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Updated: Mar 31, 2026
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
Evaluation of a new motion correction algorithm in PET/CT: combining the entire acquired PET data to create a single
Ryogo Minamimoto1, Takuya Mitsumoto, Yoko Miyata
1aDepartment of Radiology, Division of Nuclear Medicine, National Center for Global Health and Medicine, Tokyo bDepartment of Radiology, National Center for Global Health and Medicine, Kohnodai Hospital, Ichikawa, Japan cDepartment of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, Stanford, California, USA.
Purpose:
This study evaluated the potential of Q.Freeze algorithm for reducing motion artifacts, in comparison with ungated imaging (UG) and respiratory-gated imaging (RG).
Patients And Methods:
Twenty-nine patients with 53 lesions who had undergone RG F-FDG PET/CT were included in this study. Using PET list mode data, five series of PET images [UG, RG, and QF images with an acquisition duration of 3 min (QF3), 5 min (QF5), and 10 min (QF10)] were reconstructed retrospectively. The image quality was evaluated first. Next, quantitative metrics [maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), SD, metabolic tumor volume, signal to noise ratio, or lesion to background ratio] were calculated for the liver, background, and each lesion, and the results were compared across the series.
Results:
QF10 and QF5 showed better image quality compared with all other images. SUVmax in the liver, background, and lesions was lower with QF10 and QF5 than with the others, but there were no statistically significant differences in SUVmean and the lesion to background ratios. The SD with UG and RG was significantly higher than that with QF5 and QF10. The metabolic tumor volume in QF3 and QF5 was significantly lower than that in UG.
Conclusion:
The Q.Freeze algorithm can improve the quality of PET imaging compared with RG and UG.
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