Purpose: A dose perturbation model is proposed to assess dose to target(s) that models delivery errors for treatments using respiratory motion tracking on CyberKnife.
Methods: Motion-tracking residual errors are modeled as a spatial convolution of the planned dose with a probability distribution functions derived from data recorded in system log files. Uncorrected rotations are modeled by rotating the target volume about the imaging isocentre. The model estimates the accumulated dose volume by giving equal weights to each perturbation. For experimental validations, ten different experimental conditions are considered, including in-phase and out-of-phase internal and external breathing motion patterns (periodic motion and patient waveforms), with and without uncorrected rotations and for homogenous and heterogeneous phantoms emulating liver anatomy. The measured dose (using EBT3 radiochromic films) is compared to the perturbed dose using the gamma pass rate (2%/2mm or stricter criteria), and the penumbral spread and systematic dose shift if a motion phantom is employed. Four case studies are provided that demonstrate how the model can be used to assess plan robustness to delivery errors. It can also be used to evaluate the suitability of a patient-specific planning target volume (PTV) margins.
Results: The gamma analyses (> 96% using 2%/2mm criteria) and the comparison of penumbral widths (≤ 2 mm) and systematic dose shift (< 2 mm) have validated the performance of this model. Dose to targets considering delivery errors can be significantly improved if treatments are planned following certain guidelines.
Conclusion: The dose perturbation model is validated under various experimental conditions. It can be used to assess retrospectively dose to targets, evaluate the suitability of patient-specific PTV margins, or per-patient sensitivity to errors before treatment. It could enable adaptive planning processes, patient-specific PTV margins, and inform tolerances of treatments to uncorrected rotations.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by The Ontario Consortium for Adaptive Interventions in Radiation Oncology, and a research grant from Accuray, Inc., CA.