Dynamic Positron Emission Tomography Studies Research Articles

Kinetic Quantitation of Cerebral PET-FDG Studies Without Concurrent Blood Sampling: Statistical Recovery of the Arterial Input Function

Kinetic quantitation of dynamic positron emission tomography (PET) studies via compartmental modeling usually requires the time-course of the radio-tracer concentration in the arterial blood as an arterial input function (AIF). For human and animal imaging applications, significant practical difficulties are associated with direct arterial sampling and as a result there is substantial interest in alternative methods that require no blood sampling at the time of the study. A fixed population template input function derived from prior experience with directly sampled arterial curves is one possibility. Image-based extraction, including requisite adjustment for spillover and recovery, is another approach. The present work considers a hybrid statistical approach based on a penalty formulation in which the information derived from a priori studies is combined in a Bayesian manner with information contained in the sampled image data in order to obtain an input function estimate. The absolute scaling of the input is achieved by an empirical calibration equation involving the injected dose together with the subject's weight, height and gender. The technique is illustrated in the context of (18)F -Fluorodeoxyglucose (FDG) PET studies in humans. A collection of 79 arterially sampled FDG blood curves are used as a basis for a priori characterization of input function variability, including scaling characteristics. Data from a series of 12 dynamic cerebral FDG PET studies in normal subjects are used to evaluate the performance of the penalty-based AIF estimation technique. The focus of evaluations is on quantitation of FDG kinetics over a set of 10 regional brain structures. As well as the new method, a fixed population template AIF and a direct AIF estimate based on segmentation are also considered. Kinetics analyses resulting from these three AIFs are compared with those resulting from radially sampled AIFs. The proposed penalty-based AIF extraction method is found to achieve significant improvements over the fixed template and the segmentation methods. As well as achieving acceptable kinetic parameter accuracy, the quality of fit of the region of interest (ROI) time-course data based on the extracted AIF, matches results based on arterially sampled AIFs. In comparison, significant deviation in the estimation of FDG flux and degradation in ROI data fit are found with the template and segmentation methods. The proposed AIF extraction method is recommended for practical use.

Regional Rates of Cerebral Protein Synthesis Measured with l-[1-11C]Leucine and PET in Conscious, Young Adult Men: Normal Values, Variability, and Reproducibility

We report regional rates of cerebral protein synthesis (rCPS) measured with the fully quantitative L-[1-(11)C]leucine positron emission tomography (PET) method. The method accounts for the fraction (lambda) of unlabeled amino acids in the precursor pool for protein synthesis derived from arterial plasma; the remainder (1-lambda) comes from tissue proteolysis. We determined rCPS and lambda in 18 regions and whole brain in 10 healthy men (21 to 24 years). Subjects underwent two 90-min dynamic PET studies with arterial blood sampling at least 2 weeks apart. Rates of cerebral protein synthesis varied regionally and ranged from 0.97+/-0.70 to 2.25+/-0.20 nmol/g per min. Values of rCPS were in good agreement between the two PET studies. Mean differences in rCPS between studies ranged from 9% in cortical regions to 15% in white matter. The lambda value was comparatively more uniform across regions, ranging from 0.63+/-0.03 to 0.79+/-0.02. Mean differences in lambda between studies were 2% to 8%. Intersubject variability in rCPS was on average 6% in cortical areas, 9% in subcortical regions, and 12% in white matter; intersubject variability in lambda was 2% to 8%. Our data indicate that in human subjects low variance and highly reproducible measures of rCPS can be made with the L-[1-(11)C]leucine PET method.

Prediction of Short-term Survival in Patients with Advanced Nonsmall Cell Lung Cancer Following Chemotherapy Based on 2-Deoxy-2-[F-18]fluoro-d-glucose-Positron Emission Tomography: A Feasibility Study

Dynamic positron emission tomography (PET) studies with 2-deoxy-2-[F-18]fluoro-D-glucose (FDG) were performed in patients with advanced nonsmall cell lung cancer (NSCLC) who received palliative chemotherapy to evaluate the impact of full kinetic analysis and assess its value with regard to short or long survival. The evaluation includes 42 metastatic lesions in 14 patients with NSCLC. All patients received a combined chemotherapeutic protocol consisting of vinorelbin and oxaliplatin. The survival data served as reference for the PET data. All patients were examined before onset of chemotherapy and on day 15-21 after onset of the first cycle. The following parameters were retrieved from the dynamic PET studies: standardized uptake value (SUV), fractal dimension, two-compartment model with computation of k1, k2, k3, k4 (unit: 1/min), the fractional blood volume, and the FDG-influx according to Patlak was calculated using the formula (k1 x k3) / (k2 + k3). We used a two-group classification, namely, a short- and long-term survival group based on the median survival time (193 days) as a cutoff. A support vector machines (SVM) analysis was used for classification of the two a prior defined groups. The observed survival times varied from 40 to 392 days with a median survival time of 193 days. Most kinetic parameters demonstrated only small changes mostly declining after one cycle. The change in all kinetic parameters did not correlate to the survival-based classification. The change in SUV was significant between the first and second study (p = 0.006) but without an impact on the prediction of short or long survival. SVM-based analysis revealed the highest correct classification rate (CCR) between short and long survival for the combination of SUV and influx of the first study and SUV, influx, k2, and k4 of the second study with a CCR of 95.2%. The results demonstrate that a full kinetic analysis of the FDG kinetics in NSCLC is helpful for the classification into short or long survival and may be used to identify those patients who may benefit from this palliative chemotherapeutic protocol.

Clustering‐Based Linear Least Square Fitting Method for Generation of Parametric Images in Dynamic FDG PET Studies

Parametric images generated from dynamic positron emission tomography (PET) studies are useful for presenting functional/biological information in the 3-dimensional space, but usually suffer from their high sensitivity to image noise. To improve the quality of these images, we proposed in this study a modified linear least square (LLS) fitting method named cLLS that incorporates a clustering-based spatial constraint for generation of parametric images from dynamic PET data of high noise levels. In this method, the combination of K-means and hierarchical cluster analysis was used to classify dynamic PET data. Compared with conventional LLS, cLLS can achieve high statistical reliability in the generated parametric images without incurring a high computational burden. The effectiveness of the method was demonstrated both with computer simulation and with a human brain dynamic FDG PET study. The cLLS method is expected to be useful for generation of parametric images from dynamic FDG PET study.

Labeling of low-density lipoproteins using the 18F-labeled thiol-reactive reagent N-[6-(4-[18F]fluorobenzylidene)aminooxyhexyl]maleimide

The novel thiol-group-selective bifunctional 18F-labeling agent N-[6-(4-[18F]fluoro-benzylidene)aminooxyhexyl]maleimide ([18F]FBAM) has been developed. The bifunctional labeling precursor N-(6-aminoxyhexyl)maleimide containing a thiol-reactive maleimide group and a carbonyl-group-reactive aminooxy group was prepared in only three steps in a total chemical yield of 59%. Subsequent radiolabeling with 4-[18F]fluorobenzaldehyde gave the bifunctional 18F-labeling agent [18F]FBAM in a radiochemical yield of 29%. In a typical experiment, 3.88 GBq of [18F]fluoride could be converted into 723 MBq of [18F]FBAM within 69 min. Conjugation of [18F]FBAM with thiol groups was exemplified with the cysteine-containing tripeptide glutathione and with various apolipoproteins of human low-density lipoprotein (LDL) subfractions. The latter was evaluated with respect to the uptake of [18F]FBAM-LDL subfractions in human hepatoma cells (HepG2) in vitro. In vivo biodistribution studies in rats revealed high stability for [18F]FBAM-LDL subfractions. Moreover, the metabolic fate of [18F]FBAM-LDL subfractions in vivo was delineated by dynamic positron emission tomography studies using a dedicated small animal tomograph. Data were compared to former studies that used the NH2-reactive 18F-labeling agent N-succinimidyl-4-[18F]fluorobenzoate. The compound [18F]FBAM can be considered as an excellent prosthetic group for the selective and mild 18F labeling of thiol-group-containing biomolecules suitable for subsequent investigations in vitro and in vivo.

Metabolism of the A1 adenosine receptor PET ligand [18F]CPFPX by CYP1A2: implications for bolus/infusion PET studies

The A1 adenosine receptor positron emission tomography (PET) ligand 8-cyclopentyl-3-(3-[18F]fluoropropyl)-1-propylxanthine ([18F]CPFPX, ) undergoes a fast hepatic metabolism. An optimal design of PET quantitation approaches (e.g., bolus/infusion studies) necessitates the knowledge of factors that influence this metabolism. Metabolites of were separated by radio thin-layer chromatography. Metabolism in vivo, in pooled human liver microsomes and in recombinant human cytochrome isoenzyme preparations was studied. Dynamic PET studies using were performed on three controls and two patients, one treated with the antidepressant and inhibitor of cytochrome CYP1A2 fluvoxamine, the other suffering from liver cirrhosis. CPFPX is metabolized by cytochrome CYP1A2 with high selectivity [KM=1.1 microM (95% confidence interval, or CI, 0.6-2.0 microM) and Vmax=243 pmol min(-1) mg(-1) (95% CI, 112-373 pmol min(-1) mg(-1)) corresponding to 2.4 pmol min(-1) pmol(-1) cytochrome P-450]. This metabolism can competitively be inhibited by fluvoxamine with KI=68 nM (95% CI, 34-138 nM). At least eight compounds found in human plasma and in the CYP1A2 in vitro preparations have an identical migration pattern and account together for >90% and >80% of the respective metabolite yield. Metabolism was considerably delayed in the two patients. In conclusion, is metabolized by cytochrome CYP1A2. Its metabolism is therefore subdued to disease-related or xenobiotic-induced changes of CYP1A2 activity. The identification of the metabolic pathway of 1 allows to optimize image quantification in A1 adenosine receptor PET studies.

Comparative evaluation of two commercial PET scanners, ECAT EXACT HR+ and Biograph 2, using GATE

Geant4 application for tomographic emission (GATE) is a generic Monte Carlo simulation platform based on a general-purpose code GEANT4 and designed to simulate positron emission tomography (PET) and single photon emission tomography systems. Monte Carlo simulations are used in nuclear medicine to model imaging systems and develop and assess tomographic reconstruction algorithms and correction methods for improved image quantification. The purpose of this study is to validate two GATE models of the commercial available PET scanner HR+ and the PET/CT Biograph 2. The geometry of the system components has been described in GATE, including detector ring, crystal blocks, PMTs etc. The energy and spatial resolution of the scanners as given by the manufacturers have been taken into account. The GATE simulated results are compared directly to experimental data obtained using a number of NEMA NU-2-2001 performance protocols, including spatial resolution, sensitivity and scatter fraction. All the respective phantoms are precisely modeled. Furthermore, an approximate dead-time model both at the level of single and coincidence events was developed so that the simulated count rate curve can satisfactorily match the experimental count rate performance curve for each scanner In addition a software tool was developed to build the sinograms from the simulated data and import them into the software for tomographic image reconstruction where the reconstruction algorithm of FBP3DRP was applied. An agreement of less than 0.8 mm was obtained between the spatial resolution of the simulated system and the experimental results. Also the simulated scatter fraction for the NEMA NU 2-2001 scatter phantom matched the experimental results to within 3% of measured values. Finally the ratio of the simulated sensitivities with sources radially offset 0 and 10 cm from the central axis of each of the two scanners reaches an agreement of less than 1% between the simulated and experimental values. This simulation code will be used in a second phase in order to study scatter phenomena and motion artifacts. The simulation results will be used to optimize image reconstruction algorithms, with emphasis on dynamic PET studies.

Optimization algorithms and weighting factors for analysis of dynamic PET studies

Positron emission tomography (PET) pharmacokinetic analysis involves fitting of measured PET data to a PET pharmacokinetic model. The fitted parameters may, however, suffer from bias or be unrealistic, especially in the case of noisy data. There are many optimization algorithms, each having different characteristics. The purpose of the present study was to evaluate (1) the performance of different optimization algorithms and (2) the effects of using incorrect weighting factors during optimization in terms of both accuracy and reproducibility of fitted PET pharmacokinetic parameters. In this study, the performance of commonly used optimization algorithms (i.e. interior-reflective Newton methods) and a simulated annealing (SA) method was evaluated. This SA algorithm, known as basin hopping, was modified for the present application. In addition, optimization was performed using various weighting factors. Algorithms and effects of using incorrect weighting factors were studied using both simulated and clinical time–activity curves (TACs). Input data, taken from [15O]H2O, [11C]flumazenil and [11C](R)-PK11195 studies, were used to simulate time–activity curves at various variance levels (0–15% COV). Clinical evaluation was based on studies with the same three tracers. SA was able to produce accurate results without the need for selecting appropriate starting values for (kinetic) parameters, in contrast to the interior-reflective Newton method. The latter gave biased results unless it was modified to allow for a range of starting values for the different parameters. For patient studies, where large variability is expected, both SA and the extended Newton method provided accurate results. Simulations and clinical assessment showed similar results for the evaluation of different weighting models in that small to intermediate mismatches between data variance and weighting factors did not significantly affect the outcome of the fits. Large errors were observed only when the mismatch between weighting model and data variance was large. It is concluded that selection of specific optimization algorithms and weighting factors can have a large effect on the accuracy and precision of PET pharmacokinetic analysis. Apart from carefully selecting appropriate algorithms and variance models, further improvement in accuracy might be obtained by using noise reducing strategies, such as wavelet filtering, provided that these methods do not introduce significant bias.

Blind source separation for the computational analysis of dynamic oncological PET studies

The analysis of dynamic positron emission tomography (PET) studies provides clinically useful parametric information, but often requires complex and time-consuming compartmental or non-compartmental techniques. Independent component analysis (ICA), a statistical method used for feature extraction and signal separation, is applied to dynamic PET studies to facilitate the initial interpretation and visual analysis of these large image sequences. ICA produces parametric images, where structures with different kinetic characteristics are assigned opposite values and readily discriminated, improving the identification of lesions and facilitating the posterior detailed kinetic analysis.

Locally constrained mixture representation of dynamic imaging data from PET and MR studies

Dynamic positron emission tomography (PET) studies provide measurements of the kinetics of radiotracers in living tissue. This is a powerful technology which can play a major role in the study of biological processes, potentially leading to better understanding and treatment of disease. Dynamic PET data relate to complex spatiotemporal processes and its analysis poses significant challenges. In previous work, mixture models that expressed voxel-level PET time course data as a convex linear combination of a finite number of dominant time course characteristics (called sub-TACs) were introduced. This paper extends that mixture model formulation to allow for a weighted combination of scaled sub-TACs and also considers the imposition of local constraints in the number of sub-TACs that can be active at any one voxel. An adaptive 3D scaled segmentation algorithm is developed for model initialization. Increases in the weighted residual sums of squares is used to guide the choice of the number of segments and the number of sub-TACs in the final mixture model. The methodology is applied to five data sets from representative PET imaging studies. The methods are also applicable to other contexts in which dynamic image data are acquired. To illustrate this, data from an echo-planar magnetic resonance (MR) study of cerebral hemodynamics are considered. Our analysis shows little indication of departure from a locally constrained mixture model representation with at most two active components at any voxel. Thus, the primary sources of spatiotemporal variation in representative dynamic PET and MR imaging studies would appear to be accessible to a substantially simplified representation in terms of the generalized locally constrained mixture model introduced.

Evaluation of Drug Penetration into the Brain: A Double Study by in Vivo Imaging with Positron Emission Tomography and Using an in Vitro Model of the Human Blood-Brain Barrier

The blood-brain barrier (BBB) permeabilities of 11 compounds were measured both in vitro with a newly developed coculture-based model of human BBB and in vivo with positron emission tomography (PET). The 11 compounds were fluoropyridinyl derivatives labeled with the positron-emitter fluorine-18, [(18)F]F-A-85380 [2-[(18)F]fluoro-3-[2(S)-2 azetidinylmethoxy]pyridine], and 10 selected N-substituted-azetidinyl and pyrrolidinyl closely related [(18)F]fluoropyridinyl derivatives (including [N'-aromatic/aliphatic]-thioureas, -ureas, and -amides). The in vitro BBB model, a new coculture system of primary human brain endothelial cells and astrocytes, was used to measure the permeability coefficient for each compound. Dynamic PET studies were performed in rats with the same compounds, and a two-compartment model analysis was used to calculate their in vivo permeability coefficients. The 11 derivatives differed in their degree of BBB passage and transport mechanism. The analysis of PET data showed a significant cerebral uptake for six derivatives, for which the in vitro evaluation indicated active influx or free diffusion. Five derivatives displayed low in vivo cerebral uptake, in agreement with the observation of an in vitro active efflux. Overall, there was a remarkable correlation between the in vitro and in vivo permeability coefficients (r = 0.99). This double study proves a close correlationship between the assessment of the BBB passage in vitro and in vivo. The in vitro model of human BBB offers the possibility of subtle discrimination of various BBB permeability degrees and transport mechanisms. Conversely, small animal PET imaging appears suitable to screen directly in vivo brain targeting of drugs or radiopharmaceutical candidates.

Small animal positron emission tomography in food sciences

Positron emission tomography (PET) is a 3-dimensional imaging technique that has undergone tremendous developments during the last decade. Non-invasive tracing of molecular pathways in vivo is the key capability of PET. It has become an important tool in the diagnosis of human diseases as well as in biomedical and pharmaceutical research. In contrast to other imaging modalities, radiotracer concentrations can be determined quantitatively. By application of appropriate tracer kinetic models, the rate constants of numerous different biological processes can be determined. Rapid progress in PET radiochemistry has significantly increased the number of biologically important molecules labelled with PET nuclides to target a broader range of physiologic, metabolic, and molecular pathways. Progress in PET physics and technology strongly contributed to better scanners and image processing. In this context, dedicated high resolution scanners for dynamic PET studies in small laboratory animals are now available. These developments represent the driving force for the expansion of PET methodology into new areas of life sciences including food sciences. Small animal PET has a high potential to depict physiologic processes like absorption, distribution, metabolism, elimination and interactions of biologically significant substances, including nutrients, 'nutriceuticals', functional food ingredients, and foodborne toxicants. Based on present data, potential applications of small animal PET in food sciences are discussed.

A factor-image framework to quantification of brain receptor dynamic PET studies

The positron emission tomography (PET) imaging technique enables the measurement of receptor distribution or neurotransmitter release in the living brain and the changes of the distribution with time and thus allows quantification of binding sites as well as the affinity of a radioligand. However, quantification of receptor binding studies obtained with PET is complicated by tissue heterogeneity in the sampling image elements (i.e., voxels, pixels). This effect is caused by a limited spatial resolution of the PET scanner. Spatial heterogeneity is often essential in understanding the underlying receptor binding process. Tracer kinetic modeling also often requires an intrusive collection of arterial blood samples. In this paper, we propose a likelihood-based framework in the voxel domain for quantitative imaging with or without the blood sampling of the input function. Radioligand kinetic parameters are estimated together with the input function. The parameters are initialized by a subspace-based algorithm and further refined by an iterative likelihood-based estimation procedure. The performance of the proposed scheme is examined by simulations. The results show that the proposed scheme provides reliable estimation of factor time-activity curves (TACs) and the underlying parametric images. A good match is noted between the result of the proposed approach and that of the Logan plot. Real brain PET data are also examined, and good performance is observed in determining the TACs and the underlying factor images.

Kinetic modeling of PET data without blood sampling

In positron emission tomography (PET) imaging, application of kinetic modeling always requires an input curve (IC) together with the PET data. The IC can be obtained by means of external blood sampling or, in the case of cardiac studies, by means of a region-of-interest (ROI) drawn on the blood pool. It is, however, very unsuitable to withdraw and to analyze blood samples, and in small animals, these operations become difficult, while ICs determined from ROIs are generally contaminated by emissions from neighboring sites, or they are underestimated because of partial volume effect. In this paper, we report a new method to extract kinetic parameters from dynamic PET studies without a priori knowledge of the IC. The method is applied in human brain data measured with fluorodeoxyglucose (FDG) human-brain and in cardiac-rat perfusion studies with /sup 13/N-ammonia and /sup 11/C-acetate. The tissue blood volume (TBV), usually fitted together with the rate constants, is extracted simultaneously with the tissue time activity curves for cardiac studies, while for brain gray matter, TBV is known to be about 4% to 7%. The shape of IC is obtained by means of factor analysis from an ROI drawn around a cardiac tissue or a brain artery. The results show a good correlation (p<0.05) between the cerebral metabolic rate of glucose, myocardial blood flow, and oxygen consumption obtained with the new method in comparison to the usual method. In conclusion, it is possible to apply kinetic modeling without any blood sampling, which significantly simplifies PET acquisition and data analysis.

Sevoflurane and Propofol Increase 11C-Flumazenil Binding to Gamma-Aminobutyric AcidA Receptors in Humans

Based on in vitro studies and animal data, most anesthetics are supposed to act via gamma-aminobutyric acid type A (GABA(A)) receptors. However, this fundamental characteristic has not been extensively investigated in humans. We studied (11)C-flumazenil binding to GABA(A) receptors during sevoflurane and propofol anesthesia in the living human brain using positron emission tomography (PET). Fourteen healthy male subjects underwent 2 60-min dynamic PET studies with (11)C-labeled flumazenil, awake and during anesthesia. Anesthesia was maintained with 2% end-tidal sevoflurane (n = 7) or propofol at a target plasma concentration of 9.0 +/- 3.0 (mean +/- sd) microg/mL (n = 7). The depth of anesthesia was measured with bispectral index (BIS). Values of regional distribution volumes (DV) of (11)C-flumazenil were calculated in several brain areas using metabolite-corrected arterial plasma curves and a two-compartment model. Separate voxel-based statistical analysis using parametric DV images was performed for detailed visualization. The average BIS index was 35 +/- 6 in the sevoflurane group and 28 +/- 8 in the propofol group (P = 0.02). Sevoflurane increased the DV of (11)C-flumazenil significantly (P < 0.05) in all brain areas studied except the pons and the white matter. In the propofol group the increases were significant (P < 0.05) in the caudatus, putamen, cerebellum, thalamus and the frontal, temporal, and parietal cortices. Furthermore, the DV increases in the frontal, occipital, parietal, and temporal cortical areas and in the putamen were statistically significantly larger in the sevoflurane than in the propofol group. Our findings support the involvement of GABA(A) receptors in the mechanism of action of both anesthetics in humans.

Three-dimensional alignment of scans in a dynamic PET study using sinusoidal trajectory signals of a sinogram

A new alignment technique for sequences of three-dimensional (3-D) tomographic data is described. This kind of demand is, for example, in list mode acquisition of positron emission tomography (PET) or gated PET, assuming that distributions during separate scans are nearly equal. A 3-D stackgram is a stack of backprojected projections. Signals parallel to the stackgram's vertical axis are sinusoidal trajectory signals or locus-signals of the sinogram, which form the points of the object in the image domain. Transformation from the sinogram to the stackgram and back can be implemented in such a way that it is perfectly reversible even in the discrete case. The locus-signals, or locus-vectors in the discrete case, can be processed independently in the stackgram domain. The new local alignment technique is based on similarity comparisons between the locus-vectors in a 3-D neighborhood of sequences of the stackgrams. The final results are aligned sinograms (Method 1), or alternatively, the stackgrams can be summed directly to filtered backprojection images after the alignment (Method 2). The new technique does not require any external movement tracking system or landmarks, and it can be implemented to be fully automatic. In addition, the alignment is done prior to image reconstruction. In this study, the technique is tested using 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.

Brain uptake of the acid metabolites of F-18-labeled WAY 100635 analogs.

The 5-HT1A ligands [ 18F]FPWAY and [ 18F]FCWAY are metabolized to [ 18F]fluorobenzoic acid (FB) and [ 18F]fluorocyclohexanecarboxylic acid (FC), respectively. To quantify the penetration of these acids into the brain, dynamic positron emission tomography studies were performed in rhesus monkeys with [ 18F]FB and [ 18F]FC. High-performance liquid chromatography analysis of arterial blood samples showed no metabolites for [ 18F]FB, whereas [ 18F]FC was rapidly metabolized to [ 18F]fluoride. A model with one tissue compartment and vascular radioactivity was used to analyze gray matter time-activity curves. For [ 18F]FC, an additional term was added to account for [ 18F]fluoride skull spillover into the brain; this term accounted for 70% to 90% of the measured radioactivity concentration at 90 minutes. For [ 18F]FB, mean gray matter parameters were as follows: K1, 10 +/- 3 micro L. min(-1). mL(-1); distribution volume, 0.052 +/- 0.006 (mL/mL). For [ 18F]FC, the values were as follows: K1, 15 +/- 4 micro L. min(-1). mL(-1); V, 0.29 +/- 0.06 mL/mL. The values were consistent with a physiologic model that included brain-to-blood pH difference and the plasma free fraction of the acid. Simulations based on [ 18F]FCWAY human data showed that [ 18F]FC uptake produces significant biases in estimates in regions with low specific binding. These results can be used to correct the tissue [ 18F]FCWAY time-activity data for brain uptake of [ 18F]FC using the measured [ 18F]FC input function.

Segmentation of dynamic PET images using cluster analysis

Quantitative positron emission tomography (PET) studies provide in vivo measurements of dynamic physiological and biochemical processes in humans. A limitation of PET is an inability to provide precise anatomic localization due to relatively poor spatial resolution when compared to magnetic resonance (MR) imaging. Manual placement of region-of-interest (ROI) is commonly used in clinical and research settings in analysis of PET datasets. However, this approach is operator dependent and time-consuming. A semi- or fully-automated ROI delineation (or segmentation) method offers advantages by reducing operator error/subjectivity and thereby improving reproducibility. In this work, we describe an approach to automatically segment dynamic PET images using cluster analysis and we validate our approach with a simulated phantom study and assess its performance with real dynamic PET data. Our preliminary results suggest that cluster analysis can automatically segment tissues in dynamic PET studies and has the potential to replace manual ROI delineation for some applications.

Simultaneous estimation of physiological parameters and the input function--in vivo PET data.

Dynamic imaging with positron emission tomography (PET) is widely used for the in vivo measurement of regional cerebral metabolic rate for glucose (rCMRGlc) with [18F]fluorodeoxy-D-glucose (FDG) and is used for the clinical evaluation of neurological disease. However, in addition to the acquisition of dynamic images, continuous arterial blood sampling is the conventional method to obtain the tracer time-activity curve in blood (or plasma) for the numeric estimation of rCMRGlc in mg glucose/100-g tissue/min. The insertion of arterial lines and the subsequent collection and processing of multiple blood samples are impractical for clinical PET studies because it is invasive, has the remote, but real potential for producing limb ischemia, and it exposes personnel to additional radiation and risks associated with handling blood. In this paper, based on our previously proposed method for extracting kinetic parameters from dynamic PET images, we developed a modified version (post-estimation method) to improve the numerical identifiability of the parameter estimates when we deal with data obtained from clinical studies. We applied both methods to dynamic neurologic FDG PET studies in three adults. We found that the input function and parameter estimates obtained with our noninvasive methods agreed well with those estimated from the gold standard method of arterial blood sampling and that rCMRGlc estimates were highly correlated (r = 0.973). More importantly, no significant difference was found between rCMRGlc estimated by our methods and the gold standard method (P > 0.16). We suggest that our proposed noninvasive methods may offer an advance over existing methods.