Evaluation of a new motion correction algorithm in PET/CT: combining the entire acquired PET data to create a single three-dimensional motion-corrected PET/CT image

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Abstract

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 18F-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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