Purpose: The purpose of this study was to develop a novel one-key automating quality control (QA) system to perform routine machine QA on a new medical linear accelerator uRT506c using a cylindrical phantom and acquired EPID images. The authors also evaluated the machine performance of this new linac over a 4-month period.
Methods: This automating QA system consists of two main parts: (1) a hollow cylindrical phantom with 18 steel balls in the phantom surface; (2) an analysis software to process electronic portal imaging device (EPID) measurement image data and rendering results. The phantom is placed at the head of the treatment couch and aligned according to the laser. All test plans were integrated into one plan to facilitate the automatic execution of test plans and a total of 40 EPID images were automatically acquired. The system includes EPID position and image quality self-calibration to improve the accuracy of data. The tests included MLC/EPID/Jaw/couch positioning accuracy, collimator/gantry/couch rotation isocenter and angle accuracy, the beam consistency and localizing laser accuracy. The authors also evaluated the effect of analysis results on the phantom set up errors.
Results: This one-key automating QA system was able to automatically deliver QA plan, EPID image acquisition and automatic analysis. The mean time for performing the machine QA was about 4.6 minutes. The data from a total of 31 QA items over 4-month period showed this new accelerator has good machine performance and all the items are within the tolerance specified by AAPM TG-142. The phantom set up error has no effect on the QA results and the laser position deviation (-2 mm in all three directions) can be detected accurately.
Conclusion: This one-key automating QA system succeeded in performing automating machine QA on a new linac. It can provide high-precision results and improve the QA efficiency.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the Science and technology project of Guangdong Esophageal Cancer Institute (Q202008).