Room: AAPM ePoster Library
Purpose: automate the analysis of routine linac imaging and MLC QA with pylinac tools.
Methods: is a TG-142 quality assurance (QA) tool based on the popular Python language. In addition to high-level modules for automatically analyzing images and data generated by linear accelerator, you can also customize your own analysis tool. In many radiation oncology departments, IT department imposes many restrictions to software installation and web traffic. I identified a portable option of python implementation and a way to circumvent the network restriction to send text or email notifications when QA results fall outside predefined threshold.
Results: customized script was written to analyze MLC leaf position accuracy in addition to other common QA tests such as Picket Fence, Winston-Lutz, gantry speed and leaf speed, as well as MV and kV imaging qualities. The script can automatically scan the preset folder and generate reports, and when QA test fails, send a text or email to the responsible party. It can also be scaled up easily by simply editing the machine list, folder list and contact person list.
Conclusion: linac imaging and MLC QA used to be a very time consuming task at my institution because the data collection, analysis and report generation are all done manually. With the python script utilizing pylinac tools, the analysis and report generation are automated and only take a couple minutes.
Not Applicable / None Entered.