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Automated Linac QA Using Scripting and Varian Developer Mode

K Pearman*, N Koch, C Vanderstraeten, B Sintay, D Wiant, Q Shang, T Hayes, H Liu, Cone Health Cancer Center, Greensboro, NC


(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: quality assurance (QA) can be time consuming involving set up, execution, analysis and subject to user variability. The purpose of this study is to develop quatitative automation tools for mechanical and imaging QA to improve efficiency, consistency and accuracy.

Methods: QA has been performed with graph paper, film, and multiple phantoms with different set-ups. Analysis consists of ruler and vendor provided software. After setting up four phantoms on the treatment couch, a series of XML scripts are executed sequentially using Varian TrueBeam Developer Mode. Data are collected to check the light-radiation coincidence, kV and MV imaging quality, table motion, and isocentricity. Additionally, non-phantom QA tests collect EPID images to check the jaw positions, dose rate, MLC position, MLC and gantry speed, star shots and Winston-Lutz. The acquired EPID images are exported and analyzed using inhouse MATLAB codes.

Results: time savings were 3.7 hours per Linac. MLC position, star shots and Winston-Lutz giving the largest savings. Consistency improvements (standard deviation) were observed for some tests using the new methods. For example: field size improved from 0.11mm to 0.04mm and table motion improved from 0.17mm to 0.12mm. No noticeable STD change was observed for Isocentricity. We noticed a decrease in STD from 0.33mm to 0.41mm for light-radiation coincidence test, likely due to the new method having the jaws manually adjusted to match the light field. There was a small accuracy drop for jaw positions possible due to calibration being performed with graph paper and not the script. Isocentricity showed an increase in measurement accuracy from 0.47mm to 0.15mm. Table motion indicated a decrease in measurement accuracy from 0.20mm to 0.51mm, possibly due to the tendency of the user to over approximate the visual measurement.

Conclusion: is a viable, accurate and efficient option for monthly and annual QA.


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