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Purpose  This study evaluated the effects of new Bayesian penalized likelihood (BPL) reconstruction algorithm on visualization and quantification of upper abdominal malignant tumors in clinical FDG PET/CT examinations, comparing the results to those obtained by an ordered subset expectation maximization (OSEM) reconstruction algorithm. Metabolic tumor volume (MTV) and texture features (TFs), as well as SUV-related metrics, were evaluated to clarify the BPL effects on quantification.    Materials and Methods  A total of 153 upper abdominal lesions (82 liver metastatic and 71 pancreatic cancers) were included in this study. FDG PET/CT images were acquired with a GE Discovery 710 scanner equipped with a time-of-flight system. Images were reconstructed using OSEM and BPL (beta 700) algorithms. In 58 lesions <1.5 cm in greatest diameter (small-lesion group), visual image quality of each lesion was evaluated using a four-point scale. SUVmax was obtained for quantitative metrics. Visual scores and SUVmax were compared between OSEM and BPL images. In 95 lesions >2.0 cm in greatest diameter (larger-lesion group), SUVmax, SUVpeak, MTV, and six TFs were compared between OSEM and BPL images. In addition to the size-based analyses, an increase of SUVmax with BPL was evaluated according to the original SUVmax in OSEM images.    Results  In the small-lesion group, both visual score and SUVmax were significantly higher in the BPL than OSEM images. The increase in visual score was observed in 20 (34%) of all 58 lesions. In the larger-lesion group, no statistical difference was observed in SUVmax, SUVpeak, or MTV between OSEM and BPL images. BPL increased high gray-level zone emphasis and decreased low gray-level zone emphasis among six TFs compared to OSEM with statistical significance. No statistical differences were observed in other TFs. SUVmax-based analysis demonstrated that BPL increased and decreased SUVmax in lesions with low (<5) and high (>10) SUVmax in original OSEM images, respectively.    Conclusion  This study demonstrated that BPL improved conspicuity of small or low-count upper abdominal malignant lesions in clinical FDG PET/CT examinations. Only two TFs represented significant differences between OSEM and BPL images of all quantitative metrics in larger lesions.

frontiers in oncology

Effects of New Bayesian Penalized Likelihood Reconstruction Algorithm on Visualization and Quantification of Upper Abdominal Malignant Tumors in Clinical FDG PET/CT Examinations