The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Paul D Acton - One of the best experts on this subject based on the ideXlab platform.
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optimal number of pinholes in multi pinhole spect for mouse brain imaging a simulation study
Physics in Medicine and Biology, 2005Co-Authors: Riccardo Accorsi, Paul D ActonAbstract:This study simulates a multi-pinhole single-photon emission computed tomography (SPECT) system using the Monte Carlo method, and investigates different multi-pinhole designs for quantitative mouse brain imaging. Prior approaches investigating multi-pinhole SPECT were not often optimal, as the number and geometrical arrangement of pinholes were usually chosen empirically. The present study seeks to optimize the number of pinholes for a given pinhole arrangement, and also for the specific application of quantitative Neuroreceptor binding in the mouse brain. An analytical Monte Carlo simulation based method was used to generate the projection data for various count levels. A three-dimensional ordered-subsets expectation-maximization algorithm was developed and used to reconstruct the images, incorporating a realistic pinhole model for resolution recovery and noise reduction. Although artefacts arising from overlapping projections could be a major problem in multi-pinhole reconstruction, the cold-rod phantom study showed minimal loss of spatial resolution in multi-pinhole systems, compared to a single-pinhole system with the same pinhole diameter. A quantitative study of Neuroreceptor binding sites using a mouse brain phantom and low activity (37 MBq) showed that the multi-pinhole system outperformed the single-pinhole system by maintaining the mean and lowering the variance in the measured uptake ratio. Multi-pinhole collimation can be used to reduce the injected dose and thereby reduce the radiation exposure to the animal. Results also suggest that the nine-pinhole configuration shown in this paper is a good choice for mouse brain imaging.
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optimal number of pinholes in multi pinhole spect for mouse brain imaging a simulation study
Physics in Medicine and Biology, 2005Co-Authors: Riccardo Accorsi, Paul D ActonAbstract:This study simulates a multi-pinhole single-photon emission computed tomography (SPECT) system using the Monte Carlo method, and investigates different multi-pinhole designs for quantitative mouse brain imaging. Prior approaches investigating multi-pinhole SPECT were not often optimal, as the number and geometrical arrangement of pinholes were usually chosen empirically. The present study seeks to optimize the number of pinholes for a given pinhole arrangement, and also for the specific application of quantitative Neuroreceptor binding in the mouse brain. An analytical Monte Carlo simulation based method was used to generate the projection data for various count levels. A three-dimensional ordered-subsets expectation-maximization algorithm was developed and used to reconstruct the images, incorporating a realistic pinhole model for resolution recovery and noise reduction. Although artefacts arising from overlapping projections could be a major problem in multi-pinhole reconstruction, the cold-rod phantom study showed minimal loss of spatial resolution in multi-pinhole systems, compared to a single-pinhole system with the same pinhole diameter. A quantitative study of Neuroreceptor binding sites using a mouse brain phantom and low activity (37 MBq) showed that the multi-pinhole system outperformed the single-pinhole system by maintaining the mean and lowering the variance in the measured uptake ratio. Multi-pinhole collimation can be used to reduce the injected dose and thereby reduce the radiation exposure to the animal. Results also suggest that the nine-pinhole configuration shown in this paper is a good choice for mouse brain imaging.
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automatic segmentation of dynamic Neuroreceptor single photon emission tomography images using fuzzy clustering
European Journal of Nuclear Medicine and Molecular Imaging, 1999Co-Authors: Paul D Acton, Hank F Kung, Lyn S Pilowsky, Peter J EllAbstract:The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) Neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic Neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D2/D3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [123I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r2>0.99, P<0.0001). Similarly, FCM gave segmentation of the [123I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r2=0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic Neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner.
Marc Laruelle - One of the best experts on this subject based on the ideXlab platform.
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Neuroreceptor imaging in psychiatry theory and applications
International Review of Neurobiology, 2005Co-Authors: Gordon W Frankle, Mark Slifstein, Peter S Talbot, Marc LaruelleAbstract:The ability of single photon emisssion computed tomography (SPECT) and positron emission tomography (PET) to image specific biomolecules in the living brain provides a unique tool for clinical researchers. Given this, it is not surprising that the use of Neuroreceptor imaging techniques has become more widespread over the past decade. These tools are currently being used to study neurological and psychiatric disorders and to inform early‐stage drug development. The accurate derivation of receptor parameters from PET or SPECT brain activity data involves model‐based methodology. These models take into account the various sources comprising the total activity observed in the brain and fluctuations in the relative contributions from these sources over the time course of the scan. The goal of this chapter is to first present the theory underlying these models providing the reader with a conceptual framework for understanding this technique. Subsequently, we will discuss the specific methods used to generate the outcome measures from Neuroreceptor studies in detail. Finally, the application of Neuroreceptor imaging to the study of specific psychiatric illnesses will be reviewed.
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Neuroreceptor imaging in psychiatric disorders
Annals of Nuclear Medicine, 2002Co-Authors: W. Gordon Frankle, Marc LaruelleAbstract:Molecular imaging, the study of receptors, transporters and enzymes, as well as other cellular processes, has grown in recent years to be one of the most active neuroimaging areas. The application of single photon emission tomography (SPECT) and positron emission tomography (PET) techniques to the study of psychiatric illness has lead to increased understanding of disease processes as well as validated, in vivo , theories of illness etiology. Within the field of psychiatry these techniques have been applied most widely to the study of schizophrenia. Studies within schizophrenia are largely limited to either the dopamine or serotonin system. This is due in large part to the availability of suitable radiotracers as well as the current theories on the etiology of the illness. Two basic study designs are used when studying schizophrenia using molecular imaging and make up the majority of studies reviewed in this manuscript. The first type, termed “clinical studies,” compares the findings from PET and SPECT studies in those with schizophrenia to normal controls in an attempt to understand the pathophysiology of the illness. The second study design, termed “occupancy studies,” uses these techniques to enhance the understanding of the mechanism of action of the medications used in treating this illness. This review will focus on the findings of molecular imaging studies in schizophrenia, focusing, for the most part, on the serotonin and dopamine systems. Emphasis will be placed on how these findings and techniques are currently being used to inform the development of novel treatments for schizophrenia.
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models and methods for derivation of in vivo Neuroreceptor parameters with pet and spect reversible radiotracers
Nuclear Medicine and Biology, 2001Co-Authors: Mark Slifstein, Marc LaruelleAbstract:Abstract The science of quantitative analysis of PET and SPECT Neuroreceptor imaging studies has grown considerably over the past decade. A number of methods have been proposed in which receptor parameter estimation results from fitting data to a model of the underlying kinetics of ligand uptake in the brain. These approaches have come to be collectively known as model-based methods and several have received widespread use. Here, we briefly review the most frequently used methods and examine their strengths and weaknesses. Kinetic modeling is the most direct implementation of the compartment models, but with some tracers accurate input function measurement and good compartment configuration identification can be difficult to obtain. Other methods were designed to overcome some particular vulnerability to error of classical kinetic modeling, but introduced new vulnerabilities in the process. Reference region methods obviate the need for arterial plasma measurement, but are not as robust to violations of the underlying modeling assumptions as methods using the arterial input function. Graphical methods give estimates of V T without the requirement of compartment model specification, but provide a biased estimator in the presence of statistical noise. True equilibrium methods are quite robust, but their use is limited to experiments with tracers that are suitable for constant infusion. In conclusion, there is no universally "best" method that is applicable to all Neuroreceptor imaging studies, and carefully evaluation of model-based methods is required for each radiotracer.
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effects of statistical noise on graphic analysis of pet Neuroreceptor studies
The Journal of Nuclear Medicine, 2000Co-Authors: Mark Slifstein, Marc LaruelleAbstract:Because of its computational simplicity, the graphic method introduced by Logan et al. is frequently used to analyze time–activity curves of reversible radiotracers measured in brain regions with PET. The graphic method uses a nonlinear transformation of data to variables that have an asymptotically linear relationship. Compared with compartmental analysis of untransformed data, the graphic method enables derivation of regional distribution volumes that are free from assumptions about the underlying compartmental configuration. In this article, we describe statistical bias associated with this nonlinear transformation method. Methods: Theoretic analysis, Monte Carlo simulation, and statistical analysis of PET data were used to test the graphic method for bias. Results: Mean zero noise is associated with underestimation of distribution volumes when data are analyzed with graphic analysis, whereas this effect does not occur when the same data are analyzed by nonlinear regression and compartmental analysis. Moreover, this effect depends on the magnitude of the distribution volume, so that the bias is more pronounced in regions with high receptor density than regions with low receptor density or no receptors (region of reference). Conclusion: These results indicate that conventional kinetic analysis of untransformed data is less sensitive to mean zero noise than is graphic analysis of nonlinearly transformed data.
Robert B Innis - One of the best experts on this subject based on the ideXlab platform.
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strategies to improve Neuroreceptor parameter estimation by linear regression analysis
Journal of Cerebral Blood Flow and Metabolism, 2002Co-Authors: Masanori Ichise, Hiroshi Toyama, Robert B Innis, Richard E CarsonAbstract:In an attempt to improve Neuroreceptor distribution volume (V) estimates, the authors evaluated three alternative linear methods to Logan graphical analysis (GA): GA using total least squares (TLS)...
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strategies to improve Neuroreceptor parameter estimation by linear regression analysis
Journal of Cerebral Blood Flow and Metabolism, 2002Co-Authors: Masanori Ichise, Hiroshi Toyama, Robert B Innis, Richard E CarsonAbstract:In an attempt to improve Neuroreceptor distribution volume (V) estimates, the authors evaluated three alternative linear methods to Logan graphical analysis (GA): GA using total least squares (TLS), and two multilinear analyses, MA1 and MA2, based on mathematical rearrangement of GA equation and two-tissue compartments, respectively, using simulated and actual PET data of two receptor tracers, [(18)F]FCWAY and [(11)C]MDL 100,907. For simulations, all three methods decreased the noise-induced GA bias (up to 30%) at the expense of increased variability. The bias reduction was most pronounced for MA1, moderate to large for MA2, and modest to moderate for TLS. In addition, GA, TLS, and MA1, methods that used only a portion of the data (T > t*, chosen by an automatic process), showed a small underestimation for [(11)C]MDL 100,907 with its slow kinetics, due to selection of t* before the true point of linearity. These noniterative methods are computationally simple, allowing efficient pixelwise parameter estimation. For tracers with kinetics that permit t* to be accurately identified within the study duration, MA1 appears to be the best. For tracers with slow kinetics and low to moderate noise, however, MA2 may provide the lowest bias while maintaining computational ease for pixelwise parameter estimation.
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Neuroreceptor imaging with spect
The Journal of Clinical Psychiatry, 1992Co-Authors: Robert B InnisAbstract:Single photon emission computed tomography (SPECT) imaging can provide useful measurements of brain receptors and endogenous neurotransmitters and may have significant experimental and clinical applications. This presentation reviews the use of SPECT for Neuroreceptor imaging. Studies of receptors for benzodiazepines, dopamine D2 agents, and dopamine reuptake sites will be used to exemplify the capabilities of SPECT. Tracers labeled with the radioisotope 125I have high affinity, high brain uptake, and high ratios of specific to nonspecific binding. Imaging studies of human and nonhuman primate brain will be presented, and the potential clinical applicability of these agents will be discussed.
Bruce R Rosen - One of the best experts on this subject based on the ideXlab platform.
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effects of flow changes on radiotracer binding simultaneous measurement of Neuroreceptor binding and cerebral blood flow modulation
Journal of Cerebral Blood Flow and Metabolism, 2017Co-Authors: Christin Y Sander, Joseph B Mandeville, Hsiaoying Wey, Ciprian Catana, Jacob M Hooker, Bruce R RosenAbstract:The potential effects of changes in blood flow on the delivery and washout of radiotracers has been an ongoing question in PET bolus injection studies. This study provides practical insight into this topic by experimentally measuring cerebral blood flow (CBF) and Neuroreceptor binding using simultaneous PET/MRI. Hypercapnic challenges (7% CO2) were administered to non-human primates in order to induce controlled increases in CBF, measured with pseudo-continuous arterial spin labeling. Simultaneously, dopamine D2/D3 receptor binding of [11C]raclopride or [18F]fallypride was monitored with dynamic PET. Experiments showed that neither time activity curves nor quantification of binding through binding potentials ( BPND) were measurably affected by CBF increases, which were larger than two-fold. Simulations of experimental procedures showed that even large changes in CBF should have little effect on the time activity curves of radiotracers, given a set of realistic assumptions. The proposed method can be applied to experimentally assess the flow sensitivity of other radiotracers. Results demonstrate that CBF changes, which often occur due to behavioral tasks or pharmacological challenges, do not affect PET [11C]raclopride or [18F]fallypride binding studies and their quantification. The results from this study suggest flow effects may have limited impact on many PET Neuroreceptor tracers with similar properties.
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a regularized full reference tissue model for pet Neuroreceptor mapping
NeuroImage, 2016Co-Authors: Joseph B Mandeville, Christin Y Sander, Hsiaoying Wey, Jacob M Hooker, Hanne D Hansen, Gitte M Knudsen, Claus Svarer, Bruce R RosenAbstract:Abstract The full reference tissue model (FRTM) is a PET analysis framework that includes both free and specifically bound compartments within tissues, together with rate constants defining association and dissociation from the specifically bound compartment. The simplified reference tissue model (SRTM) assumes instantaneous exchange between tissue compartments, and this “1-tissue” approximation reduces the number of parameters and enables more robust mapping of non-displaceable binding potentials. Simulations based upon FRTM have shown that SRTM exhibits biases that are spatially dependent, because biases depend upon binding potentials. In this work, we describe a regularized model (rFRTM) that employs a global estimate of the dissociation rate constant from the specifically bound compartment (k4). The model provides an internal calibration for optimizing k4 through the reference-region outflow rate k2′, a model parameter that should be a global constant but varies regionally in SRTM. Estimates of k4 by rFRTM are presented for four PET radioligands. We show that SRTM introduces bias in parameter estimates by assuming an infinite value for k4, and that rFRTM ameliorates bias with an appropriate choice of k4. Theoretical considerations and simulations demonstrate that rFRTM reduces bias in non-displaceable binding potentials. A two-parameter reduction of the model (rFRTM2) provides robust mapping at a voxel-wise level. With a structure similar to SRTM, the model is easily implemented and can be applied as a PET reference region analysis that reduces parameter bias without substantially altering parameter variance.
Hugh Gurling - One of the best experts on this subject based on the ideXlab platform.
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Neuroreceptor subunit genes and the genetic susceptibility to gilles de la tourette syndrome
Biological Psychiatry, 1997Co-Authors: P M Brett, David Curtis, Mary M Robertson, Hugh GurlingAbstract:Segregation studies have shown that Gilles de la Tourette Syndrome (GTS) is probably transmitted as an autosomal dominant gene disorder and can therefore be studied by classical linkage analysis to identify susceptibility loci. Many neurotransmitter systems have been implicated in the etiology of GTS. Most recently the alpha-1 subunit of the glycine receptor etiologically responsible for hyperekplexia has been hypothesized as the cause of the susceptibility to GTS. Because of this and the high concentration of other Neuroreceptor genes at 5q33-35, it was decided to study this region and the associated gene cluster on chromosome 4p12-16 in a large British kindred multiply affected with GTS and chronic motor tics. The genotypes of the microsatellite markers at these loci were determined by polymerase chain reaction. The allele data were analyzed using both parametric and nonparametric methods. Approximate multipoint maps were constructed across the regions of interest using FASTLINK. All of the lod scores produced were negative, showing no evidence of linkage to GTS in the family studied. The multipoint maps showed good exclusion across these regions. The glycine receptor gene responsible for hyperekplexia and the other Neuroreceptor genes examined in this paper are not involved in the etiology of GTS in this large pedigree.
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the genetic susceptibility to gilles de la tourette syndrome in a large multiple affected british kindred linkage analysis excludes a role for the genes coding for dopamine d1 d2 d3 d4 d5 receptors dopamine beta hydroxylase tyrosinase and tyrosine hy
Biological Psychiatry, 1995Co-Authors: P M Brett, David Curtis, Mary M Robertson, Hugh GurlingAbstract:Segregation analyses have shown that Gilles de la Tourette Syndrome (GTS) is transmitted as an autosomal dominant gene disorder indicating that classical linkage analysis should be able to identify susceptibility loci. Previous studies of GTS have included investigations of Neuroreceptor function, neurotransmitters, and their metabolites as well as neurotransmitter-related enzymes in an attempt to determine the pathophysiology of GTS. The neurotransmitter systems most often thought to be involved in GTS include those involving adrenaline, noradrenaline, and dopamine. We have carried out research to test the hypothesis that genes encoding proteins in the catecholamine pathways may contribute to the genetic etiology of GTS. Polymorphic markers at or near the D1, D2, D3, D4, D5 Neuroreceptor gene loci as well as at the genes encoding dopamine beta hydroxylase (DBH), tyrosinase (TY) and tyrosine hydroxylase (TH) were studied in one large multiple affected pedigree. The linkage results of this investigation exclude a major role of these candidate genes in the etiology of GTS in the pedigree.