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Objectives: To evaluate the impact of fully automatic motion correction by data-driven respiratory gating (DDG) on positron emission tomography (PET) image quality, lesion detection and patient management. Materials and Methods: A total of 149 patients undergoing PET/CT for cancer (re-)staging were retrospectively included. Patients underwent a PET/CT on a digital detector scanner and for every patient a PET data set where DDG was enabled (PETDDG) and as well as where DDG was not enabled (PETnonDDG) was reconstructed. All PET data sets were evaluated by two readers which rated the general image quality, motion effects and organ contours. Further, both readers reviewed all scans on a case-by-case basis and evaluated the impact of PETDDG on additional apparent lesion, change of report, and change of management. Results: In 85% (n = 126) of the patients, at least one bed position was acquired using DDG, resulting in mean scan time increase of 4:37 min per patient in the whole study cohort (n = 149). General image quality was not rated differently for PETnonDDG and PETDDG images (p = 1.000) while motion effects (i.e. indicating general blurring) was rated significantly lower in PETDDG images and organ contours, including liver and spleen, were rated significantly sharper using PETDDG as compared to PETnonDDG (all p < 0.001). In 27% of patients, PETDDG resulted in a change of the report and in a total of 12 cases (8%), PETDDG resulted in a change of further clinical management. Conclusion: Deviceless DDG provided reliable fully automatic motion correction in clinical routine and increased lesion detectability and changed management in a considerable number of patients. Advances in knowledge: DDG enables PET/CT with respiratory gating to be used routinely in clinical practice without external gating equipment needed.

Background The aim of this study was to evaluate and compare PET image reconstruction algorithms on novel digital silicon photomultiplier PET/CT in patients with newly diagnosed and histopathologically confirmed lung cancer. A total of 45 patients undergoing 18F-FDG PET/CT for initial lung cancer staging were included. PET images were reconstructed using ordered subset expectation maximization (OSEM) with time-of-flight and point spread function modelling as well as Bayesian penalized likelihood reconstruction algorithm (BSREM) with different β-values yielding a total of 7 datasets per patient. Subjective and objective image assessment with all image datasets was carried out, including subgroup analyses for patients with high dose (> 2.0 MBq/kg) and low dose (≤ 2.0 MBq/kg) of 18F-FDG injection regimen. Results Subjective image quality ratings were significantly different among all different reconstruction algorithms as well as among BSREM using different β-values only (both p < 0.001). BSREM with a β-value of 600 was assigned the highest score for general image quality, image sharpness, and lesion conspicuity. BSREM reconstructions resulted in higher SUVmax of lung tumors compared to OSEM of up to + 28.0% (p < 0.001). BSREM reconstruction resulted in higher signal-/ and contrast-to-background ratios of lung tumor and higher signal-/ and contrast-to-noise ratio compared to OSEM up to a β-value of 800. Lower β-values (BSREM450) resulted in the best image quality for high dose 18F-FDG injections, whereas higher β-values (BSREM600) lead to the best image quality in low dose 18F-FDG PET/CT (p < 0.05). Conclusions BSREM reconstruction algorithm used in digital detector PET leads to significant increases of lung tumor SUVmax, signal-to-background ratio, and signal-to-noise ratio, which translates into a higher image quality, tumor conspicuity, and image sharpness.