SU-E-T-207: Flatness and Symmetry Threshold Detection Using Statistical Process Control

PURPOSE

AAPM TG-142 guidelines state that beam uniformity (flatness and symmetry) should maintain a constancy of 1 % relative to baseline. The focus of this study is to determine if statistical process control (SPC) methodology using process control charts (PCC) of steering coil currents (SCC) can detect changes in beam uniformity prior to exceeding the 1% constancy criteria.

METHODS

SCCs for the transverse and radial planes are adjusted such that a reproducibly useful photon or electron beam is available. Transverse and radial - positioning and angle SCC are routinely documented in the Morning Check file during daily warm-up. The 6 MV beam values for our linac were analyzed using average and range (Xbar/R) PCC. Using this data as a baseline, an experiment was performed in which each SCC was changed from its mean value (steps of 0.01 or 0.02 Ampere) while holding the other SCC constant. The effect on beam uniformity was measured using a beam scanning system. These experimental SCC values were plotted in the PCC to determine if they would exceed the predetermined limits.

RESULTS

The change in SCC required to exceed the 1% constancy criteria was detected by the PCC for 3 out of the 4 steering coils. The reliability of the result in the one coil not detected (transverse position coil) is questionable because the SCC slowly drifted during the experiment (0.05 A) regardless of the servo control setting.

CONCLUSIONS

X-bar/R charts of SCC can detect exceptional variation prior to exceeding the beam uniformity criteria set forth in AAPM TG-142. The high level of PCC sensitivity to change may result in an alarm when in fact minimal change in beam uniformity has occurred. Further study is needed to determine if a combination of individual SCC alarms would reduce the false positive rate for beam uniformity intervention. This project was supoorted by a grant from Varian Medical Systems, Inc.

SU-E-T-205: MLC Predictive Maintenance Using Statistical Process Control Analysis

PURPOSE

MLC failure increases accelerator downtime and negatively affects the clinic treatment delivery schedule. This study investigates the use of Statistical Process Control (SPC), a modern quality control methodology, to retrospectively evaluate MLC performance data thereby predicting the impending failure of individual MLC leaves.

METHODS

SPC, a methodology which detects exceptional variability in a process, was used to analyze MLC leaf velocity data. A MLC velocity test is performed weekly on all leaves during morning QA. The leaves sweep 15 cm across the radiation field with the gantry pointing down. The leaf speed is analyzed from the generated dynalog file using quality assurance software. MLC leaf speeds in which a known motor failure occurred (8) and those in which no motor replacement was performed (11) were retrospectively evaluated for a 71 week period. SPC individual and moving range (I/MR) charts were used in the analysis. The I/MR chart limits were calculated using the first twenty weeks of data and set at 3 standard deviations from the mean.

RESULTS

The MLCs in which a motor failure occurred followed two general trends: (a) no data indicating a change in leaf speed prior to failure (5 of 8) and (b) a series of data points exceeding the limit prior to motor failure (3 of 8). I/MR charts for a high percentage (8 of 11) of the non-replaced MLC motors indicated that only a single point exceeded the limit. These single point excesses were deemed false positives.

CONCLUSIONS

SPC analysis using MLC performance data may be helpful in detecting a significant percentage of impending failures of MLC motors. The ability to detect MLC failure may depend on the method of failure (i.e. gradual or catastrophic). Further study is needed to determine if increasing the sampling frequency could increase reliability. Project was support by a grant from Varian Medical Systems, Inc.