Abstract

Purpose:The CyberKnife robotic radiosurgery system uses Synchrony respiratory motion compensation, which requires independent performance verification. In this work, the RADPOS 4D dosimetry system's motion measurements are compared with internal fiducial position measurements. In addition, RADPOS measurements are compared with Synchrony's predictive correlation model, which is based on internal fiducial and external LED marker position measurements.Methods:A treatment plan was created for a lung insert containing fiducials, RADPOS detector, and Solid Water tumor phantom. Two Quasar Respiratory Motion Phantoms (Q1 and Q2) and two RADPOS detectors (R1 and R2) were used: Q1 simulated lung motion with a lung insert moving in the superior/inferior direction, while Q2 simulated chest motion with a chest platform moving in the anterior/posterior direction. Before treatment, R1 was secured inside of the tumor phantom within Q1, while LED markers and R2 were positioned on the chest platform of Q2. Two treatment delivery cases were studied: isocentric plan (I) and non‐isocentric patient plan (P). Four motion cases were studied: no motion (0), sinusoidal and in‐phase (1), sinusoidal and out‐of‐phase (2), patient waveform and out‐of‐phase (3). A coordinate alignment algorithm was implemented, allowing RADPOS and model position data to be compared within the fiducial coordinate system.Results:The standard deviation of the differences between RADPOS and fiducial position measurements was below 0.6 mm for all experimental cases. The standard deviation of the differences between RADPOS and model position data was 1.0, 1.5, and 1.6 mm along the primary direction of motion for case I1, I2, and P3, respectively.Conclusion:Our work demonstrates that RADPOS is a useful tool for independent quality assurance of CyberKnife treatment with Synchrony respiratory compensation. RADPOS and fiducial position measurement closely match, and RADPOS confirms the effectiveness of CyberKnife's Synchrony motion tracking.This work was supported by OCAIRO (Ontario Consortium for Adaptive Interventions in Radiation Oncology) grant.

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