Purpose: Data-driven gating (DDG) approaches for positron emission tomography (PET) are interesting alternatives to conventional hardware-based gating methods. In DDG, the measured PET data themselves are utilized to calculate a respiratory signal, that is, subsequently used for gating purposes. The success of gating is then highly dependent on the statistical quality of the PET data. In this study, we investigate how this quality determines signal noise and thus motion resolution in clinical PET scans using a center-of-mass-based (COM) DDG approach, specifically with regard to motion management of target structures in future radiotherapy planning applications.
Methods: PET list mode datasets acquired in one bed position of 19 different radiotherapy patients undergoing pretreatment [18 F]FDG PET/CT or [18 F]FDG PET/MRI were included into this retrospective study. All scans were performed over a region with organs (myocardium, kidneys) or tumor lesions of high tracer uptake and under free breathing. Aside from the original list mode data, datasets with progressively decreasing PET statistics were generated. From these, COM DDG signals were derived for subsequent amplitude-based gating of the original list mode file. The apparent respiratory shift d from end-expiration to end-inspiration was determined from the gated images and expressed as a function of signal-to-noise ratio SNR of the determined gating signals. This relation was tested against additional 25 [18 F]FDG PET/MRI list mode datasets where high-precision MR navigator-like respiratory signals were available as reference signal for respiratory gating of PET data, and data from a dedicated thorax phantom scan.
Results: All original 19 high-quality list mode datasets demonstrated the same behavior in terms of motion resolution when reducing the amount of list mode events for DDG signal generation. Ratios and directions of respiratory shifts between end-respiratory gates and the respective nongated image were constant over all statistic levels. Motion resolution d/dmax could be modeled as d/dmax=1-e-1.52(SNR-1)0.52, with dmax as the actual respiratory shift. Determining dmax from d and SNR in the 25 test datasets and the phantom scan demonstrated no significant differences to the MR navigator-derived shift values and the predefined shift, respectively.
Conclusions: The SNR can serve as a general metric to assess the success of COM-based DDG, even in different scanners and patients. The derived formula for motion resolution can be used to estimate the actual motion extent reasonably well in cases of limited PET raw data statistics. This may be of interest for individualized radiotherapy treatment planning procedures of target structures subjected to respiratory motion.
Keywords: PET; PET/CT; PET/MRI; data-driven gating; respiratory motion.
© 2018 American Association of Physicists in Medicine.