Real-time data-driven respiratory gating with optimized automatic VOI selection

Data-driven respiratory gating was previous developed to extract respiratory information directly from PET listmode data. It was also shown that different regions may contribute differently to the accuracy of the motion signal, affecting the successful rate of this method. The goal of this study is to develop and evaluate methods that automatically determine the optimum regions to acquire respiratory motion signal. Maximum likely annihilation point was used to map each listmode event to 3D volume space. Parametric-based volume of interests (VOI) was used for selection of events. For a fixed VOI, center of mass (COM) within each fixed time interval was calculated to generate a motion signal. The signal to noise ratio (SNR) of the acquired motion signal was calculated based on Fourier analysis. The optimum VOI was determined by acquiring the motion signal which has the maximum SNR. Twenty patients with 18F-FDG injection were included in this study to evaluate this method. Conventional method which calculates the motion signal using COM without the selection of VOI was also implemented for comparison. External device were also included for several patients to validate our methods. Fourier analysis of the acquired signal shows that while in only 50% of the patients the respiratory signal peaks were clearly visible in Fourier analysis, more than 90% of the patients show clear respiratory signal peaks using the proposed method. High correlation was achieved with signal measured using external device. Gated reconstructed images using the acquire motion signal also showed much reduced motion blurring and attenuation-activity mismatch artifacts. The results suggested that the successful rate of data-driven respiratory gating improved dramatically using the new methods without user intervention and can be achieved real-time during scan, making it viable for clinical application.