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

Adriana A. S. Tavares¹  Oliver Howes²  Robert McCutcheon²  Matthew M. Nour³  William Hallett⁴  Daniela Ribeiro⁵ 

[1]Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK

[2]Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

[3]Institute of Medical Sciences, Medical Research Council London, London, UK

[4]Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK

[5]Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

[6]Max Planck Centre for Computational Psychiatry and Ageing Research, Institute of Neurology, University College London, London, UK

[7]Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK

[8]Invicro, Centre for Imaging Sciences, Hammersmith Hospital, Invicro, Imperial College London, Burlington Danes Building, Du Cane Road, W12 0NN, London, UK

[9]Invicro, Centre for Imaging Sciences, Hammersmith Hospital, Invicro, Imperial College London, Burlington Danes Building, Du Cane Road, W12 0NN, London, UK

[10]Edinburgh Imaging, The University of Edinburgh, Edinburgh, UK

关键词: PET-MR;    [C]PHNO;    Reconstruction;    Bayesian;    Neuroimaging;   

DOI  :  10.1186/s13550-022-00883-1

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY