MR‐linac daily semi‐automated end‐to‐end quality control verification

Abstract

AbstractPurposeAdaptive radiation therapy (ART) on the integrated Elekta Unity magnetic resonance (MR)‐linac requires routine quality assurance to verify delivery accuracy and system data transfer. In this work, our objective was to develop and validate a novel automated end‐to‐end test suite that verifies data transfer between multiple software platforms and quantifies the performance of multiple machine subcomponents critical to the ART process.MethodsWe designed and implemented a software tool to quantify the MR and megavoltage (MV) isocenter coincidence, treatment couch positioning consistency, isocenter shift accuracy for the adapted plan as well as the MLC and jaw position accuracy following the beam aperture adaptation. Our tool employs a reference treatment plan with a simulated isocenter shift generated on an MR image of a readily available phantom with MR and MV visible fiducials. Execution of the test occurs within the standard adapt‐to‐position (ATP) clinical workflow with MV images collected of the delivered treatment fields. Using descriptive statistics, we quantified uncertainty in couch positioning, isocentre shift as well as the jaw and MLC positions of the adapted fields. We also executed sensitivity measurements to evaluate the detection algorithm's performance.ResultsWe report the results of 301 daily testing instances. We demonstrated consistent tracking of the MR‐to‐MV alignment with respect to the established value and to detect small changes on the order of 0.2 mm following machine service events. We found couch position consistency relative to the test baseline value was within 95% CI [–0.31, 0.26 mm]. For phantom shifts that form the basis for the plan adaptation, we found agreement between MV‐image‐detected phantom shift and online image registration, within ± 1.5 mm in all directions with a 95% CI difference of [–1.29, 0.79 mm]. For beam aperture adaptation accuracy, we found differences between the planned and detected jaw positions had a mean value of 0.27 mm and 95% CI of [–0.29, 0.82 mm] and –0.17 mm and 95% CI of [–0.37, 0.05 mm] for the MLC positions. Automated fiducial detected accuracy was within 0.08 ± 0.20 mm of manual localization. Introduced jaw and MLC position errors (1–10 mm) were detected within 0.55 mm (within 1 mm for 15/256 instances for the jaws). Phantom shifts (1.3 or 5 mm in each cardinal direction) from a reference position were detected within 0.26 mm.ConclusionsWe have demonstrated the accuracy and sensitivity of a daily end‐to‐end test suite capable of detecting errors in multiple machine subcomponents including system data transfer. Our test suite evaluates the entire treatment workflow and has captured system communication issues prior to patient treatment. With automated processing and the use of a standard vendor‐provided phantom, it is possible to expand to other Unity sites.