Quantification of ligand PET studies using a reference region with a displaceable fraction: application to occupancy studies with [(11)C]-DASB as an example.
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ABSTRACT:
SUBMITTER: Turkheimer FE
PROVIDER: S-EPMC3323353 | biostudies-other | 2012 Jan
REPOSITORIES: biostudies-other
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Project description:Performance of two supervised cluster analysis (SVCA) algorithms for extracting reference tissue curves was evaluated to improve quantification of dynamic (R)-[(11)C]PK11195 brain positron emission tomography (PET) studies. Reference tissues were extracted from images using both a manually defined cerebellum and SVCA algorithms based on either four (SVCA4) or six (SVCA6) kinetic classes. Data from controls, mild cognitive impairment patients, and patients with Alzheimer's disease were analyzed using various kinetic models including plasma input, the simplified reference tissue model (RPM) and RPM with vascular correction (RPMV(b)). In all subject groups, SVCA-based reference tissue curves showed lower blood volume fractions (V(b)) and volume of distributions than those based on cerebellum time-activity curve. Probably resulting from the presence of specific signal from the vessel walls that contains in normal condition a significant concentration of the 18 kDa translocation protein. Best contrast between subject groups was seen using SVCA4-based reference tissues as the result of a lower number of kinetic classes and the prior removal of extracerebral tissues. In addition, incorporation of V(b) in RPM improved both parametric images and binding potential contrast between groups. Incorporation of V(b) within RPM, together with SVCA4, appears to be the method of choice for analyzing cerebral (R)-[(11)C]PK11195 neurodegeneration studies.
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Project description:There is a great need for a non-invasive methodology enabling the quantification of translocator protein overexpression in PET clinical imaging. [18F]DPA-714 has emerged as a promising translocator protein radiotracer as it is fluorinated, highly specific and returned reliable quantification using arterial input function. Cerebellum gray matter was proposed as reference region for simplified quantification; however, this method cannot be used when inflammation involves cerebellum. Here we adapted and validated a supervised clustering (supervised clustering algorithm (SCA)) for [18F]DPA-714 analysis. Fourteen healthy subjects genotyped for translocator protein underwent an [18F]DPA-714 PET, including 10 with metabolite-corrected arterial input function and three for a test-retest assessment. Two-tissue compartmental modelling provided [Formula: see text] estimates that were compared to either [Formula: see text] or [Formula: see text] generated by Logan analysis (using supervised clustering algorithm extracted reference region or cerebellum gray matter). The supervised clustering algorithm successfully extracted a pseudo-reference region with similar reliability using classes that were defined using either all subjects, or separated into HAB and MAB subjects. [Formula: see text], [Formula: see text] and [Formula: see text] were highly correlated (ICC of 0.91?±?0.05) but [Formula: see text] were ?26% higher and less variable than [Formula: see text]. Reproducibility was good with 5% variability in the test-retest study. The clustering technique for [18F]DPA-714 provides a simple, robust and reproducible technique that can be used for all neurological diseases.
| S-EPMC5951011 | biostudies-literature
Project description:BackgroundThe standard reference region (RR) for amyloid-beta (Aβ) PET studies is the cerebellar grey matter (GMCB), while alternative RRs have mostly been utilized without prior validation against the gold standard. This study compared five commonly used RRs to gold standard plasma input-based quantification using the GMCB.MethodsThirteen subjects from a test-retest (TRT) study and 30 from a longitudinal study were retrospectively included (total: 17 Alzheimer's disease, 13 mild cognitive impairment, 13 controls). Dynamic [11C]PiB PET (90 min) and T1-weighted MR scans were co-registered and time-activity curves were extracted for cortical target regions and the following RRs: GMCB, whole cerebellum (WCB), white matter brainstem/pons (WMBS), whole brainstem (WBS) and eroded subcortical white matter (WMES). A two-tissue reversible plasma input model (2T4k_Vb) with GMCB as RR, reference Logan and the simplified reference tissue model were used to derive distribution volume ratios (DVRs), and standardized uptake value (SUV) ratios were calculated for 40-60 min and 60-90 min intervals. Parameter variability was evaluated using TRT scans, and correlations and agreements with the gold standard (DVR from 2T4k_Vb with GMCB RR) were also assessed. Next, longitudinal changes in SUVs (both intervals) were assessed for each RR. Finally, the ability to discriminate between visually Aβ positive and Aβ negative scans was assessed.ResultsAll RRs yielded stable TRT performance (max 5.1% variability), with WCB consistently showing lower variability. All approaches were able to discriminate between Aβ positive and Aβ negative scans, with highest effect sizes obtained for GMCB (range - 0.9 to - 0.7), followed by WCB (range - 0.8 to - 0.6). Furthermore, all approaches provided good correlations with the gold standard (r ≥ 0.78), while the highest bias (as assessed by the regression slope) was observed using WMES (range slope 0.52-0.67), followed by WBS (range slope 0.58-0.92) and WMBS (range slope 0.62-0.91). Finally, RR SUVs were stable across a period of 2.6 years for all except WBS and WMBS RRs (60-90 min interval).ConclusionsGMCB and WCB are considered the best RRs for quantifying amyloid burden using [11C]PiB PET.
| S-EPMC7572969 | biostudies-literature
Project description:BackgroundAvailability of the α2C-adrenoceptor (α2C-AR) positron emission tomography (PET) tracer, [11C]ORM-13070, and the α2C-AR antagonist ORM-12741 allows probing of the roles of this G-protein coupled receptor subtype in brain function, both in healthy humans and in patients with various brain disorders. This translational study employed [11C]ORM-13070 autoradiography and PET to determine α2C-AR occupancy by ORM-12741 in rat and human brain, respectively.ResultsORM-12741 has high affinity (Ki: 0.08 nM) and potent antagonist activity (Kb: 0.04 nM) as well as selectivity (Ki estimates for the human α2A-AR and α2B-AR were 8.3 nM and 0.8 nM, respectively) for the human α2C-AR subtype. [11C]ORM-13070 had highest uptake in the basal ganglia of rat and human brain. Pretreatment with ORM-12741 inhibited [11C]ORM-13070 binding in rat striatum in a time- and dose-dependent manner at 10 and 50 µg/kg (s.c.) with an EC50 estimate of 1.42 ng/mL in rat plasma, corresponding to protein-free drug concentration of 0.23 nM. In the living human brain, time- and dose-related α2C-AR occupancy was detected with EC50 estimates of 24 ng/mL and 31 ng/mL for the caudate nucleus and putamen, respectively, corresponding to protein-free concentrations in plasma of 0.07 nM and 0.1 nM. Modelling-based maximum α2C-AR occupancy estimates were 63% and 52% in the caudate nucleus and the putamen, respectively.ConclusionsORM-12741 is a selective α2C-AR antagonist which penetrates the rat and human brain to occupy α2C-ARs in a manner consistent with its receptor pharmacology. Trial registration number and date of registration: ClinicalTrial.cov NCT00829907. Registered 11 December 2008. https://clinicaltrials.gov/ .
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Project description:RATIONALE:Sedation, dependence, and abuse liability limit the use of non-selective ?-aminobutyric acid (GABAA) receptor positive modulators for the treatment of anxiety. AZD7325 and AZD6280 are novel, subtype-selective GABAA?2,3 receptor positive modulators with limited sedative effects. OBJECTIVES:The current study aimed to confirm target engagement at GABAA receptors by AZD7325 and AZD6280 in humans and to determine the relationship between exposure, GABAA receptor occupancy, and tolerability. METHOD:Two PET studies, using high-resolution research tomography (HRRT) and the radioligand [11C]flumazenil, were performed in 12 subjects at baseline and after administration of single oral doses of AZD7325 (0.2 to 30 mg) and AZD6280 (5 to 40 mg). PET images were analyzed using a simplified reference tissue model, and regional binding potentials (BPND) were obtained. The relationship between plasma concentration of AZD7325 or AZD6280 and GABAA receptor occupancy was described by hyperbolic function, and K i,plasma (plasma concentration required for 50% receptor occupancy) was estimated. Assessments of safety and tolerability included recording of adverse events, vital signs, electrocardiogram, and laboratory tests. RESULTS:The [11C]flumazenil binding was reduced in a dose-dependent, saturable manner by both agents. Maximum receptor occupancy could be reached for both compounds without causing sedation or cognitive impairment. The K i,plasma estimates for AZD7325 and AZD6280 were 15 and 440 nmol/l, respectively. CONCLUSION:High GABAA receptor occupancy by AZD7325 and AZD6280 could be reached without clear sedative effects.
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Project description:BackgroundRegional cardiac sympathetic denervation is predictive of sudden cardiac arrest in patients with ischemic cardiomyopathy. The reproducibility of denervation scores between automated software programs has not been evaluated. This study seeks to (1) compare the inter-rater reliability of regional denervation measurements using two analysis programs: FlowQuant® and Corridor4DM®; (2) evaluate test-retest repeatability of regional denervation scores.MethodsN = 190 dynamic [11C]meta-hydroxyephedrine (HED) PET scans were reviewed from the PAREPET trial in ischemic cardiomyopathy patients with reduced left ventricular ejection fraction(LVEF ≤ 35%). N = 12 scans were excluded due to non-diagnostic quality. N = 178 scans were analyzed using FlowQuant and Corridor4DM software, each by two observers. Test-retest scans from N = 20 patients with stable heart failure were utilized for test-retest analysis. Denervation scores were defined as extent × severity of relative uptake defects in LV regions with < 75% of maximal uptake. Results were evaluated using intraclass correlation coefficient (ICC) and Bland-Altman coefficient of repeatability (RPC).ResultsInter-observer, inter-software, and test-retest ICC values were excellent (ICC = 94% to 99%) and measurement variability was small (RPC < 11%). Mean differences between observers ranged .2% to 1.1% for Corridor4DM (P = .28), FlowQuant (P < .001), and between software programs (P < .001). Kaplan-Meier analysis demonstrated HED scores from both programs were predictive of SCA.ConclusionInter-rater reliability for both analysis programs was excellent and test-retest repeatability was consistent. The minimal difference in scores between FlowQuant and Corridor4DM supports their use in future trials.
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Project description:Measurements of drug occupancies using positron emission tomography (PET) can be biased if the radioligand concentration exceeds "tracer" levels. Negative bias would also arise in successive PET scans if clearance of the radioligand is slow, resulting in a carryover effect. We developed a method to (1) estimate the in vivo dissociation constant Kd of a radioligand from PET studies displaying a non-tracer carryover (NTCO) effect and (2) correct the NTCO bias in occupancy studies taking into account the plasma concentration of the radioligand and its in vivo Kd. This method was applied in a study of healthy human subjects with the histamine H3 receptor radioligand [11C]GSK189254 to measure the PK-occupancy relationship of the H3 antagonist PF-03654746. From three test/retest studies, [11C]GSK189254 Kd was estimated to be 9.5 ± 5.9 pM. Oral administration of 0.1 to 4 mg of PF-03654746 resulted in occupancy estimates of 71%-97% and 30%-93% at 3 and 24 h post-drug, respectively. NTCO correction adjusted the occupancy estimates by 0%-15%. Analysis of the relationship between corrected occupancies and PF-03654746 plasma levels indicated that PF-03654746 can fully occupy H3 binding sites ( ROmax = 100%), and its IC50 was estimated to be 0.144 ± 0.010 ng/mL. The uncorrected IC50 was 26% higher.
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Project description:11C-UCB-J ((R)-1-((3-(11C-methyl-11C)pyridin-4-yl)methyl)-4-(3,4,5-trifluorophenyl)pyrrolidin-2-one) is a PET tracer for synaptic vesicle glycoprotein 2A, which may be a marker of synaptic density. To simplify the scan protocol, SUV ratios (SUVRs) were compared with model-based nondisplaceable binding potential (BP ND) to select the optimal time window in healthy and neuropsychiatric subjects. Methods: In total, 141 scans were acquired for 90 min. Arterial blood sampling and metabolite analysis were conducted. SUVR-1 (centrum semiovale reference region) was computed for six 30-min windows and compared with 1-tissue-compartment model BP ND Simulations were performed to assess the time dependency of SUVR-1. Results: Greater correlation and less bias were observed for SUVR-1 at later time windows for all subjects. Simulations showed that the agreement between SUVR-1 and BP ND is time-dependent. Conclusion: The 60- to 90-min period provided the best match between SUVR-1 and BP ND (-1% ± 7%); thus, a short scan is sufficient for accurate quantification of 11C-UCB-J-specific binding.
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Project description:In some positron emission tomography (PET) studies, a reversibly binding radioligand is administered as a constant infusion to establish true equilibrium for quantification. This approach reduces scanning time and simplifies data analysis, but assumes similar behavior of the radioligand in plasma across the study population to establish true equilibrium in all subjects. Bias in outcome measurements can result if this assumption is not met. This work developed and validated a correction that reduces bias in total distribution volume (VT) estimates when true equilibrium is not present. This correction, termed tissue clearance correction (TCC), took the form VT=VT(A)/(1+βγVT(A)), where β is the radioligand clearance rate in tissue, γ is a radiotracer-specific constant, and VT(A) is the apparent VT. Simulations characterized the robustness of TCC across imperfect values of γ and β and demonstrated reduction to false positive rates. This approach was validated with human infusion data for three radiotracers: [18F]FPEB, (-)-[18F]flubatine, and [11C]UCB-J. TCC reduced bias in VT estimates for all radiotracers and significantly reduced intersubject variance in VT for [18F]FPEB data in some brain regions. Thus, TCC improves quantification of data acquired from PET infusion studies.
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Project description:Structural MR images concomitantly acquired with PET images can provide crucial anatomic information for precise quantitative analysis. However, in the clinical setting, not all the subjects have corresponding MR images. Here, we developed a model to generate structural MR images from amyloid PET using deep generative networks. We applied our model to quantification of cortical amyloid load without structural MR. Methods: We used florbetapir PET and structural MR data from the Alzheimer Disease Neuroimaging Initiative database. The generative network was trained to generate realistic structural MR images from florbetapir PET images. After the training, the model was applied to the quantification of cortical amyloid load. PET images were spatially normalized to the template space using the generated MR, and then SUV ratio (SUVR) of the target regions was measured by predefined regions of interest. A real MR-based quantification was used as the gold standard to measure the accuracy of our approach. Other MR-less methods-a normal PET template-based, a multiatlas PET template-based, and a PET segmentation-based normalization/quantification-were also tested. We compared the performance of quantification methods using generated MR with that of MR-based and MR-less quantification methods. Results: Generated MR images from florbetapir PET showed signal patterns that were visually similar to the real MR. The structural similarity index between real and generated MR was 0.91 ± 0.04. The mean absolute error of SUVR of cortical composite regions estimated by the generated MR-based method was 0.04 ± 0.03, which was significantly smaller than other MR-less methods (0.29 ± 0.12 for the normal PET template, 0.12 ± 0.07 for the multiatlas PET template, and 0.08 ± 0.06 for the PET segmentation-based methods). Bland-Altman plots revealed that the generated MR-based SUVR quantification was the closest to the SUVRs estimated by the real MR-based method. Conclusion: Structural MR images were successfully generated from amyloid PET images using deep generative networks. Generated MR images could be used as templates for accurate and precise amyloid quantification. This generative method might be used to generate multimodal images of various organs for further quantitative analyses.
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