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Purpose Alzheimer’s disease (AD) is the most common form of dementia. Clinically, it is characterized by progressive cognitive and functional impairment with structural hallmarks of cortical atrophy and ventricular expansion. Amyloid plaque aggregation is also known to occur in AD subjects. In-vivo imaging of amyloid plaques is now possible with positron emission tomography (PET) radioligands. PET imaging suffers from a degrading phenomenon known as the partial volume effect (PVE). The quantitative accuracy of PET images is reduced by PVEs primarily due to the limited spatial resolution of the scanner. The degree of PVE is influenced by structure size, with smaller structures tending to suffer from more severe PVEs such as atrophied grey matter regions. The aims of this paper were to investigate the effect of partial volume correction (PVC) on the quantification of amyloid PET and to highlight the importance of selecting an appropriate PVC technique. Methods An improved PVC technique, region-based voxel-wise (RBV) correction, was compared against existing Van-Cittert (VC) and Müller-Gärtner (MG) methods using amyloid PET imaging data. Digital phantom data were produced using segmented MRI scans from a control subject and an AD subject. Typical tracer distributions were generated for each of the phantom anatomies. Also examined were 70 clinical PET scans acquired using [ 18 F]flutemetamol. Volume of interest (VOI) analysis was performed for corrected and uncorrected images. Results PVC was shown to improve the quantitative accuracy of regional analysis performed on amyloid PET images. Of the corrections applied, VC deconvolution demonstrated the worst recovery of grey matter values. MG PVC was shown to induce biases in some grey matter regions due to grey matter variability. In addition, white matter variability was shown to influence the accuracy of MG PVC in cortical grey matter and also cerebellar grey matter, a typical reference region for amyloid PET normalization in sporadic AD. RBV was shown to be more accurate than MG in terms of grey matter and white matter uptake. An increase in within-group variability after PVC was observed and is believed to be a genuine, more accurate representation of the data rather than a correction-induced error. The standardized uptake value ratio (SUVR) threshold for classifying subjects as either amyloid-positive or amyloid-negative was found to be 1.64 in the uncorrected dataset, rising to 2.25 after PVC. Conclusion Care should be taken when applying PVC to amyloid PET images. Assumptions made in existing PVC strategies can induce biases that could lead to erroneous inferences about uptake in certain regions. The proposed RBV PVC technique accounts for within-compartment variability, with the potential to reduce errors of this kind.

Blood-based kinetic analysis of PET data relies on an accurate estimate of the arterial plasma input function (PIF). An alternative to invasive measurements from arterial sampling is an image-derived input function (IDIF). However, an IDIF provides the whole blood radioactivity concentration, rather than the required free tracer radioactivity concentration in plasma. To estimate the tracer PIF, we corrected an IDIF from the carotid artery with estimates of plasma parent fraction (PF) and plasma-to-whole blood (PWB) ratio obtained from five venous samples. We compared the combined IDIF+venous approach to gold standard data from arterial sampling in 10 healthy volunteers undergoing [18F]GE-179 brain PET imaging of the NMDA receptor. Arterial and venous PF and PWB ratio estimates determined from 7 patients with traumatic brain injury (TBI) were also compared to assess the potential effect of medication. There was high agreement between areas under the curves of the estimates of PF (r = 0.99, p<0.001), PWB ratio (r = 0.93, p<0.001), and the PIF (r = 0.92, p<0.001) as well as total distribution volume (VT) in 11 regions across the brain (r = 0.95, p<0.001). IDIF+venous VT had a mean bias of −1.7% and a comparable regional coefficient of variation (arterial: 21.3 ± 2.5%, IDIF+venous: 21.5 ± 2.0%). Simplification of the IDIF+venous method to use only one venous sample provided less accurate VT estimates (mean bias 9.9%; r = 0.71, p<0.001). A version of the method that avoids the need for blood sampling by combining the IDIF with population-based PF and PWB ratio estimates systematically underestimated VT (mean bias −20.9%), and produced VT estimates with a poor correlation to those obtained using arterial data (r = 0.45, p<0.001). Arterial and venous blood data from 7 TBI patients showed high correlations for PF (r = 0.92, p = 0.003) and PWB ratio (r = 0.93, p = 0.003). In conclusion, the IDIF+venous method with five venous samples provides a viable alternative to arterial sampling for quantification of [18F]GE-179 VT.

We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.

INTRODUCTION The Centiloid scale aims to harmonize amyloid beta (Aβ) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). METHODS We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian‐mixture‐modelling–derived cutpoints for Aβ PET positivity were converted. RESULTS The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM‐based Centiloids. Linear adjustment produced a WM‐based cutpoint of 18.1. DISCUSSION Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. HIGHLIGHTS Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results. Centiloid values can be influenced by differences in acquisition. We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort. Whole cerebellum referenced values could be reliably transformed to Centiloids. White matter referenced values may be less generalizable between datasets.

Purpose It has recently been recognized that PET/CT may play a role in diffuse parenchymal lung disease. However, interpretation can be confounded due to the variability in lung density both within and between individuals. To address this issue a novel correction method is proposed. Methods A CT scan acquired during shallow breathing is registered to a PET study and smoothed so as to match the PET resolution. This is used to derive voxel-based tissue fraction correction factors for the individual. The method was evaluated in a lung phantom study in which the lung was simulated by a Styrofoam/water mixture. The method was further evaluated using 18 F-FDG in 12 subjects free from pulmonary disease where ranges before and after correction were considered. Results Correction resulted in similar activity concentrations for the lung and background regions, consistent with the experimental phantom set-up. Correction resulted in reduced inter- and intrasubject variability in the estimated SUV. The possible application of the method was further demonstrated in five subjects with interstitial lung changes where increased SUV was demonstrated. Single study pre- and post-treatment studies were also analysed to further illustrate the utility of the method. Conclusion The proposed tissue fraction correction method is a promising technique to account for variability of density in interpreting lung PET studies.

BackgroundCurrently, there is no consensus on the optimal partial volume correction (PVC) algorithm for oncology imaging. Several existing PVC methods require knowledge of the reconstructed resolution, usually as the point spread function (PSF)—often assumed to be spatially invariant. However, this is not the case for SPECT imaging. This work aimed to assess the accuracy of SPECT quantification when PVC is applied using a case-specific PSF.MethodsSimulations of SPECT 99mTc imaging were performed for a range of activity distributions, including those replicating typical clinical oncology studies. Gaussian PSFs in reconstructed images were estimated using perturbation with a small point source. Estimates of the PSF were made in situations which could be encountered in a patient study, including; different positions in the field of view, different lesion shapes, sizes and contrasts, noise-free and noisy data. Ground truth images were convolved with the perturbation-estimated PSF, and with a PSF reflecting the resolution at the centre of the field of view. Both were compared with reconstructed images and the root-mean-square error calculated to assess the accuracy of the estimated PSF. PVC was applied using Single Target Correction, incorporating the perturbation-estimated PSF. Corrected regional mean values were assessed for quantitative accuracy.ResultsPerturbation-estimated PSF values demonstrated dependence on the position in the Field of View and the number of OSEM iterations. A lower root mean squared error was observed when convolution of the ground truth image was performed with the perturbation-estimated PSF, compared with convolution using a different PSF. Regional mean values following PVC using the perturbation-estimated PSF were more accurate than uncorrected data, or data corrected with PVC using an unsuitable PSF. This was the case for both simple and anthropomorphic phantoms. For the simple phantom, regional mean values were within 0.7% of the ground truth values. Accuracy improved after 5 or more OSEM iterations (10 subsets). For the anthropomorphic phantoms, post-correction regional mean values were within 1.6% of the ground truth values for noise-free uniform lesions.ConclusionPerturbation using a simulated point source could potentially improve quantitative SPECT accuracy via the application of PVC, provided that sufficient reconstruction iterations are used.

Purpose Reconstruction of DaTSCAN brain studies using OS-EM iterative reconstruction offers better image quality and more accurate quantification than filtered back-projection. However, reconstruction must proceed for a sufficient number of iterations to achieve stable and accurate data. This study assessed the impact of the number of iterations on the image quantification, comparing the results of the iterative reconstruction with filtered back-projection data. Methods A striatal phantom filled with 123 I using striatal to background ratios between 2:1 and 10:1 was imaged on five different gamma camera systems. Data from each system were reconstructed using OS-EM (which included depth-independent resolution recovery) with various combinations of iterations and subsets to achieve up to 200 EM-equivalent iterations and with filtered back-projection. Using volume of interest analysis, the relationships between image reconstruction strategy and quantification of striatal uptake were assessed. Results For phantom filling ratios of 5:1 or less, significant convergence of measured ratios occurred close to 100 EM-equivalent iterations, whereas for higher filling ratios, measured uptake ratios did not display a convergence pattern. Assessment of the count concentrations used to derive the measured uptake ratio showed that nonconvergence of low background count concentrations caused peaking in higher measured uptake ratios. Compared to filtered back-projection, OS-EM displayed larger uptake ratios because of the resolution recovery applied in the iterative algorithm. Conclusion The number of EM-equivalent iterations used in OS-EM reconstruction influences the quantification of DaTSCAN studies because of incomplete convergence and possible bias in areas of low activity due to the nonnegativity constraint in OS-EM reconstruction. Nevertheless, OS-EM using 100 EM-equivalent iterations provides the best linear discriminatory measure to quantify the uptake in DaTSCAN studies.

Rationale Ketamine induces effects resembling both positive and negative psychotic symptoms of schizophrenia. These are thought to arise through its action as an uncompetitive antagonist of the N -methyl- D -aspartate (NMDA) receptor. Objectives We used [ 123 I]CNS-1261 to study ketamine binding to NMDA receptors in healthy human controls in vivo and its relationship to positive and negative psychotic symptom induction. Materials and methods Ten healthy controls underwent two single-photon emission tomography scans with [ 123 I]CNS-1261. On each occasion, they received a bolus infusion of either ketamine or saline. The Brief Psychiatric Rating Scale (BPRS) was administered at the end of each scan. Predefined regions of interest were used to estimate change in volume of distribution of [ 123 I]CNS-1261 following ketamine administration. Two normalised-to-cortex binding indices were also used in order to study effects of ketamine on NMDA receptor availability by region, after correction for global and nonspecific effects. Results Ketamine-induced reduction in [ 123 I]CNS-1261 volume of distribution in all regions showed the strongest correlation with BPRS negative subscale ( p  < 0.01). With the normalised-to-cortex measures, NMDA receptor binding in middle inferior frontal cortex showed a significant correlation with BPRS negative subscale (BI1 r  = 0.88, BI2 r  = 95.9, p  < 0.001). Conclusions [ 123 I]CNS-1261 binding was modulated by ketamine, a drug known to compete for the same site on the NMDA receptor in vitro. Ketamine may induce negative symptoms through direct inhibition of the NMDA receptor, and positive symptoms may arise through a different neurochemical pathway.

BackgroundKinetic analysis of dynamic PET data requires an accurate knowledge of available PET tracer concentration within blood plasma over time, known as the arterial input function (AIF). The gold standard method used to measure the AIF requires serial arterial blood sampling over the course of the PET scan, which is an invasive procedure and makes this method less practical in clinical settings. Traditional image-derived methods are limited to specific tracers and are not accurate if metabolites are present in the plasma.ResultsIn this work, we utilise an image-derived whole blood curve measurement to reduce the computational complexity of the simultaneous estimation method (SIME), which is capable of estimating the AIF directly from tissue time activity curves (TACs). This method was applied to data obtained from a serotonin receptor study (11C-SB207145) and estimated parameter results are compared to results obtained using the original SIME and gold standard AIFs derived from arterial samples. Reproducibility of the method was assessed using test-retest data. It was shown that the incorporation of image-derived information increased the accuracy of total volume of distribution (V T) estimates, averaged across all regions, by 40% and non-displaceable binding potential (BP ND) estimates by 16% compared to the original SIME. Particular improvements were observed in K1 parameter estimates. BP ND estimates, based on the proposed method and the gold standard arterial sample-derived AIF, were not significantly different (P=0.7).ConclusionsThe results of this work indicate that the proposed method with prior AIF information obtained from a partial volume corrected image-derived whole blood curve, and modelled parent fraction, has the potential to be used as an alternative non-invasive method to perform kinetic analysis of tracers with metabolite products.

Serotonergic brain regions play a crucial role in the modulation of emotion, and serotonergic dysfunction may contribute to several neurological disorders. [123I]ADAM is a novel SPECT tracer which binds with high affinity to serotonin transporters (SERT). The objective of this study was to compare different methods for the quantification of tracer binding and to develop a simplified single-scan protocol for this tracer, as well as to investigate its potential for characterisation of the transporter occupancy versus plasma concentration curve of a selective serotonin re-uptake inhibitor (SSRI). Dynamic SPECT scans were performed on 16 healthy volunteers after administration of approximately 150 MBq [123I]ADAM. Data were acquired from the time of injection until approximately 5.5 h after injection in 30- or 45-min sessions. Each subject was scanned twice: with and without pre-treatment with the SSRI citalopram in various dosage regimens. The plasma concentration of citalopram (C(p)) was determined from venous samples. Images were reconstructed by filtered back-projection with scatter and attenuation correction. Tracer binding was quantified for midbrain, striatum and thalamus using cerebellum as a reference region. Quantification was done by kinetic modelling, graphical analysis and multi-linear regression, as well as by the ratio method, with binding potential (BP2) as the outcome measure. The SERT occupancy by citalopram was determined relative to the baseline scan for each subject, and the occupancy versus C(p) curve was fitted with the E(max) model. The highest binding of [123I]ADAM was in midbrain (mean baseline BP2+/-SD=1.31+/-0.29), with lower binding in thalamus (0.79+/-0.16) and striatum (0.66+/-0.13). There was good agreement between BP2 values obtained by different quantification methods. Using the ratio method, the best agreement with kinetic modelling was obtained with data from the time interval [200,260] min after injection. The fitting of the midbrain occupancy curve yielded a maximum occupancy of 84% and a plasma concentration required to reach 50% of the maximum of 2.5 ng/ml, with a goodness-of-fit variability of 13% (SD). Binding of [123I]ADAM to SERT in midbrain can be quantified with a single scan starting 200 min after injection. However, the variability of estimated occupancy values may be too high for critical assessment of occupancy of SERT by SSRI.