ArticleJournal of applied clinical medical physics2026
Computer vision mechanical QA: Development, characterization, and five years of clinical performance.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundQuality assurance (QA) in radiation therapy is a critical component of ensuring accurate and consistent patient treatments, but many tasks are time consuming and rely on human visual acuity, which limits their accuracy. PURPOSE: We aimed to automate monthly mechanical QA tests, improve measurement accuracy and precision, and decrease inter-user variability using a computer vision-based quality assurance (CVQA) system.
methodsA custom marker board incorporating four ArUco markers was created along with a custom camera holder that mounted onto the gantry head. OpenCV was utilized to automate tests for couch translation, collimator and table angles, collimator and table walkout, optical distance indicator (ODI), and field size detection. A GUI was created to guide users through the tests, determine passing status of measurements based on MPPG8.b criteria, and display image captures to allow users to troubleshoot if needed. Reproducibility tests were taken across four days. The system was tested against manual measurements by graph paper or digital level for each test. Field sizes and ODI were read by 12 different physicists to determine human variability and comparisons for CVQA.
resultsThe CVQA system took 5 min to set up and 7 min to perform all tests. All field sizes, collimator/table angles, collimator/table walkout, and table translations were reproducible within 0.5 mm and 0.5°. ODI measurements were reproducible within 1 mm. Table travel, ODI, and walkout measurements agreed with manual measurements within 0.5 mm and 0.4°, except for vertical table motions that agreed within 0.9 mm due to the lens focus being optimized for the 100 cm SSD plane. The standard deviation between physicists for almost all symmetric and asymmetric jaws was larger than the reproducibility of CVQA. CVQA has been utilized for five years and has demonstrated ability to identify mechanical machine issues.
conclusionsCVQA successfully automates mechanical QA tasks, providing an efficient and precise system. CVQA is open source and freely available for academic institutions. Its adoption can improve workflow efficiency and consistency in clinical environments.
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