A comparison of recent parametric neuroreceptor mapping approaches based on measurements with the high affinity PET radioligands [11C]FLB 457 and [11C]WAY 100635 (original) (raw)
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A factor-image framework to quantification of brain receptor dynamic PET studies
IEEE Transactions on Signal Processing, 2000
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.
Maps of receptor binding parameters in the human brain ? a kinetic analysis of PET measurements
European Journal of Nuclear Medicine, 1990
A kinetic method is described for the estimation of neuroreceptor density as well as the rate constants for association and dissociation of rapidly equilibrating radioligands. The method is exemplified by positron emission tomographic measurements of the human brain using 11C-raclopride, a D2 dopamine receptor antagonist, and 1 a C-Ro 15-1788, a benzodiazepine receptor antagonist. Using a linear non iterative algorithm, regional binding characteristics were calculated and displayed pixel by pixel in brain maps. Data from repeated experiments on the same subject with different amounts of the unlabeled ligand were utilized. The binding characteristics were determined according to a two step procedure in which the time course of the free radioligand concentration was estimated from a reference region considered to be free of specific receptor binding sites. Alternative methods to determine the concentration of free radioligand are discussed.
A likelihood-based framework for quantification of brain receptor PET studies in the pixel domain
2004
Quantification of receptor binding studies obtained with PET is complicated by tissue heterogeneity in the sampling image elements pixels and voxels. This effect is caused by a limited spatial resolution of the PET scanner. On the other hand, spatial heterogeneity is often essential in understanding the underlying receptor binding process. In this paper, we propose a likelihood-based framework in the pixel domain for quantitative imaging with or without 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 performances of the proposed scheme is examined by simulations. Real brain PET data are also examined to show the performance in determining the time activity curves and the underlying factor images.
Quantitative Rodent Brain Receptor Imaging
Molecular Imaging and Biology, 2019
Positron emission tomography (PET) is a non-invasive imaging technology employed to describe metabolic, physiological, and biochemical processes in vivo. These include receptor availability, metabolic changes, neurotransmitter release, and alterations of gene expression in the brain. Since the introduction of dedicated small-animal PET systems along with the development of many novel PET imaging probes, the number of PET studies using rats and mice in basic biomedical research tremendously increased over the last decade. This article reviews challenges and advances of quantitative rodent brain imaging to make the readers aware of its physical limitations, as well as to inspire them for its potential applications in preclinical research. In the first section, we briefly discuss the limitations of small-animal PET systems in terms of spatial resolution and sensitivity and point to possible improvements in detector development. In addition, different acquisition and post-processing methods used in rodent PET studies are summarized. We further discuss factors influencing the test-retest variability in small-animal PET studies, e.g., different receptor quantification methodologies which have been mainly translated from human to rodent receptor studies to determine the binding potential and changes of receptor availability and radioligand affinity. We further review different kinetic modeling approaches to obtain quantitative binding data in rodents and PET studies focusing on the quantification of endogenous neurotransmitter release using pharmacological interventions. While several studies have focused on the dopamine system due to the availability of several PET tracers which are sensitive to dopamine release, other neurotransmitter systems have become more and more into focus and are described in this review, as well. We further provide an overview of latest genome engineering technologies, including the CRISPR/Cas9 and DREADD systems that may advance our understanding of brain disorders and function and how imaging has been successfully applied to animal models of human brain disorders. Finally, we review the strengths and opportunities of simultaneous PET/magnetic resonance imaging systems to study drug-receptor interactions and challenges for the translation of PET results from bench to bedside.
Model-Based receptor quantization analysis for PET parametric imaging
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2005
Dynamic PET (positron emission tomography) imaging technique allows image-wide quantification of physiologic and biochemical parameters. Compartment modeling is the most popular approach for receptor binding studies. However, current compartment-model based methods often either require the accurate arterial blood measurements as the input function or assume the existence of a reference region. To obviate the need for the input function or a reference region, in this paper, we propose to estimate the input function and the kinetic parameters simultaneously. The initial estimate of the input functions is obtained by the analysis of space intersections. Then both the input function and the receptor parameters, thus the underlying distribution volume (DV) parametric image, are estimated iteratively. The performance of the proposed scheme is examined by both simulations and real brain PET data in obtaining the underlying parametric images.
European Journal of Nuclear Medicine and Molecular Imaging, 2008
Purpose We evaluated the noise reduction capability of wavelet denoising for estimated binding potential (BP) images (k 3 /k 4 ) of the peripheral benzodiazepine receptor using 18 F-FEDAA1106 and nonlinear least-square fitting. Methods Wavelet denoising within a three-dimensional discrete dual-tree complex wavelet transform was applied to simulate data and clinical dynamic positron emission tomography images of 18 F-FEDAA1106. To eliminate noise components in wavelet coefficients, real and imaginary coefficients for each subband were thresholded individually using NormalShrink. A simulated dynamic brain image of 18 F-FEDAA1106 was generated and Gaussian noise was added to mimic PET dynamic scan. The derived BP images were compared with true images using 156 rectangular regions of interest. Wavelet denoising was also applied to data derived from seven young normal volunteers. Results In the simulations, estimated BP by denoised image showed better correlation with the true BP values (Y=0.83X+ 0.94, r=0.80), although no correlation was observed in the estimates between noise-added and true images (Y=1.2X+ 0.78, r=0.49). For clinical data, there were visual improvements in the signal-to-noise ratio for estimated BP images. Conclusions Wavelet denoising improved the bias and reduced the variation of pharmacokinetic parameters for BP. Keywords Positron emission tomography . Kinetic analysis . Wavelet denoising . Peripheral benzodiazepine receptor . Parametric imaging (SRTM) in simulation studies. In a series of papers [1-6], Turkheimer et al. have performed the most rigorous analysis of the wavelet transform for dynamic PET data. For linear regression analysis such as the Patlak plot [3, 5] and Eur
Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2006
Automated voxel-based or predefined volume-of-interest (VOI) analysis of rodent small-animal PET data is necessary for optimal use of information because the number of available resolution elements is limited. We have mapped metabolic ((18)F-FDG), dopamine transporter (DAT) (2'-(18)F-fluoroethyl(1R-2-exo-3-exe)-8-methyl-3-(4-chlorophenyl)-8-azabicyclo[3.2.1]-octane-2-carboxylate [(18)F-FECT]), and dopaminergic D(2) receptor ((11)C-raclopride) small-animal PET data onto a 3-dimensional T2-weighted MRI rat brain template oriented according to the rat brain Paxinos atlas. In this way, ligand-specific templates for sensitive analysis and accurate anatomic localization were created. Registration accuracy and test-retest and intersubject variability were investigated. Also, the feasibility of individual rat brain statistical parametric mapping (SPM) was explored for (18)F-FDG and DAT imaging of a 6-hydroxydopamine (6OHDA) model of Parkinson's disease. Ten adult Wistar rats were sc...
Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data
NeuroImage, 2014
Exploratory (i.e., voxelwise) spatial methods are commonly used in neuroimaging to identify areas that show an effect when a region-of-interest (ROI) analysis cannot be performed because no strong a priori anatomical hypothesis exists. However, noise at a single voxel is much higher than noise in a ROI making noise management critical to successful exploratory analysis. This work explores how preprocessing choices affect the bias and variability of voxelwise kinetic modeling analysis of brain positron emission tomography (PET) data. These choices include the use of volume- or cortical surface-based smoothing, level of smoothing, use of voxelwise partial volume correction (PVC), and PVC masking threshold. PVC was implemented using the Muller-Gartner method with the masking out of voxels with low gray matter (GM) partial volume fraction. Dynamic PET scans of an antagonist serotonin-4 receptor radioligand ([(11)C]SB207145) were collected on sixteen healthy subjects using a Siemens HRRT PET scanner. Kinetic modeling was used to compute maps of non-displaceable binding potential (BPND) after preprocessing. The results showed a complicated interaction between smoothing, PVC, and masking on BPND estimates. Volume-based smoothing resulted in large bias and intersubject variance because it smears signal across tissue types. In some cases, PVC with volume smoothing paradoxically caused the estimated BPND to be less than when no PVC was used at all. When applied in the absence of PVC, cortical surface-based smoothing resulted in dramatically less bias and the least variance of the methods tested for smoothing levels 5mm and higher. When used in combination with PVC, surface-based smoothing minimized the bias without significantly increasing the variance. Surface-based smoothing resulted in 2-4 times less intersubject variance than when volume smoothing was used. This translates into more than 4 times fewer subjects needed in a group analysis to achieve similarly powered statistical tests. Surface-based smoothing has less bias and variance because it respects cortical geometry by smoothing the PET data only along the cortical ribbon and so does not contaminate the GM signal with that of white matter and cerebrospinal fluid. The use of surface-based analysis in PET should result in substantial improvements in the reliability and detectability of effects in exploratory PET analysis, with or without PVC.
PLOS ONE, 2018
PET and SPECT voxel kinetics are highly noised. To our knowledge, no study has determined the effect of denoising on the ability to detect differences in binding at the voxel level using Statistical Parametric Mapping (SPM). Methods In the present study, groups of subject-images with a 10%-and 20%-difference in binding of [ 123 I]iomazenil (IMZ) were simulated. They were denoised with Factor Analysis (FA). Parametric images of binding potential (BP ND) were produced with the simplified reference tissue model (SRTM) and the Logan non-invasive graphical analysis (LNIGA) and analyzed using SPM to detect group differences. FA was also applied to [ 123 I]IMZ and [ 11 C]flumazenil (FMZ) clinical images (n = 4) and the variance of BP ND was evaluated. Results Estimations from FA-denoised simulated images provided a more favorable bias-precision profile in SRTM and LNIGA quantification. Simulated differences were detected in a higher number of voxels when denoised simulated images were used for voxel-wise estimations, compared to quantification on raw simulated images. Variability of voxel-wise binding estimations on denoised clinical SPECT and PET images was also significantly diminished. Conclusion In conclusion, noise removal from dynamic brain SPECT and PET images may optimize voxel-wise BP ND estimations and detection of biological differences using SPM.