(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose:
Machine downtime is a major interruption for a clinic. We investigated the sensitivity of MPC and the enhanced MLC check (eMLC) in early detection of machine failure.
Methods:
MPC2.5 was commissioned on 5 Varian TrueBeam, which included consistency check of machine geometry and dosimetry, and independent validation of output, mechanical and isocenter measurements. To test MPC’s sensitivity for machine change, output and mechanical drifts were simulated by mis-calibrating the machine parameters. A new feature in MPC2.7, which measured the positioning offsets and repeatability of each MLC leaf was performed immediately before and after a full MLC service. The dosimetric impact of MLC repeatability was evaluated with repeated measurements of 5 IMRT plan QAs.
Results:
MPC data showed high fidelity and the baseline characteristics of variability in each measured parameter was established for each LINAC. By mis-calibrating machine parameters, MPC promptly detected machine drifts in output (2%), gantry angle (1o), MLC leaf offset (1mm), couch position (3mm), and jaw position (3mm). The detected changes are 4-100 standard deviations larger than the characteristic measurement variability, suggesting the feasibility and confidence in detecting even a much smaller drift in machine parameter. A maximum MLC offset at 1.04 mm and repeatability at 0.87mm predicted a MLC leaf failure. Further, following a full MLC service that reduced the maximum and mean MLC repeatability from 0.71mm to 0.48mm and from 0.40mm to 0.23mm, Gamma analysis between predicted and measured dose on average showed a small but statistically significant (P<0.001, paired t-test) higher passing rate of +0.45%@[1%, 1mm], +0.39%@[2%, 2mm], and +0.27%@[3%, 3mm]. Standard deviation of the passing rate is not significantly correlated with the MLC leaf repeatability.
Conclusion:
MPC and eMLC sensitively detect changes in machine parameters before LINAC down. We recommend to include them into routine QA to reduce downtime and ensure treatment accuracy.
Keywords
Quality Assurance, Portal Imaging
Taxonomy
Not Applicable / None Entered.
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