Abstract
OBJECTIVES: The present study aimed to investigate the influence of the deep progressive learning reconstruction (DPR) algorithm on the (18)F-FDG PET image quality and quantitative parameters. METHODS: In this retrospective study, data were collected from 55 healthy individuals and 184 patients with primary malignant pulmonary tumors who underwent (18)F-FDG PET/CT examinations. PET data were reconstructed using the ordered subset expectation maximization (OSEM) and DPR algorithms. The influence of DPR algorithm on quantitative parameters was explored, including the SUV(max), SUV(mean), standard deviation of SUV (SUV(SD)), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and tumor-to-background uptake ratio (TBR). Finally, the differences in image quality parameters, including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), between the two reconstruction algorithms were evaluated. RESULTS: DPR algorithm significantly reduced the SUV(max) and SUV(SD) of background tissues (all, P < 0.001) compared to OSEM algorithm, while no statistical difference was observed in SUV(mean) between the two algorithms (all, P > 0.05). DPR algorithm notably increased the SUV(max), SUV(mean), and TBR of lesions (all, P < 0.001) and reduced MTV (P = 0.005), with minimal differences in TLG noted between the reconstruction algorithms (P < 0.001). The percentage differences in SUV(max) (P = 0.001), SUV(mean) (P = 0.005), and TBR (P = 0.001) between the two algorithms were significantly higher in solid nodules than in pure ground glass nodules (pGGNs). The ΔCNR between solid nodules (P = 0.031) and mixed ground glass nodules (P = 0.020) was greater than that between pGGNs. SNR and CNR obtained using the DPR algorithm were markedly improved compared to those determined using the OSEM algorithm (all, P < 0.001). CONCLUSION: Under identical acquisition conditions, the DPR algorithm enhanced the accuracy of quantitative parameters in pulmonary lesions and potentially improved lesion detectability. The DPR algorithm increased image SNR and CNR compared to those obtained using the OSEM algorithm, significantly optimizing overall image quality. This advancement facilitated precise clinical diagnosis, underpinning its potential to significantly contribute to the field of medical imaging.