Program Information
Evaluation of Elekta Agility MLC Performance Using Statistical Process Control
SM Meyers*, MJ Balderson , D Letourneau , Princess Margaret Cancer Centre and University of Toronto, Toronto, ON
Presentations
SU-D-201-4 (Sunday, July 31, 2016) 2:05 PM - 3:00 PM Room: 201
Purpose: to evaluate the performance and stability of the Elekta Agility MLC model using an automated quality control (QC) test in combination with statistical process control tools.
Methods: Leaf positions were collected daily for 11 Elekta units over 5-19 months using the automated QC test, which analyzes 23 MV images to determine the location of MLC leaves relative to the radiation isocenter. The leaf positions are measured at 5 nominal positions, and images are acquired at collimator 0° and 180° to capture all MLC leaves in the field-of-view. Leaf positioning accuracy was assessed using individual and moving range control charts. Control limits were recomputed following MLC recalibration (occurred 1-2 times for 4 units). Specification levels of ±0.5, ±1 and ±1.5mm were tested. The mean and range of duration between out-of-control and out-of-specification events were determined.
Results: Leaf position varied little over time, as confirmed by very tight individual control limits (mean ±0.19mm, range 0.09-0.44). Mean leaf position error was -0.03mm (range -0.89–0.83). Due to sporadic out-of-control events, the mean in-control duration was 3.3 days (range 1-23). Data stayed within ±1mm specification for 205 days on average (range 3-372) and within ±1.5mm for the entire date range. Measurements stayed within ±0.5mm for 1 day on average (range 0-17); however, our MLC leaves were not calibrated to this level of accuracy.
Conclusion: The Elekta Agility MLC model was found to perform with high stability, as evidenced by the tight control limits. The in-specification durations support the current recommendation of monthly MLC QC tests with a ±1mm tolerance. Future work is on-going to determine if Agility performance can be optimized further using high-frequency QC test results to drive recalibration frequency. Factors that can affect leaf positioning accuracy, including beam spot motion, leaf gain calibration, drifting leaves, and image artifacts, are under investigation.
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