible, indicating that the additional input did not bias the parent tracer only-related estimates. When the AIC suggested a substantial contribution of the metabolite [18F]FLT-glucuronide, on the other hand, the change in the parent tracer only-related parameters was significant (R2 = 0.33 for Ki). Our results indicated that improvements of DI over SI approaches can range from moderate to substantial and are more significant for tracers with a high rate of metabolism. Furthermore, they showed that SA is suitable for DI modeling and can be used effectively in the analysis of PET data.