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

Ribeiro, D; Hallett, W; Howes, O; McCutcheon, R; Nour, MM; Tavares, AAS

Neuroscience & Neuroimaging Research Collection

Journal article

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 (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. 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: 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

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Ribeiro, D., Hallett, W., Howes, O., McCutcheon, R., Nour, M. M., & Tavares, A. A. S. (2022). 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. EJNMMI Research, 12(1), 11–11.

MLA Style

Ribeiro, D, et al. “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.” EJNMMI Research, vol. 12, no. 1, 2022, pp. 11–11.

Chicago Style

Ribeiro, D, W Hallett, O Howes, R McCutcheon, MM Nour, and AAS Tavares. 2022. “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.” EJNMMI Research 12 (1): 11–11.
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