Dosimetric evaluation of a “virtual” image-guidance alternative to explicit 6 degree of freedom robotic couch correction

Abstract

Clinical evaluation of a "virtual" methodology for providing 6 degrees of freedom (6DOF) patient set-up corrections and comparison to corrections facilitated by a 6DOF robotic couch. A total of 55 weekly in-room image-guidance computed tomographic (CT) scans were acquired using a CT-on-rails for 11 pelvic and head and neck cancer patients treated at our facility. Fusion of the CT-of-the-day to the simulation CT allowed prototype virtual 6DOF correction software to calculate the translations, single couch yaw, and beam-specific gantry and collimator rotations necessary to effectively reproduce the same corrections as a 6DOF robotic couch. These corrections were then used to modify the original treatment plan beam geometry and this modified plan geometry was applied to the CT-of-the-day to evaluate the dosimetric effects of the virtual correction method. This virtual correction dosimetry was compared with calculated geometric and dosimetric results for an explicit 6DOF robotic couch correction methodology. A (2%, 2mm) gamma analysis comparing dose distributions created using the virtual corrections to those from explicit corrections showed that an average of 95.1% of all points had a gamma of 1 or less, with a standard deviation of 3.4%. For a total of 470 dosimetric metrics (ie,maximum and mean dose statistics for all relevant structures) compared for all 55 image-guidance sessions, the average dose difference for these metrics between the plans employing the virtual corrections and the explicit corrections was -0.12% with a standard deviation of 0.82%; 97.9% of all metrics were within 2%. Results showed that the virtual corrections yielded dosimetric distributions that were essentially equivalent to those obtained when 6DOF robotic corrections were used, and that always outperformed the most commonly employed clinical approach of 3 translations only. This suggests that for the patient datasets studied here, highly effective image-guidance corrections can be made without the use of a robotic couch.