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

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

Standardization of Preclinical PET/CT Imaging to Improve Quantitative Accuracy, Precision, and Reproducibility: A Multicenter Study

Preclinical PET/CT is a well-established noninvasive imaging tool for studying disease development/progression and the development of novel radiotracers and pharmaceuticals for clinical applications. Despite this pivotal role, standardization of preclinical PET/CT protocols, including CT absorbed dose guidelines, is essentially nonexistent. This study (1) quantitatively assesses the variability of current preclinical PET/CT acquisition and reconstruction protocols routinely used across multiple centers and scanners; and (2) proposes acquisition and reconstruction PET/CT protocols for standardization of multicenter data, optimized for routine scanning in the preclinical PET/CT laboratory. Methods: Five different commercial preclinical PET/CT scanners in Europe and the United States were enrolled. Seven different PET/CT phantoms were used for evaluating biases on default/general scanner protocols, followed by developing standardized protocols. PET, CT, and absorbed dose biases were assessed. Results: Site default CT protocols were the following: greatest extracted Hounsfield units (HU) were 133 HU for water and −967 HU for air; significant differences in all tissue equivalent material (TEM) groups were measured. The average CT absorbed doses for mouse and rat were 72 mGy and 40 mGy, respectively. Standardized CT protocol were the following: greatest extracted HU were −77 HU for water and −990 HU for air; TEM precision improved with a reduction in variability for each tissue group. The average CT absorbed dose for mouse and rat decreased to 37 mGy and 24 mGy, respectively. Site default PET protocols were the following: uniformity was substandard in one scanner, recovery coefficients (RCs) were either over- or underestimated (maximum of 43%), standard uptake values (SUVs) were biased by a maximum of 44%. Standardized PET protocols were the following: scanner with substandard uniformity improved by 36%, RC variability decreased by 13% points, and SUV accuracy improved to 10%. Conclusion: Data revealed important quantitative biases in preclinical PET/CT and absorbed doses with default protocols. Standardized protocols showed improvements in measured PET/CT accuracy and precision with reduced CT absorbed dose across sites. Adhering to standardized protocols generates reproducible and consistent preclinical imaging datasets, thus augmenting translation of research findings to the clinic

Inhibiting CSF1R alleviates cerebrovascular white matter disease and cognitive impairment

\ua9 2023 The Authors. GLIA published by Wiley Periodicals LLC. White matter abnormalities, related to poor cerebral perfusion, are a core feature of small vessel cerebrovascular disease, and critical determinants of vascular cognitive impairment and dementia. Despite this importance there is a lack of treatment options. Proliferation of microglia producing an expanded, reactive population and associated neuroinflammatory alterations have been implicated in the onset and progression of cerebrovascular white matter disease, in patients and in animal models, suggesting that targeting microglial proliferation may exert protection. Colony-stimulating factor-1 receptor (CSF1R) is a key regulator of microglial proliferation. We found that the expression of CSF1R/Csf1r and other markers indicative of increased microglial abundance are significantly elevated in damaged white matter in human cerebrovascular disease and in a clinically relevant mouse model of chronic cerebral hypoperfusion and vascular cognitive impairment. Using the mouse model, we investigated long-term pharmacological CSF1R inhibition, via GW2580, and demonstrated that the expansion of microglial numbers in chronic hypoperfused white matter is prevented. Transcriptomic analysis of hypoperfused white matter tissue showed enrichment of microglial and inflammatory gene sets, including phagocytic genes that were the predominant expression modules modified by CSF1R inhibition. Further, CSF1R inhibition attenuated hypoperfusion-induced white matter pathology and rescued spatial learning impairments and to a lesser extent cognitive flexibility. Overall, this work suggests that inhibition of CSF1R and microglial proliferation mediates protection against chronic cerebrovascular white matter pathology and cognitive deficits. Our study nominates CSF1R as a target for the treatment of vascular cognitive disorders with broader implications for treatment of other chronic white matter diseases

Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

BACKGROUND: The aim of the study was to derive and compare metabolic parameters relating to benign and malignant pulmonary nodules using dynamic 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) PET/CT, and nodule perfusion parameters derived through perfusion computed tomography (CT). PATIENTS AND METHODS: Twenty patients with 21 pulmonary nodules incidentally detected on CT underwent a dynamic 18F-FDG PET/CT and a perfusion CT. The maximum standardized uptake value (SUVmax) was measured on conventional 18F-FDG PET/CT images. The influx constant (Ki ) was calculated from the dynamic 18F-FDG PET/CT data using Patlak model. Arterial flow (AF) using the maximum slope model and blood volume (BV) using the Patlak plot method for each nodule were calculated from the perfusion CT data. All nodules were characterized as malignant or benign based on histopathology or 2 year follow up CT. All parameters were statistically compared between the two groups using the nonparametric Mann-Whitney test. RESULTS: Twelve malignant and 9 benign lung nodules were analysed (median size 20.1 mm, 9-29 mm) in 21 patients (male/female = 11/9; mean age ± SD: 65.3 ± 7.4; age range: 50-76 years). The average SUVmax values ± SD of the benign and malignant nodules were 2.2 ± 1.7 vs. 7.0 ± 4.5, respectively (p = 0.0148). Average Ki values in benign and malignant nodules were 0.0057 ± 0.0071 and 0.0230 ± 0.0155 min-1, respectively (p = 0.0311). Average BV for the benign and malignant nodules were 11.6857 ± 6.7347 and 28.3400 ± 15.9672 ml/100 ml, respectively (p = 0.0250). Average AF for the benign and malignant nodules were 74.4571 ± 89.0321 and 89.200 ± 49.8883 ml/100g/min, respectively (p = 0.1613). CONCLUSIONS: Dynamic 18F-FDG PET/CT and perfusion CT derived blood volume had similar capability to differentiate benign from malignant lung nodules