Analysis Of Positron Emission Tomography Data Research Articles

Comparative Evaluation of Three TSPO PET Radiotracers in a LPS-Induced Model of Mild Neuroinflammation in Rats

PurposeOver the past 20 years, neuroinflammation (NI) has increasingly been recognised as having an important role in many neurodegenerative diseases, including Alzheimer’s disease. As such, being able to image NI non-invasively in patients is critical to monitor pathological processes and potential therapies targeting neuroinflammation. The translocator protein (TSPO) has proven a reliable NI biomarker for positron emission tomography (PET) imaging. However, if TSPO imaging in acute conditions such as stroke provides strong and reliable signals, TSPO imaging in neurodegenerative diseases has proven more challenging. Here, we report results comparing the recently developed TSPO tracers [18F]GE-180 and [18F]DPA-714 with (R)-[11C]PK11195 in a rodent model of subtle focal inflammation.ProceduresAdult male Wistar rats were stereotactically injected with 1 μg lipopolysaccharide in the right striatum. Three days later, animals underwent a 60-min PET scan with (R)-[11C]PK11195 and [18F]GE-180 (n = 6) or [18F]DPA-714 (n = 6). Ten animals were scanned with either [18F]GE-180 (n = 5) or [18F]DPA-714 (n = 5) only. Kinetic analysis of PET data was performed using the simplified reference tissue model (SRTM) with a contralateral reference region or a novel data-driven input to estimate binding potential BPND. Autoradiography and immunohistochemistry were performed to confirm in vivo results.ResultsAt 40–60 min post-injection, [18F]GE-180 dual-scanned animals showed a significantly increased core/contralateral uptake ratio vs. the same animals scanned with (R)-[11C]PK11195 (3.41 ± 1.09 vs. 2.43 ± 0.39, p = 0.03); [18]DPA-714 did not (2.80 ± 0.69 vs. 2.26 ± 0.41). Kinetic modelling with a contralateral reference region identified significantly higher binding potential (BPND) in the core of the LPS injection site with [18F]GE-180 but not with [18F]DPA-714 vs. (R)-[11C]PK11195. A cerebellar reference region and novel data-driven input to the SRTM were unable to distinguish differences in tracer BPND.ConclusionsSecond-generation TSPO-PET tracers are able to accurately detect mild-level NI. In this model, [18F]GE-180 shows a higher core/contralateral ratio and BPND when compared to (R)-[11C]PK11195, while [18F]DPA-714 did not.

Multimodality Molecular Imaging of Cardiac Cell Transplantation: Part II. In Vivo Imaging of Bone Marrow Stromal Cells in Swine with PET/CT and MR Imaging.

Purpose To quantitatively determine the limit of detection of marrow stromal cells (MSC) after cardiac cell therapy (CCT) in swine by using clinical positron emission tomography (PET) reporter gene imaging and magnetic resonance (MR) imaging with cell prelabeling. Materials and Methods Animal studies were approved by the institutional administrative panel on laboratory animal care. Seven swine received 23 intracardiac cell injections that contained control MSC and cell mixtures of MSC expressing a multimodality triple fusion (TF) reporter gene (MSC-TF) and bearing superparamagnetic iron oxide nanoparticles (NP) (MSC-TF-NP) or NP alone. Clinical MR imaging and PET reporter gene molecular imaging were performed after intravenous injection of the radiotracer fluorine 18-radiolabeled 9-[4-fluoro-3-(hydroxyl methyl) butyl] guanine ((18)F-FHBG). Linear regression analysis of both MR imaging and PET data and nonlinear regression analysis of PET data were performed, accounting for multiple injections per animal. Results MR imaging showed a positive correlation between MSC-TF-NP cell number and dephasing (dark) signal (R(2) = 0.72, P = .0001) and a lower detection limit of at least approximately 1.5 × 10(7) cells. PET reporter gene imaging demonstrated a significant positive correlation between MSC-TF and target-to-background ratio with the linear model (R(2) = 0.88, P = .0001, root mean square error = 0.523) and the nonlinear model (R(2) = 0.99, P = .0001, root mean square error = 0.273) and a lower detection limit of 2.5 × 10(8) cells. Conclusion The authors quantitatively determined the limit of detection of MSC after CCT in swine by using clinical PET reporter gene imaging and clinical MR imaging with cell prelabeling. (©) RSNA, 2016 Online supplemental material is available for this article.

Federated optimisation of kinetic analysis problems

Positron Emission Tomography (PET) data is intrinsically dynamic, and kinetic analysis of dynamic PET data can substantially augment the information provided by static PET reconstructions. Yet despite the insights into disease that kinetic analysis offers, it is not used clinically and seldom used in research beyond the preclinical stage. The utility of PET kinetic analysis is hampered by several factors including spatial inconsistency within regions of homogeneous tissue and relative computational expense when fitting complex models to individual voxels. Even with sophisticated algorithms inconsistencies can arise because local optima frequently have narrow basins of convergence, are surrounded by relatively flat (uninformative) regions, have relatively low-gradient valley floors, or combinations thereof. Based on the observation that cost functions for individual voxels frequently bear some resemblance to each-other, this paper proposes the federated optimisation of the individual kinetic analysis problems within a given image. This approach shares parameters proposed during optimisation with other, similar voxels. Federated optimisation exploits the redundancy typical of large medical images to improve the optimisation residuals, computational efficiency and, to a limited extent, image consistency. This is achieved without restricting the formulation of the kinetic model, resorting to an explicit regularisation parameter, or limiting the resolution at which parameters are computed.

A Functional Approach to Deconvolve Dynamic Neuroimaging Data

Positron emission tomography (PET) is an imaging technique which can be used to investigate chemical changes in human biological processes such as cancer development or neurochemical reactions. Most dynamic PET scans are currently analyzed based on the assumption that linear first-order kinetics can be used to adequately describe the system under observation. However, there has recently been strong evidence that this is not the case. To provide an analysis of PET data which is free from this compartmental assumption, we propose a nonparametric deconvolution and analysis model for dynamic PET data based on functional principal component analysis. This yields flexibility in the possible deconvolved functions while still performing well when a linear compartmental model setup is the true data generating mechanism. As the deconvolution needs to be performed on only a relative small number of basis functions rather than voxel by voxel in the entire three-dimensional volume, the methodology is both robust to typical brain imaging noise levels while also being computationally efficient. The new methodology is investigated through simulations in both one-dimensional functions and 2D images and also applied to a neuroimaging study whose goal is the quantification of opioid receptor concentration in the brain.

Spectral Analysis of Dynamic PET Studies: A Review of 20 Years of Method Developments and Applications.

In Positron Emission Tomography (PET), spectral analysis (SA) allows the quantification of dynamic data by relating the radioactivity measured by the scanner in time to the underlying physiological processes of the system under investigation. Among the different approaches for the quantification of PET data, SA is based on the linear solution of the Laplace transform inversion whereas the measured arterial and tissue time-activity curves of a radiotracer are used to calculate the input response function of the tissue. In the recent years SA has been used with a large number of PET tracers in brain and nonbrain applications, demonstrating that it is a very flexible and robust method for PET data analysis. Differently from the most common PET quantification approaches that adopt standard nonlinear estimation of compartmental models or some linear simplifications, SA can be applied without defining any specific model configuration and has demonstrated very good sensitivity to the underlying kinetics. This characteristic makes it useful as an investigative tool especially for the analysis of novel PET tracers. The purpose of this work is to offer an overview of SA, to discuss advantages and limitations of the methodology, and to inform about its applications in the PET field.

PET imaging evaluation of [(18)F]DBT-10, a novel radioligand specific to α7 nicotinic acetylcholine receptors, in nonhuman primates.

Positron emission tomography (PET) radioligands specific to α7 nicotinic acetylcholine receptors (nAChRs) afford in vivo imaging of this receptor for neuropathologies such as Alzheimer's disease, schizophrenia, and substance abuse. This work aims to characterize the kinetic properties of an α7-nAChR-specific radioligand, 7-(1,4-diazabicyclo[3.2.2]nonan-4-yl)-2-[(18)F]-fluorodibenzo[b,d]thiophene 5,5-dioxide ([(18)F]DBT-10), in nonhuman primates. [(18)F]DBT-10 was produced via nucleophilic substitution of the nitro-precursor. Four Macaca mulatta subjects were imaged with [(18)F]DBT-10 PET, with measurement of [(18)F]DBT-10 parent concentrations and metabolism in arterial plasma. Baseline PET scans were acquired for all subjects. Following one scan, ex vivo analysis of brain tissue was performed to inspect for radiolabeled metabolites in brain. Three blocking scans with 0.69 and 1.24 mg/kg of the α7-nAChR-specific ligand ASEM were also acquired to assess dose-dependent blockade of [(18)F]DBT-10 binding. Kinetic analysis of PET data was performed using the metabolite-corrected input function to calculate the parent fraction corrected total distribution volume (V T/f P). [(18)F]DBT-10 was produced within 90 min at high specific activities of 428 ± 436 GBq/μmol at end of synthesis. Metabolism of [(18)F]DBT-10 varied across subjects, stabilizing by 120 min post-injection at parent fractions of 15-55%. Uptake of [(18)F]DBT-10 in brain occurred rapidly, reaching peak standardized uptake values (SUVs) of 2.9-3.7 within 30 min. The plasma-free fraction was 18.8 ± 3.4%. No evidence for radiolabeled [(18)F]DBT-10 metabolites was found in ex vivo brain tissue samples. Kinetic analysis of PET data was best described by the two-tissue compartment model. Estimated V T/f P values were 193-376 ml/cm(3) across regions, with regional rank order of thalamus > frontal cortex > striatum > hippocampus > occipital cortex > cerebellum > pons. Dose-dependent blockade of [(18)F]DBT-10 binding by structural analog ASEM was observed throughout the brain, and occupancy plots yielded a V ND/f P estimate of 20 ± 16 ml/cm(3). These results demonstrate suitable kinetic properties of [(18)F]DBT-10 for in vivo quantification of α7-nAChR binding in nonhuman primates.

In vivo imaging of brain androgen receptors in rats: a [18F]FDHT PET study

IntroductionSteroid hormones like androgens play an important role in the development and maintenance of several brain functions. Androgens can act through androgen receptors (AR) in the brain. This study aims to demonstrate the feasibility of positron emission tomography (PET) with 16β-[18F]fluoro-5α-dihydrotestosterone ([18F]FDHT) to image AR expression in the brain. MethodsMale Wistar rats were either orchiectomized to inhibit endogenous androgen production or underwent sham-surgery. Fifteen days after surgery, rats were subjected to a 90-min dynamic [18F]FDHT PET scan with arterial blood sampling. In a subset of orchiectomized rats, 1mg/kg dihydrotestosterone was co-injected with the tracer in order to saturate the AR. Plasma samples were analyzed for the presence of radioactive metabolites by radio-TLC. Pharmacokinetic modeling was performed to quantify brain kinetics of the tracer. After the PET scan, the animals were terminated for ex-vivo biodistribution. ResultsPET imaging and ex vivo biodistribution studies showed low [18F]FDHT uptake in all brain regions, except pituitary. [18F]FDHT uptake in the surrounding cranial bones was high and increased over time. [18F]FDHT was rapidly metabolized in rats. Metabolism was significantly faster in orchiectomized rats than in sham-orchiectomized rats. Quantitative analysis of PET data indicated substantial spill-over of activity from cranial bones into peripheral brain regions, which prevented further analysis of peripheral brain regions. Logan graphical analysis and kinetic modeling using 1- and 2-tissue compartment models showed reversible and homogenously distributed tracer uptake in central brain regions. [18F]FDHT uptake in the brain could not be blocked by endogenous androgens or administration of dihydrotestosterone. ConclusionThe results of this study indicate that imaging of AR availability in rat brain with [18F]FDHT PET is not feasible. The low AR expression in the brain, the rapid metabolism of [18F]FDHT in rats and the poor brain penetration of the tracer likely contributed to the poor performance of [18F]FDHT PET in this study.

Poster — Thur Eve — 44: Linearization of Compartmental Models for More Robust Estimates of Regional Hemodynamic, Metabolic and Functional Parameters using DCE‐CT/PET Imaging

Quantitative analysis of dynamic positron emission tomography (PET) data usually involves minimizing a cost function with nonlinear regression, wherein the choice of starting parameter values and the presence of local minima affect the bias and variability of the estimated kinetic parameters. These nonlinear methods can also require lengthy computation time, making them unsuitable for use in clinical settings. Kinetic modeling of PET aims to estimate the rate parameter k3, which is the binding affinity of the tracer to a biological process of interest and is highly susceptible to noise inherent in PET image acquisition. We have developed linearized kinetic models for kinetic analysis of dynamic contrast enhanced computed tomography (DCE‐CT)/PET imaging, including a 2‐compartment model for DCE‐CT and a 3‐compartment model for PET. Use of kinetic parameters estimated from DCE‐CT can stabilize the kinetic analysis of dynamic PET data, allowing for more robust estimation of k3. Furthermore, these linearized models are solved with a non‐negative least squares algorithm and together they provide other advantages including: 1) only one possible solution and they do not require a choice of starting parameter values, 2) parameter estimates are comparable in accuracy to those from nonlinear models, 3) significantly reduced computational time. Our simulated data show that when blood volume and permeability are estimated with DCE‐CT, the bias of k3 estimation with our linearized model is 1.97 ± 38.5% for 1,000 runs with a signal‐to‐noise ratio of 10. In summary, we have developed a computationally efficient technique for accurate estimation of k3 from noisy dynamic PET data.

Dynamic functional imaging of brain glucose utilization using fPET-FDG

Glucose is the principal source of energy for the brain and yet the dynamic response of glucose utilization to changes in brain activity is still not fully understood. Positron emission tomography (PET) allows quantitative measurement of glucose metabolism using 2-[18F]-fluorodeoxyglucose (FDG). However, FDG PET in its current form provides an integral (or average) of glucose consumption over tens of minutes and lacks the temporal information to capture physiological alterations associated with changes in brain activity induced by tasks or drug challenges. Traditionally, changes in glucose utilization are inferred by comparing two separate scans, which significantly limits the utility of the method. We report a novel method to track changes in FDG metabolism dynamically, with higher temporal resolution than exists to date and within a single session. Using a constant infusion of FDG, we demonstrate that our technique (termed fPET-FDG) can be used in an analysis pipeline similar to fMRI to define within-session differential metabolic responses. We use visual stimulation to demonstrate the feasibility of this method. This new method has a great potential to be used in research protocols and clinical settings since fPET-FDG imaging can be performed with most PET scanners and data acquisition and analysis are straightforward. fPET-FDG is a highly complementary technique to MRI and provides a rich new way to observe functional changes in brain metabolism.

Preclinical imaging evaluation of novel TSPO-PET ligand 2-(5,7-Diethyl-2-(4-(2-[(18)F]fluoroethoxy)phenyl)pyrazolo[1,5-a]pyrimidin-3-yl)-N,N-diethylacetamide ([ (18)F]VUIIS1008) in glioma.

Translocator protein (TSPO) concentrations are elevated in glioma, suggesting a role for TSPO positron emission tomography (PET) imaging in this setting. In preclinical PET studies, we evaluated a novel, high-affinity TSPO PET ligand, [(18)F]VUIIS1008, in healthy mice and glioma-bearing rats. Dynamic PET data were acquired simultaneously with [(18)F]VUIIS1008 injection, with binding reversibility and specificity evaluated in vivo by non-radioactive ligand displacement or blocking. Compartmental analysis of PET data was performed using metabolite-corrected arterial input functions. Imaging was validated with histology and immunohistochemistry. [(18)F]VUIIS1008 exhibited rapid uptake in TSPO-rich organs. PET ligand uptake was displaceable with non-radioactive VUIIS1008 or PBR06 in mice. Tumor accumulation of [(18)F]VUIIS1008 was blocked by pretreatment with VUIIS1008 in rats. [(18)F]VUIIS1008 exhibited improved tumor-to-background ratio and higher binding potential in tumors compared to a structurally similar pyrazolopyrimidine TSPO ligand, [(18)F]DPA-714. The PET ligand [(18)F]VUIIS1008 exhibits promising characteristics as a tracer for imaging glioma. Further translational studies appear warranted.

Imaging Brain Metabolism and Pathology in Alzheimer's Disease with Positron Emission Tomography.

Current Positron Emission Tomography (PET) biomarkers for Alzheimer’s disease (AD) assess either neuronal function, or associated pathological features of this common neurodegenerative disease. The most widely accepted clinical PET tool for AD is 18-fluorodeoxyglucose PET (FDG-PET), which measures cerebral metabolic glucose utilization rate (CMRglc). FDG-PET is a marker of synaptic activity, neuronal function, and neuronal metabolic activity. AD is characterized by a distinct pattern of hypometabolism, as seen with the FDG images. This pattern can show variability across different subjects and is present before a patient is demented, specifically in amnestic mild cognitive impairment a clinical diagnosis defined as an intermediate state from normal aging to dementia. In addition to FDG PET, novel PET approaches assess known pathological hallmarks of AD including extracellular amyloid-beta plaques (Aβ) and intracellular neurofibrillary tangles composed of tau fibrils. Already, amyloid PET imaging is a tool that allows in vivo imaging of extracellular beta-amyloid levels. Efforts to bring tau imaging into clinical use continue, but this approach is hampered by the intracellular nature of tau protein deposition, subsequent weak radiotracer binding, and low image contrast. Several new candidate probes for tau-specific PET imaging are currently available but have not found their way into broad clinical applications.This study gives an overview of the most recent PET-based neuroimaging techniques for AD. We place special emphasis on PET data analysis and interpretation techniques, as well as radiochemistry for imaging metabolism and assessing Aβ and tau pathology.

The Brain’s Error-Detecting Mechanism – a PET Study

The present study addresses the cerebral support for the mechanism of error detection (ED) operating during conscious execution of incorrect actions (deceptions) and in the resting state. Local cerebral blood flow was measured by positron emission tomography (PET), and demonstrated involvement of the anterior cingulate gyrus in processes associated with conscious deception. The data obtained here showed that ED operates in persistently executed errors – conscious control of ED was shown to be impossible. The hypothesis that failure of ED is an important factor in the formation of obsessive-compulsive disorder (OCD) was supported by analysis of PET data on the rate of glucose metabolism in the state of operative rest. Normative data from healthy subjects were compared with results obtained from patients with diagnoses of OCD and Tourette’s syndrome. Patients showed decreases in glucose metabolism in the anterior cingulate gyrus, which can be regarded as a reflection of abnormal functioning of the cerebral ED system.

Modeling of PET data in CNS drug discovery and development

Positron emission tomography (PET) is increasingly used in drug discovery and development for evaluation of CNS drug disposition and for studies of disease biomarkers to monitor drug effects on brain pathology. The quantitative analysis of PET data is based on kinetic modeling of radioactivity concentrations in plasma and brain tissue compartments. A number of quantitative methods of analysis have been developed that allow the determination of parameters describing drug pharmacokinetics and interaction with target binding sites in the brain. The optimal method of quantification depends on the properties of the radiolabeled drug or radioligand and the binding site studied. We here review the most frequently used methods for quantification of PET data in relation to CNS drug discovery and development. The utility of PET kinetic modeling in the development of novel CNS drugs is illustrated by examples from studies of the brain kinetic properties of radiolabeled drug molecules.

SAKE: A new quantification tool for positron emission tomography studies

In dynamic positron emission tomography (PET) studies, spectral analysis (SA) refers to a data-driven quantification method, based on a single-input single-output model for which the transfer function is described by a sum of exponential terms. SA allows to quantify numerosities, amplitudes and eigenvalues of the transfer function allowing, in this way, to separate kinetic components of the tissue tracer activity with minimal model assumptions. The SA model can be solved with a linear estimator alone or with numerical filters, resulting in different types of SA approaches. Once estimated the number, amplitudes and eigenvalues of the transfer function, one can distinguish the presence in the system of irreversible and/or reversible components as well as derive parameters of physiological significance.These characteristics make it an appealing alternative method to compartmental models which are widely used for the quantitative analysis of dynamic studies acquired with PET. However, despite its applicability to a large number of PET tracers, its implementation is not straightforward and its utilization in the nuclear medicine community has been limited especially by the lack of an user-friendly software application.In this paper we proposed SAKE, a computer program for the quantitative analysis of PET data through the main SA methods. SAKE offers a unified pipeline of analysis usable also by people with limited computer knowledge but with high interest in SA.

Introduction to the analysis of PET data in oncology

Several reviews on specific topics related to positron emission tomography (PET) ranging in complexity from introductory to highly technical have already been published. This introduction to the analysis of PET data was written as a simple guide of the different phases of analysis of a given PET dataset, from acquisition to preprocessing, to the final data analysis. Although sometimes issues specific to PET in neuroimaging will be mentioned for comparison, most of the examples and applications provided will refer to oncology. Due to the limitations of space we couldn't address each issue comprehensively but, rather, we provided a general overview of each topic together with the references that the interested reader should consult. We will assume a familiarity with the basic principles of PET imaging.

Novel spatial analysis method for PET images using 3D moment invariants: Applications to Parkinson's disease

We present a novel analysis method for positron emission tomography (PET) data that uses the spatial characteristics of the radiotracer's distribution within anatomically-defined regions of interest (ROIs) to provide an independent feature that may aid in characterizing pathological and normal states. The analysis of PET data for research purposes traditionally involves kinetic modeling of the concentration of the radiotracer over time within a ROI to derive parameters related to the uptake/binding of the radiotracer in the body. Here we describe an analysis method to quantify the spatial changes present in PET images based on 3D shape descriptors that are invariant to translation, scaling, and rotation, called 3D moment invariants (3DMIs). An ROI can therefore be characterized not only by the radiotracer's uptake rate constant or binding potential within the ROI, but also the 3D spatial shape and distribution of the radioactivity throughout the ROI. This is particularly relevant in Parkinson's disease (PD), where both the kinetic and the spatial distribution of the tracer are known to change due to disease: the posterior parts of the striatum (in particular in the putamen) are affected before the anterior parts. Here we show that 3DMIs are able to quantify the spatial distribution of PET radiotracer images allowing for discrimination between healthy controls and PD subjects. More importantly, 3DMIs are found to be well correlated with subjects' scores on the United Parkinson's Disease Rating Scale (a clinical measure of disease severity) in all anatomical regions studied here (putamen, caudate and ventral striatum). On the other hand, kinetic parameters only show significant correlation to clinically-assessed PD severity in the putamen. We also find that 3DMI-characterized changes in spatial patterns of dopamine release in response to l-dopa medication are significantly correlated with PD severity. These findings suggest that quantitative studies of a radiotracer's spatial distribution may provide complementary information to kinetic modeling that is relatively robust to intersubject variability and may contribute novel information in PET neuroimaging studies.

Reproducible Analysis of Rat Brain PET Studies Using an Additional [18F]NaF Scan and an MR-Based ROI Template

Background. An important step in the analysis of positron emission tomography (PET) studies of the brain is the definition of regions of interest (ROI). Image coregistration, ROI analysis, and quantification of brain PET data in small animals can be observer dependent. The purpose of this study was to investigate the feasibility of ROI analysis based on a standard MR template and an additional [18F]NaF scan. Methods. [18F]NaF scans of 10 Wistar rats were coregistered with a standard MR template by 3 observers and derived transformation matrices were applied to corresponding [11C]AF150(S) images. Uptake measures were derived for several brain regions delineated using the MR template. Overall agreement between the 3 observers was assessed by interclass correlation coefficients (ICC) of uptake data. In addition, [11C]AF150(S) ROI data were compared with ex vivo biodistribution data. Results. For all brain regions, ICC analysis showed excellent agreement between observers. Reproducibility, estimated by calculation of standard deviation of the between-observer differences, was demonstrated by an average of 17% expressed as coefficient of variation. Uptake of [11C]AF150(S) derived from ROI analysis closely matched ex vivo biodistribution data. Conclusions. The proposed method provides a reproducible and tracer-independent method for ROI analysis of rat brain PET data.

Improved kinetic analysis of dynamic PET data with optimized HYPR‐LR

Highly constrained backprojection-local reconstruction (HYPR-LR) has made a dramatic impact on magnetic resonance angiography (MRA) and shows promise for positron emission tomography (PET) because of the improvements in the signal-to-noise ratio (SNR) it provides dynamic images. For PET in particular, HYPR-LR could improve kinetic analysis methods that are sensitive to noise. In this work, the authors closely examine the performance of HYPR-LR in the context of kinetic analysis, they develop an implementation of the algorithm that can be tailored to specific PET imaging tasks to minimize bias and maximize improvement in variance, and they provide a framework for validating the use of HYPR-LR processing for a particular imaging task. HYPR-LR can introduce errors into non sparse PET studies that might bias kinetic parameter estimates. An implementation of HYPR-LR is proposed that uses multiple temporally summed composite images that are formed based on the kinetics of the tracer being studied (HYPR-LR-MC). The effects of HYPR-LR-MC and of HYPR-LR using a full composite formed with all the frames in the study (HYPR-LR-FC) on the kinetic analysis of Pittsburgh compound-B ([11C]-PIB) are studied. HYPR-LR processing is compared to spatial smoothing. HYPR-LR processing was evaluated using both simulated and human studies. Nondisplaceable binding potential (BP(ND)) parametric images were generated from fifty noise realizations of the same numerical phantom and eight [(11)C]-PIB positive human scans before and after HYPR-LR processing or smoothing using the reference region Logan graphical method and receptor parametric mapping (RPM2). The bias and coefficient of variation in the frontal and parietal cortex in the simulated parametric images were calculated to evaluate the absolute performance of HYPR-LR processing. Bias in the human data was evaluated by comparing parametric image BP(ND) values averaged over large regions of interest (ROIs) to Logan estimates of the BP(ND) from TACs averaged over the same ROIs. Variance was assessed qualitatively in the parametric images and semiquantitatively by studying the correlation between voxel BP(ND) estimates from Logan analysis and RPM2. Both the simulated and human data show that HYPR-LR-FC overestimates BP(ND) values in regions of high [(11)C]-PIB uptake. HYPR-LR-MC virtually eliminates this bias. Both implementations of HYPR-LR reduce variance in the parametric images generated with both Logan analysis and RPM2, and HYPR-LR-FC provides a greater reduction in variance. This reduction in variance nearly eliminates the noise-dependent Logan bias. The variance reduction is greater for the Logan method, particularly for HYPR-LR-MC, and the variance in the resulting Logan images is comparable to that in the RPM2 images. HYPR-LR processing compares favorably with spatial smoothing, particularly when the data are analyzed with the Logan method, as it provides a reduction in variance with no loss of spatial resolution. HYPR-LR processing shows significant potential for reducing variance in parametric images, and can eliminate the noise-dependent Logan bias. HYPR-LR-FC processing provides the greatest reduction in variance but introduces a positive bias into the BP(ND) of high-uptake border regions. The proposed method for forming HYPR composite images, HYPR-LR-MC, eliminates this bias at the cost of less variance reduction.

Widespread abnormality of the γ-aminobutyric acid-ergic system in Tourette syndrome

Dysfunction of the γ-aminobutyric acid-ergic system in Tourette syndrome may conceivably underlie the symptoms of motor disinhibition presenting as tics and psychiatric manifestations, such as attention deficit hyperactivity disorder and obsessive-compulsive disorder. The purpose of this study was to identify a possible dysfunction of the γ-aminobutyric acid-ergic system in Tourette patients, especially involving the basal ganglia-thalamo-cortical circuits and the cerebellum. We studied 11 patients with Tourette syndrome and 11 healthy controls. Positron emission tomography procedure: after injection of 20 mCi of [(11)C]flumazenil, dynamic emission images of the brain were acquired. Structural magnetic resonance imaging scans were obtained to provide an anatomical framework for the positron emission tomography data analysis. Images of binding potential were created using the two-step version of the simplified reference tissue model. The binding potential images then were spatially normalized, smoothed and compared between groups using statistical parametric mapping. We found decreased binding of GABA(A) receptors in Tourette patients bilaterally in the ventral striatum, globus pallidus, thalamus, amygdala and right insula. In addition, the GABA(A) receptor binding was increased in the bilateral substantia nigra, left periaqueductal grey, right posterior cingulate cortex and bilateral cerebellum. These results are consistent with the longstanding hypothesis that circuits involving the basal ganglia and thalamus are disinhibited in Tourette syndrome patients. In addition, the abnormalities in GABA(A) receptor binding in the insula and cerebellum appear particularly noteworthy based upon recent evidence implicating these structures in the generation of tics.

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.