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.

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

“…This results in a variable set of images with both low-and high-count statistics, which again influences the quantitative accuracy of the analysis. To our knowledge, only one previous study has investigated how the β-factor affects the pharmacokinetic modelling analysis in dynamic PET brain imaging (13). In this study, the authors compared the use of Q.Clear with variable β-factors (100-1000 in increments of 100) to OSEM-reconstructed data as the reference standard and recommended a β-factor between 100 and 200.…”

“…Furthermore, studies have until now mainly focused on whole-body static PET scans. To the best of our knowledge, only one published study has investigated the effect of the choice of β-factor on the analysis of dynamic clinical PET data (13). Ribeiro et al…”

Recent advances in combined Positron Emission Tomography and Magnetic Resonance Imaging

“…In general, these methods are susceptible to proper hyperparameter selection, and their use in the clinic is still limited. Q.clear [241][242][243] is one of the currently available commercial software, based on BSREM and the RDP prior. In the Kernelized Expectation Maximization (KEM) method [244], the image is modeled with a linear combination of specific kernels based on features that can be obtained from statical images in dynamic frames [244][245][246] or anatomical images [247,248].…”

Q.Clear Reconstruction for Reducing the Scanning Time for 68Gallium-DOTA-FAPI-04 PET/MR Imaging

“…The Q.Clear algorithm uses a customizable penalization factor (β) for noise suppression, and can achieve multiple iterations while suppressing background noise [7][8]. Recently, several studies have reported that, compared with traditional OSEM reconstruction, Q.Clear improved the quality of PET images by increasing SUVs within lesions, signal-to-noise ratio (SNR), and spatial resolution [9][10][11]. However, large SUVs from Q.Clear PET reconstruction appear problematic when interpreted according to current criteria for quantitative evaluation, because they may lead to overestimation of the disease burden and thus limit mainstream application in the clinic [12].…”