Effects of different reconstruction algorithms on SUV of pulmonary nodules in 18F-FDG PET/CT / 中华核医学与分子影像杂志

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(SUV max), mean of SUV(SUV mean) and peak of SUV (SUV peak) 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 (%&Delta;SUV) 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 SUV max and SUV mean between G1-G4 (median SUV max 2.65-5.29, median SUV mean 2.05-2.99; H values 20.628 and 17.749, respectively, both P<0.001), G4 had significant increases compared to G1 in SUV max (median 5.29 and 2.65, P<0.001) and SUV mean (median 2.99 and 2.05, P<0.001). The %&Delta;SUV max (median 4.45%-52.96%) and %&Delta;SUV mean (median 1.69%-47.56%) were negatively correlated with the diameter of nodules (9.75(6.20, 16.58) mm; r s values -0.371 to -0.354, -0.371 to -0.320, all P<0.001). In 59 small nodules, G1 significantly increased the SUV max of G4 (median 4.05 and 2.14, H=18.327, P<0.001), while G4 significantly increased the SUV mean of G1 and G3 (median 2.31, 1.26 and 1.53, H=16.808, P<0.05). There was no significant difference in SUVs between G1-G4 in 55 large nodules ( H values 0.812-7.290, all P>0.05). The optimal threshold values of SUV max 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 SUV mean and SUV peak also showed a similar trend. Conclusion:Among the four reconstruction algorithms, BRERM can not only enhance the image quality, but also significantly improve the SUV max and SUV mean of lung nodules diameter below 10 mm, and thus its diagnostic threshold of SUV should be appropriately increased.