Positron emission tomography compartmental models

Quantification of human brain benzodiazepine receptors using [18F]fluoroethylflumazenil: a first report in volunteers and epileptic patients

Fluorine-18 fluoroethylflumazenil ([18F]FEF) is a tracer for central benzodiazepine (BZ) receptors which is proposed as an alternative to carbon-11 flumazenil for in vivo imaging using positron emission tomography (PET) in humans. In this study, [18F]FEF kinetic data were acquired using a 60-min two-injection protocol on three normal subjects and two patients suffering from mesiotemporal epilepsy as demonstrated by abnormal magnetic resonance imaging and [18F]fluorodeoxyglucose positron emission tomography. First, a tracer bolus injection was performed and [18F]FEF rapidly distributed in the brain according to the known BZ receptor distribution. Thirty minutes later a displacement injection of 0.01 mg/kg of unlabelled flumazenil was performed. Activity was rapidly displaced from all BZ receptor regions demonstrating the specific binding of [18F]FEF. No displacement was observed in the pons. Plasma input function was obtained from arterial blood sampling, and metabolite analysis was performed by high-performance liquid chromatography. Metabolite quantification revealed a fast decrease in tracer plasma concentration, such that at 5 min post injection about 70% of the total radioactivity in plasma corresponded to [18F]FEF, reaching 24% at 30 min post injection. The interactions between [18F]FEF and BZ receptors were described using linear compartmental models with plasma input and reference tissue approaches. Binding potential values were in agreement with the known distribution of BZ receptors in human brain. Finally, in two patients with mesiotemporal sclerosis, reduced uptake of [18F]FEF was clearly observed in the implicated left hippocampus.

Synthesis and preliminary evaluation of (R,R)(S,S) 5-(2-(2-[4-(2-[(18)F]fluoroethoxy)phenyl]-1-methylethylamino)-1-hydroxyethyl)-benzene-1,3-diol ([(18)F]FEFE) for the in vivo visualisation and quantification of the beta2-adrenergic receptor status in lung

The (18)F-labeled beta2-adrenergic receptor ligand (R,R)(S,S) 5-(2-(2-[4-(2-[(18)F]fluoroethoxy)phenyl]-1-methylethylamino)-1-hydroxyethyl)-benzene-1,3-diol, a derivative of the original highly selective racemic fenoterol, was synthesized in an overall radiochemical yield of 20% after 65 min with a radiochemical purity higher than 98%. The specific activity was in the range of 50-60 GBq/micromol. In vitro testing of the non-radioactive fluorinated fenoterol derivative with isolated guinea pig trachea was conducted to obtain an IC(50) value of 60 nM. Preliminary ex vivo organ distribution and in vivo experiments with positron emission tomography (PET) on guinea pigs were performed to study the biodistribution as well as the displacement of the radiotracer to prove specific binding to the beta2-receptor.

Kinetic modelling of [123I]CNS 1261--a potential SPET tracer for the NMDA receptor

N-(1-napthyl)-N'-(3-[(123)I]-iodophenyl)-N-methylguanidine ([(123)I]CNS 1261) is a novel SPET ligand developed for imaging the NMDA receptor intra-channel MK 801/PCP/ketamine site. Data was acquired in 7 healthy volunteers after bolus injection of [(123)I]CNS 1261. Kinetic modeling showed reversible tracer binding. Arterial and venous time-activity curves overlapped after 90 min. The rank order of binding was: Thalamus > striatum > cortical regions > white matter. This distribution concurs with [(11)C]-ketamine and [(18)F]-memantine PET studies. These data provide a methodological basis for further direct in vivo challenge studies.

On the undecidability among kinetic models: from model selection to model averaging

This article deals with the problem of model selection for the mathematical description of tracer kinetics in nuclear medicine. It stems from the consideration of some specific data sets where different models have similar performances. In these situations, it is shown that considerate averaging of a parameter's estimates over the entire model set is better than obtaining the estimates from one model only. Furthermore, it is also shown that the procedure of averaging over a small number of "good" models reduces the "generalization error," the error introduced when the model selected over a particular data set is applied to different conditions, such as subject populations with altered physiologic parameters, modified acquisition protocols, and different signal-to-noise ratios. The method of averaging over the entire model set uses Akaike coefficients as measures of an individual model's likelihood. To facilitate the understanding of these statistical tools, the authors provide an introduction to model selection criteria and a short technical treatment of Akaike's information-theoretic approach. The new method is illustrated and epitomized by a case example on the modeling of [11C]flumazenil kinetics in the brain, containing both real and simulated data.

Linear regression with spatial constraint to generate parametric images of ligand-receptor dynamic PET studies with a simplified reference tissue model

For the quantitative analysis of ligand-receptor dynamic positron emission tomography (PET) studies, it is often desirable to apply reference tissue methods that eliminate the need for arterial blood sampling. A common technique is to apply a simplified reference tissue model (SRTM). Applications of this method are generally based on an analytical solution of the SRTM equation with parameters estimated by nonlinear regression. In this study, we derive, based on the same assumptions used to derive the SRTM, a new set of operational equations of integral form with parameters directly estimated by conventional weighted linear regression (WLR). In addition, a linear regression with spatial constraint (LRSC) algorithm is developed for parametric imaging to reduce the effects of high noise levels in pixel time activity curves that are typical of PET dynamic data. For comparison, conventional weighted nonlinear regression with the Marquardt algorithm (WNLRM) and nonlinear ridge regression with spatial constraint (NLRRSC) were also implemented using the nonlinear analytical solution of the SRTM equation. In contrast to the other three methods, LRSC reduces the percent root mean square error of the estimated parameters, especially at higher noise levels. For estimation of binding potential (BP), WLR and LRSC show similar variance even at high noise levels, but LRSC yields a smaller bias. Results from human studies demonstrate that LRSC produces high-quality parametric images. The variance of R(1) and k(2) images generated by WLR, WNLRM, and NLRRSC can be decreased 30%-60% by using LRSC. The quality of the BP images generated by WLR and LRSC is visually comparable, and the variance of BP images generated by WNLRM can be reduced 10%-40% by WLR or LRSC. The BP estimates obtained using WLR are 3%-5% lower than those estimated by LRSC. We conclude that the new linear equations yield a reliable, computationally efficient, and robust LRSC algorithm to generate parametric images of ligand-receptor dynamic PET studies.

Positron emission tomography compartmental models: a basis pursuit strategy for kinetic modeling

A kinetic modeling approach for the quantification of in vivo tracer studies with dynamic positron emission tomography (PET) is presented. The approach is based on a general compartmental description of the tracer's fate in vivo and determines a parsimonious model consistent with the measured data. The technique involves the determination of a sparse selection of kinetic basis functions from an overcomplete dictionary using the method of basis pursuit denoising. This enables the characterization of the systems impulse response function from which values of the systems macro parameters can be estimated. These parameter estimates can be obtained from a region of interest analysis or as parametric images from a voxel-based analysis. In addition, model order estimates are returned that correspond to the number of compartments in the estimated compartmental model. Validation studies evaluate the methods performance against two preexisting data led techniques, namely, graphical analysis and spectral analysis. Application of this technique to measured PET data is demonstrated using [11C]diprenorphine (opiate receptor) and [11C]WAY-100635 (5-HT1A receptor). Although the method is presented in the context of PET neuroreceptor binding studies, it has general applicability to the quantification of PET/SPECT radiotracer studies in neurology, oncology, and cardiology.

In vivo metabolism and partitioning of 6-[18F]fluoro-L-meta-tyrosine in whole blood: a unified compartment model

Physiological quantification of dynamic PET data requires the determination of an input function, preferably from plasma. A compartmental model relating a parent radiotracer, its radiolabelled metabolites and their exchange between plasma and erythrocytes is presented. This model allows for the time course of radioactivity measured in whole blood to be transformed into the time course of the radiotracer in plasma. The utility of this approach is illustrated with blood data collected on 30 human subjects injected with 6-[18F]fluoro-L-meta-tyrosine (FmT), a pre-synaptic dopaminergic radiotracer. A three-compartment four-parameter model is shown to yield significantly better fits to the blood data than related lower and higher order models. This model is found to be robust to measurement noise, and yet sensitive to metabolic changes induced by pretreatment with carbidopa. For FmT, the between-subject variations are shown to be small enough to warrant the use of a population-based correction; tissue time-activity curves were simulated to verify that this correction does not significantly affect the precision and accuracy of the derived rate constants. The unified blood model can be adapted for radiotracers other than FmT as long as the blood partition ratio of the parent radiotracer differs from that of its metabolites and/or the rate at which they equilibrate between plasma and erythrocytes is different.