A Tool for Approximating Radiotherapy Delivery via Informed Simulation (TARDIS)

Purpose: The multi-leaf collimator (MLC) is a critical component in intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). The MLC discrepancies between planned and actual position directly affects the quality of treatment. This study analyzed MLC positional discrepancies and gantry angle discrepancies via trajectory log files from Varian TrueBeam linear accelerators to determine the consistency of machine performance accuracy over the course of treatment. The mechanical parameters that affect accuracy were examined and evaluated to build a machine learning algorithm to predict moving components’ discrepancies. A tool was developed to predict the treatment delivery discrepancies on a Varian TrueBeam linear accelerator for any given plan, which can simulate radiotherapy treatment delivery without actual delivery.

Methods: Trajectory log files of 116 IMRT plans and 125 VMAT plans from nine Varian TrueBeam linear accelerators were collected and analyzed. Data was binned by treatment site and machine type to determine their relationship with MLC and gantry angle discrepancies. Trajectory log files were used to evaluate whether MLC positional accuracy was consistent between patient-specific quality assurance (QA) and the course of treatment. Mechanical parameters including MLC velocity, MLC acceleration, gantry angle, gantry velocity, gantry acceleration, collimator angle, control point, dose rate, and gravity vector were analyzed to evaluate correlations with delivery discrepancies. A regression model was used to develop a machine learning algorithm to predict delivery discrepancies based on mechanical parameters.

Results: MLC discrepancies at pre-treatment patient-specific QA differed from the course of treatment by a small (mean = 0.0031 ± 0.0036 mm, p = 0.0089 for IMRT; mean = 0.0014 ± 0.0016 mm, p = 0.0003 for VMAT) but statistically significant amount, likely due to setting the gantry angle to zero for QA. Mechanical parameters showed significant correlation with MLC discrepancies, especially MLC velocity, which had an approximately linear relationship (β = -0.0027, R2 = 0.79). Incorporating other mechanical parameters, the final generalized model trained by data from all linear accelerators can predict MLC errors to a high degree of accuracy with high correlation (R2 = 0.86) between predicted and actual errors. The same prediction model performed well across different treatment sites and linear accelerators; however, a significant difference was found in the predictions made by models trained using different treatment techniques (IMRT vs VMAT) (mean difference of RMSE = 0.0153 ± 0.0040 mm).

Conclusion: We have developed a machine learning model using prior trajectory log files to predict the MLC discrepancies on TrueBeam linear accelerators. This model has been a released as a research tool in which a DICOM-RT with predicted MLC positions can be generated using the original DICOM-RT file as input. This tool can be used to simulate radiotherapy treatment delivery and may be useful for studies evaluating plan robustness and dosimetric uncertainties from treatment delivery.