Quantification of Intrafraction and Interfraction Tumor Motion Amplitude and Prediction Error for Different Liver Tumor Trajectories in Cyberknife Synchrony Tracking

Purpose To research the fiducial-based, real-time tracking intrafraction (during the fraction [intra-]) and interfraction (between fractions [inter-]) tumor respiration amplitude, motion trajectory, and prediction error and quantify their relationships for different types of motion trajectories during Cyberknife-based stereotactic ablation radiotherapy. Methods and Materials Twelve patients with liver tumors were treated using a Cyberknife system, and 58 fractions were involved in this study. Real-time target motion tracking data were extracted and transformed from the robot coordinate system into the patient coordinate system by the rotation matrix. Only the time sessions of the beam on were studied according to the data information generated from the Cyberknife motion tracking system. The motion correlation model between the external marker signal and internal fiducial position was built to present the type of motion trajectory. Results Using the correlation model as a function of external marker signal and internal fiducial position, we knew 4 motion trajectories mainly existed for liver cancer patients as follows: perfect linearity (group I), simple linearity (group II), hysteresis (group III), and area respiratory (group IV) patterns. More than half of the patients had a linear breathing trajectory. Analyzing all patients together, the intra-amplitudes were slightly less than those of the inter-amplitudes. The amplitude from large to small was in the superior-inferior, left-right and anterior-posterior directions, regardless of inter- and intra-amplitudes. Then, patients with a larger peak-to-peak have a larger standard deviation of amplitude and a larger amplitude in all fractions/sessions. The prediction errors of the linear motion trajectory were generally less than 1 mm. The prediction errors of the regular hysteresis breathing model were smaller than those of the irregular hysteresis model. Scattered breathing would result in a larger tracking error, such as the area respiratory trajectory. It was logical that prediction errors were larger for patients who showed much variation in their breathing amplitude. Conclusions This paper showed that the liver motion trajectory model included perfect linearity, sample linearity, hysteresis, and area. The linear motion trajectory presented the minimum tracking error and the best stability, and the hysteresis and area trajectory were the worst. Therefore, breathing management, including respiration training, control, and evaluation of motion trajectory in all directions, was significantly necessary during liver SABR treatment.