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

• Published in: EJNMMI research 2022-02, Vol.12 (1), p.11-13, Article 11
• Main Authors: Ribeiro, Daniela, Hallett, William, Howes, Oliver, McCutcheon, Robert, Nour, Matthew M., Tavares, Adriana A. S.
• Format: Article
• Language: English
• Subjects: [11C]PHNO; Algorithms; Bayesian; Bayesian analysis; Bias; Brain; Cardiac Imaging; Coefficient of variation; Computed tomography; Correlation coefficients; Human performance; Image acquisition; Image reconstruction; Imaging; In vivo methods and tests; Iterative methods; Magnetic resonance; Mathematical models; Medicine; Medicine & Public Health; Neuroimaging; Nuclear Medicine; Oncology; Original Research; Orthopedics; PET-MR; Positron emission; Radiology; Reconstruction; Reliability; Signal to noise ratio; Spatial resolution; Statistical analysis; Thalamus; Tomography; Variance analysis
• ISSN: 2191-219X; 2191-219X
• DOI: 10.1186/s13550-022-00883-1

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 [ 11 C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with β 100–1000 (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 β 100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β 200 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 [ 11 C]PHNO and reconstructed with Q.Clear with β