Clinical evaluation of a data-driven respiratory gating algorithm for whole-body positron emission tomography with continuous bed motion

Abstract

Respiratory gating is the standard to overcome respiration effects degrading image quality in positron emission tomography (PET). Data-driven gating (DDG) using signals derived from PET raw data are promising alternatives to gating approaches requiring additional hardware. However, continuous bed motion (CBM) scans require dedicated DDG approaches for axially-extended PET, compared to DDG for conventional step-and-shoot scans. In this study, a CBM-capable DDG algorithm was investigated in a clinical cohort, comparing it to hardware-based gating using gated and fully motion-corrected reconstructions. Methods: 56 patients with suspected malignancies in thorax or abdomen underwent whole-body ¹⁸F-FDG CBM-PET/CT imaging using DDG and hardware-based respiratory gating (pressure-sensitive belt gating, BG). Correlation analyses were performed on both gating signals. Besides static reconstructions, BG and DDG were used for optimally-gated PET (BG-OG, DDG-OG) and fully motion-corrected PET (elastic motion correction; BG-EMOCO, DDG-EMOCO). Metabolic volumes, SUV_max and SUVmean of lesions were compared amongst the reconstructions. Additionally, the quality of lesion delineation in different PET reconstructions was independently evaluated by three experts. Results: Global correlation coefficients between BG and DDG signals amounted to 0.48±0.11, peaking at 0.89±0.07 when scanning the kidney and liver region. In total, 196 lesions were analyzed. SUV measurements were significantly higher in BG-OG, DDG-OG, BG-EMOCO and DDG-EMOCO compared to static images (P<0.001; median SUV_max: static, 14.3±13.4; BG-EMOCO, 19.8±15.7; DDG-EMOCO, 20.5±15.6; BG-OG, 19.6±17.1; DDG-OG, 18.9±16.6). No significant differences between BG-OG and DDG-OG, and BG-EMOCO and DDG-EMOCO, respectively, were found. Visual lesion delineation was significantly better in BG-EMOCO and DDG-EMOCO than in static reconstructions (P<0.001); no significant difference was found comparing BG and DDG (EMOCO, OG, respectively). Conclusion: DDG-based motion-compensation of CBM-PET acquisitions outperforms static reconstructions, delivering qualities comparable to hardware-based approaches. The new algorithm may be a valuable alternative for CBM-PET systems.