Abstract

IntroductionQ.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.MethodsSix [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 (BPND) 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.ResultsWhen 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).ConclusionBPND 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.

Highlights

  • 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

  • When comparing a standard ordered subset expectation maximization (OSEM) reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and repeatability coefficients (RC) were lower for Q.Clear with β100 for the Substantia Nigra (SN) (RC = 2.17), Th (RC = 0.08) and Globus Pallidus (GP) (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10)

  • binding potential relative to non-displaceable volume (BPND) 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

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Summary

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. FBP is not available in recently developed scanners, including the General Electric (GE) Signa PET- Magnetic Resonance (MR) scanners and other alternatives have been devised Current reconstruction algorithms such as OSEM and Block Sequential Regularised Expectation Maximisation (BSREM) are considered iterative reconstruction algorithms and can be used in images acquired in PET-Computed Tomography (CT) and in PET-MR scanners [2]. It has been shown that results obtained from OSEM reconstructions are biased in low statistics and it is unclear if BSREM algorithms perform in the same way [1]

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