Automated Analysis Tool for Annual Beam-Scanning QA
Abstract
Purpose
Annual linac beam scanning QA is time-intensive and often relies on manual data handling, spreadsheet analysis, and site-specific workflows. These steps can be tedious and inconsistent, even when the underlying measurements are solid. The goal of this work was to build and evaluate an automated workflow to analyze for beam scanning data acquired during annual linac QA (PDD’s and Profiles), reducing hands-on time while improving consistency and data management.
Methods
An automated analysis workflow was developed using Radmachine test lists to process annual photon and electron PDD’s and profiles from 10 linear accelerators. The photon energies included 6X, 15X, 6FFF, and 10FFF, while the electron energies included 6, 9, 12, 16, and 20 MeV. Calculation tests were implemented in a Python 3.9 scripting environment to process uploaded measurement files, apply consistent resampling, and generate quantitative comparisons. The workflow successfully reproduced existing clinical metrics and added other metrics, such as distance-to-agreement (DTA) and one-dimensional gamma analysis. Comparisons were performed against both treatment planning system data and commissioning baseline measurements. Performance and time requirements were compared to the existing manual spreadsheet-based workflow.
Results
Automation reduced the time required for analysis and report generation from approximately two hours per machine to 5–10 minutes per machine. Automated results agreed with the existing manual process within established annual QA tolerances, while providing additional quantitative information not previously captured. Reports were generated automatically and stored in a centralized QA database with network backups, improving documentation consistency and long-term accessibility.
Conclusion
Automating annual beam scanning QA within Radmachine significantly reduces workload while improving consistency and documentation quality. The workflow enhances quantitative evaluation and supports centralized QA recordkeeping, offering a straightforward and scalable approach for modern clinical linac QA programs.