Dataset Information

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [¹¹C]PHNO brain PET-MR studies.

ABSTRACT:

Introduction

Q.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different β value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR.

Methods

Six [¹¹C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with β100-1000 (in increments of 100) and OSEM. The binding potential relative to non-displaceable volume (BP_ND) were obtained for the Substantia Nigra (SN), Striatum (St), Globus Pallidus (GP), Thalamus (Th), Caudate (Cd) and Putamen (Pt), using the MIAKAT™ software. Intraclass correlation coefficients (ICC), repeatability coefficients (RC), coefficients of variation (CV) and bias from Bland-Altman plots were reported. Statistical analysis was conducted using a 2-way ANOVA model with correction for multiple comparisons.

Results

When comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and RC were lower for Q.Clear with β100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43).

Conclusion

BP_ND results obtained from quantitative low count brain PET studies using [¹¹C]PHNO and reconstructed with Q.Clear with β < 400, which is the value used for clinical [¹⁸F]FDG whole-body studies, demonstrate the lowest bias versus the typical iterative reconstruction method OSEM.

SUBMITTER: Ribeiro D

PROVIDER: S-EPMC8859021 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [<sup>11</sup>C]PHNO brain PET-MR studies.

Ribeiro Daniela D Hallett William W Howes Oliver O McCutcheon Robert R Nour Matthew M MM Tavares Adriana A S AAS

EJNMMI research 20220220 1

<h4>Introduction</h4>Q.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain ima ...[more]

PMID: 35184229

Similar Datasets

Project description:UnlabelledQ.Clear, a Bayesian penalized-likelihood reconstruction algorithm for PET, was recently introduced by GE Healthcare on their PET scanners to improve clinical image quality and quantification. In this work, we determined the optimum penalization factor (beta) for clinical use of Q.Clear and compared Q.Clear with standard PET reconstructions.MethodsA National Electrical Manufacturers Association image-quality phantom was scanned on a time-of-flight PET/CT scanner and reconstructed using ordered-subset expectation maximization (OSEM), OSEM with point-spread function (PSF) modeling, and the Q.Clear algorithm (which also includes PSF modeling). Q.Clear was investigated for β (B) values of 100-1,000. Contrast recovery (CR) and background variability (BV) were measured from 3 repeated scans, reconstructed with the different algorithms. Fifteen oncology body (18)F-FDG PET/CT scans were reconstructed using OSEM, OSEM PSF, and Q.Clear using B values of 200, 300, 400, and 500. These were visually analyzed by 2 scorers and scored by rank against a panel of parameters (overall image quality; background liver, mediastinum, and marrow image quality; noise level; and lesion detectability).ResultsAs β is increased, the CR and BV decreases; Q.Clear generally gives a higher CR and lower BV than OSEM. For the smallest sphere reconstructed with Q.Clear B400, CR is 28.4% and BV 4.2%, with corresponding values for OSEM of 24.7% and 5.0%. For the largest hot sphere, Q.Clear B400 yields a CR of 75.2% and a BV of 3.8%, with corresponding values for OSEM of 64.4% and 4.0%. Scorer 1 and 2 ranked B400 as the preferred reconstruction in 13 of 15 (87%) and 10 of 15 (73%) cases. The least preferred reconstruction was OSEM PSF in all cases. In most cases, lesion detectability was highest ranked for B200, in 9 of 15 (67%) and 10 of 15 (73%), with OSEM PSF ranked lowest. Poor lesion detectability on OSEM PSF was seen in cases of mildly (18)F-FDG-avid mediastinal nodes in lung cancer and small liver metastases due to background noise. Conversely, OSEM PSF was ranked second highest for lesion detectability in most pulmonary nodule evaluation cases. The combined scores confirmed B400 to be the preferred reconstruction.ConclusionOur phantom measurement results demonstrate improved CR and reduced BV when using Q.Clear instead of OSEM. A β value of 400 is recommended for oncology body PET/CT using Q.Clear.

Project description:Preclinical studies support an important role of dopamine D3 receptors (DRD3s) in alcohol use disorder (AUD). In animals, voluntary alcohol consumption increases DRD3 expression, and pharmacological blockade of DRD3s attenuates alcohol self-administration and reinstatement of alcohol seeking. However, these findings have yet to be translated in humans. This study used positron emission tomography (PET) and [11C]-(+)-PHNO to compare receptor levels in several dopamine D2 receptor (DRD2) and DRD3 regions of interest between AUD subjects in early abstinence (n = 17; 6.59 ± 4.14 days of abstinence) and healthy controls (n = 18). We recruited non-treatment seeking subjects meeting DSM-5 criteria for AUD. We examined the relationship between DRD2/3 levels and both alcohol craving and alcohol motivation/wanting, using a cue reactivity procedure and an intravenous alcohol self-administration (IVASA) paradigm, respectively. [11C]-(+)-PHNO binding levels in AUD subjects were significantly lower than binding in HCs when looking at all DRD2/3 ROIs jointly (Wilk's Λ = .58, F(6,28) =3.33, p = 0.013, η2p = 0.42), however there were no region-specific differences. Binding values demonstrate -12.3% and -16.1% lower [11C]-(+)-PHNO binding in the SMST and SN respectively, though these differences did not withstand Bonferroni corrections. There was a positive association between [11C]-(+)-PHNO binding in the SN (almost exclusively reflective of DRD3) and alpha (lower values reflect higher alcohol demand) in the APT after Bonferroni corrections (r = 0.66, p = 0.0080). This demonstrates that AUD subjects with lower DRD3 levels in the SN exhibit increased demand for alcohol. These results replicate previous findings demonstrating reduced DRD2/3 levels while also supporting a lack of DRD3 upregulation and potential downregulation in early abstinent AUD. Furthermore, the finding that binding in the SN is associated with alcohol demand warrants further examination.

Project description:Childhood trauma is a risk factor for psychosis. Amphetamine increases synaptic striatal dopamine levels and can induce positive psychotic symptoms in healthy individuals and patients with schizophrenia. Socio-developmental hypotheses of psychosis propose that childhood trauma and other environmental risk factors sensitize the dopamine system to increase the risk of psychotic symptoms, but this remains to be tested in humans. We used [11C]-(+)-PHNO positron emission tomography to measure striatal dopamine-2/3 receptor (D2/3R) availability and ventral striatal dexamphetamine-induced dopamine release in healthy participants (n = 24). The relationships between dexamphetamine-induced dopamine release, dexamphetamine-induced positive psychotic symptoms using the Positive and Negative Syndrome Scale (PANSS), and childhood trauma using the Childhood Trauma Questionnaire (CTQ) were assessed using linear regression and mediation analyses, with childhood trauma as the independent variable, dexamphetamine-induced dopamine release as the mediator variable, and dexamphetamine-induced symptoms as the dependent variable. There was a significant interaction between childhood trauma and ventral striatal dopamine release in predicting dexamphetamine-induced positive psychotic symptoms (standardized β = 1.83, p = 0.003), but a mediation analysis was not significant (standardized β = -0.18, p = 0.158). There were no significant effects of dopamine release and childhood trauma on change in negative (p = 0.280) or general PANSS symptoms (p = 0.061), and there was no relationship between ventral striatal baseline D2/3R availability and positive symptoms (p = 0.368). This indicates childhood trauma and dopamine release interact to influence the induction of positive psychotic symptoms. This is not consistent with a simple sensitization hypothesis, but suggests that childhood trauma moderates the cognitive response to dopamine release to make psychotic experiences more likely.

Project description:BackgroundMesolimbic dopamine system dysfunction is believed to contribute to major depressive disorder (MDD), but molecular neuroimaging of striatal dopamine neurotransmission has yielded mixed results, possibly owing to limited sensitivity of antagonist radioligands used with positron emission tomography to assess dopamine release capacity. This study used an agonist radioligand with agonist challenge to assess dopamine release capacity and D2/D3 receptor availability in MDD.MethodsTwenty-six treatment-naive adults with MDD and 26 healthy comparison participants underwent functional magnetic resonance imaging during a probabilistic reinforcement task, and positron emission tomography with the D3-preferring ligand [11C]-(+)-PHNO, before and after oral dextroamphetamine. MDD participants then received pramipexole treatment for 6 weeks.ResultsMDD participants had trend-level greater dopamine release capacity in the ventral striatum, as measured by percent change in baseline binding potential relative to nondisplaceable compartment (ΔBPND) (-34% vs. -30%; p = .072, d = 0.58) but no difference in D2/D3 receptor availability (BPND). Striatal and extrastriatal BPND and percent change in baseline BPND were not significantly associated with blood oxygen level-dependent response to reward prediction error in the ventral striatum, severity of depression and anhedonia, or antidepressant response to pramipexole (response rate = 72.7%).Conclusions[11C]-(+)-PHNO demonstrated high sensitivity to displacement by amphetamine-induced dopamine release, but dopamine release capacity and D2/D3 availability were not associated with ventral striatal activation to reward prediction error or clinical features, in this study powered to detect large effects. While a preponderance of indirect evidence implicates dopaminergic dysfunction in MDD, these findings suggest that presynaptic dopamine dysregulation may not be a feature of MDD or a prerequisite for treatment response to dopamine agonists.

Dataset Information

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [¹¹C]PHNO brain PET-MR studies.

Introduction

Methods

Results

Conclusion

Publications

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [<sup>11</sup>C]PHNO brain PET-MR studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [11C]PHNO brain PET-MR studies.

Introduction

Methods

Results

Conclusion

Publications

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [&lt;sup&gt;11&lt;/sup&gt;C]PHNO brain PET-MR studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [¹¹C]PHNO brain PET-MR studies.

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [<sup>11</sup>C]PHNO brain PET-MR studies.