Impact of Bayesian penalized likelihood reconstruction on quantitative and qualitative aspects for pulmonary nodule detection in digital 2-[ 18 F]FDG-PET/CT.

To evaluate the impact of block sequential regularized expectation maximization (BSREM) reconstruction on quantitative and qualitative aspects of 2-[ ¹⁸ F]FDG-avid pulmonary nodules compared to conventional ordered subset expectation maximization (OSEM) reconstruction method. Ninety-one patients with 144 2-[ ¹⁸ F]FDG-avid pulmonary nodules (all ≤ 20 mm) undergoing PET/CT for oncological (re-)staging were retrospectively included. Quantitative parameters in BSREM and OSEM (including point spread function modelling) were measured, including maximum standardized uptake value (SUV _max ). Nodule conspicuity in BSREM and OSEM images was evaluated by two readers. Wilcoxon matched pairs signed-rank test was used to compare quantitative and qualitative parameters in BSREM and OSEM. Pulmonary nodule SUV _max was significantly higher in BSREM images compared to OSEM images [BSREM 5.4 (1.2-20.7), OSEM 3.6 (0.7-17.4); p = 0.0001]. In a size-based analysis, the relative increase in SUV _max was more pronounced in smaller nodules (≤ 7 mm) as compared to larger nodules (8-10 mm, or > 10 mm). Lesion conspicuity was higher in BSREM than in OSEM (p < 0.0001). BSREM reconstruction results in a significant increase in SUV _max and a significantly improved conspicuity of small 2-[ ¹⁸ F]FDG-avid pulmonary nodules compared to OSEM reconstruction. Digital 2-[ ¹⁸ F]FDG-PET/CT reading may be enhanced with BSREM as small lesion conspicuity is improved.
(© 2022. The Author(s).)