Evaluation of a respiratory motion-corrected image reconstruction algorithm in 2-[(18)F]FDG and [(68)Ga]Ga-DOTA-NOC PET/CT: impacts on image quality and tumor quantification

BACKGROUND: Respiratory motions may cause artifacts on positron emission tomography (PET) images that degrade image quality and quantification accuracy. This study aimed to evaluate the effect of a respiratory motion-corrected image reconstruction (MCIR) algorithm on image quality and tumor quantification compared with nongated/nonmotion-corrected reconstruction. METHODS: We used a phantom consisting of 5 motion spheres immersed in a chamber driven by a motor. The spheres and the background chamber were filled with 18F solution at a sphere-to-background ratio of 5:1. We enrolled 42 and 16 patients undergoing 2-deoxy-2-[(18)F]fluoro-D-glucose {2-[(18)F]FDG} and (68)Ga-labeled [1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid]-1-Nal3-octreotide {[(68)Ga]Ga-DOTA-NOC} PET/computed tomography (CT) from whom 74 and 30 lesions were segmented, respectively. Three reconstructions were performed: data-driven gating-based motion correction (DDGMC), external vital signal module-based motion correction (VSMMC), and noncorrection reconstruction. The standardized uptake values (SUVs) and the volume of the spheres and the lesions were measured and compared among the 3 reconstruction groups. The image noise in the liver was measured, and the visual image quality of motion artifacts was scored by radiologists in the patient study. RESULTS: In the phantom study, the spheres’ SUVs increased by 26–36%, and the volumes decreased by 35–38% in DDGMC and VSMMC compared with the noncorrection group. In the 2-[(18)F]FDG PET patient study, the lesions’ SUVs had a median increase of 10.87–12.65% while the volumes had a median decrease of 14.88–15.18% in DDGMC and VSMMC compared with those of noncorrection. In the [(68)Ga]Ga-DOTA-NOC PET patient study, the lesions’ SUVs increased by 14.23–15.45%, and the volumes decreased by 19.11–20.94% in DDGMC and VSMMC. The image noise in the liver was equal between the DDGMC, VSMMC, and noncorrection groups. Radiologists found improved image quality in more than 45% of the cases in DDGMC and VSMMC compared with the noncorrection group. There was no statistically significant difference in SUVs, volumes, or visual image quality scores between DDGMC and VSMMC. CONCLUSIONS: MCIR improves tumor quantification accuracy and visual image quality by reducing respiratory motion artifacts without compromised image noise performance or elongated acquisition time in 2-[(18)F]FDG and [(68)Ga]Ga-DOTA-NOC PET/CT tumor imaging. The performance of DDG-driven MCIR is as good as that of the external device-driven solution.