Artificial Intelligence Powered Direct Prediction of Linear Accelerator Machine Parameters: Towards a New Paradigm for Patient Specific Pre-Treatment QA

Purpose: Traditional pre-treatment patient specific QA is known for its high workload for physicist, ineffectiveness at identifying clinically relevant dosimetric uncertainties of treatment plans, and incompatibility with on-line adaptive radiotherapy. Our purpose is to develop a trajectory file based PSQA procedure that allows for a virtual pre-treatment QA that can effectively evaluate the performance and robustness of a treatment plan via a DVH based analysis and can be carried out with online adaptive radiotherapy. For this purpose, we have developed a machine learning model that can predict discrepancy in machine parameters between delivery and treatment plan on a Varian TrueBeam linear accelerator.

Methods: Trajectory log files and DICOM-RT plan files of 30 IMRT plans and 75 VMAT plans from four Varian TrueBeam linear accelerators were collected for analysis. The discrepancy in machine parameters is divided into “conversion error” (from converting DICOM-RT to deliverable machine trajectory) and “delivery error” (difference in machine parameters recorded in trajectory files). Correlation matrices were obtained to determine the linear correlation between actual discrepancy and mechanical parameters, such as MLC velocity, MLC acceleration, control point, dose rate, gravity vector, gantry velocity, and gantry acceleration. Multiple regression algorithms were used to develop machine learning models to predict the total discrepancy in machine parameters and its components based on mechanical parameters. The fully trained models were validated with an independent validation dataset and treatment plans constructed with varying degrees of complexity approaching the limitations of the linear accelerator.

Results: For both IMRT and VMAT, the RMS of conversion error (0.1528 mm) was 4 times greater than the RMS of delivery error (0.0367 mm). A high correlation existed between MLC velocity and both components of discrepancies for IMRT (R2 ∈ [0.61, 0.75]) and VMAT [0.75, 0.85]). Final models trained by data from all linear accelerators can predict MLC delivery errors, conversion errors, and combined errors with a high degree of accuracy and correlation between predicted and actual errors for IMRT (R2 = 0.99, 0.86, 0.98) and VMAT (R2 = 0.84, 0.86, 0.87).

Conclusion: We developed an AI model that can predict total MLC discrepancy on Varian TrueBeam linear accelerator with high accuracy using mechanical parameters from trajectory log files and DICOM-RT plans. The software tool from our previous study has been updated to incorporate the discrepancy in planned position into the predictions of total delivery error. We have released the tool for public uses to enable researchers to simulate a treatment delivery without a physical delivery. The tool also has promise in clinical scenarios by allowing for a virtual pre-treatment QA and can be carried out with online adaptive radiotherapy, thereby increasing the effectiveness of pre-treatment patient specific QA.