Effects of different reconstruction algorithms on SUV of pulmonary nodules in 18F-FDG PET/CT

Abstract

Objective To compare four reconstruction algorithms of 18F-fluorodeoxyglucose (FDG) PET/CT on standardized uptake value (SUV) of pulmonary nodules. Methods A total of 46 patients (27 males, 19 females; median age: 66 (range: 44-82) years) with solid pulmonary nodules from February 2018 to July 2019 in the First Hospital of Shanxi Medical University who performed 18F-FDG PET/CT imaging were enrolled. All PET/CT images were retrospectively reconstructed by using four algorithms reconstructions including ordered subset expectation maximization (OSEM), OSEM+ time of flight (TOF), OSEM+ TOF+ point spread function (PSF) and block sequential regularized expectation maximization (BSREM) (G1-G4). Nodule and background parameters were analyzed semi-quantitatively and visually. The maximum of SUV(SUVmax), mean of SUV(SUVmean) and peak of SUV (SUVpeak) were collected by the region of interest (ROI). Nodules were divided into small nodule group (diameter ≤10 mm) and large nodule group (10 mm < diameter ≤30 mm). Kruskal-Wallis rank sum test and Bonferroni method were performed to compare the differences of SUVs between G1-G4, and Spearman correlation analysis was used to analyze the correlation between the change rate of SUV (%ΔSUV) and the diameter of nodules. The receiver operating characteristic (ROC) curve analysis was used to analyze the diagnostic efficacy of SUV for the differential diagnosis of pulmonary nodules and to get the optimal threshold. Results There were 114 pulmonary nodules (large nodules, n=55; small nodules, n=59). In visual analysis, the visual detection rates of small nodules in G4 were 55.93%(33/59), 44.07%(26/59), 20.34%(12/59) higher than those in G1-G3. Of 114 pulmonary nodules in 46 patients, there were differences in SUVmax and SUVmean between G1-G4 (median SUVmax : 2.65-5.29, median SUVmean: 2.05-2.99; H values: 20.628 and 17.749, respectively, both P 0.05). The optimal threshold values of SUVmax in G1-G4 were 4.335, 5.185, 5.410, 5.745 and the area of under curves (AUCs) were 0.747, 0.699, 0.756, 0.778 respectively. The AUC of SUVmean and SUVpeak also showed a similar trend. Conclusion Among the four reconstruction algorithms, BRERM can not only enhance the image quality, but also significantly improve the SUVmax and SUVmean of lung nodules diameter below 10 mm, and thus its diagnostic threshold of SUV should be appropriately increased. Key words: Lung neoplasms; Positron-emission tomography; Tomography, X-ray computed; Image processing, computer-assisted; Deoxyglucose