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BackgroundUnscheduled accelerator downtime can negatively impact the quality of life of patients during their struggle against cancer. Currently digital data accumulated in the accelerator system is not being exploited in a systematic manner to assist in more efficient deployment of service engineering resources. The purpose of this study is to develop an effective process for detecting unexpected deviations in accelerator system operating parameters and/or performance that predicts component failure or system dysfunction and allows maintenance to be performed prior to the actuation of interlocks.MethodsThe proposed predictive maintenance (PdM) model is as follows: 1) deliver a daily quality assurance (QA) treatment; 2) automatically transfer and interrogate the resulting log files; 3) once baselines are established, subject daily operating and performance values to statistical process control (SPC) analysis; 4) determine if any alarms have been triggered; and 5) alert facility and system service engineers. A robust volumetric modulated arc QA treatment is delivered to establish mean operating values and perform continuous sampling and monitoring using SPC methodology. Chart limits are calculated using a hybrid technique that includes the use of the standard SPC 3σ limits and an empirical factor based on the parameter/system specification.ResultsThere are 7 accelerators currently under active surveillance. Currently 45 parameters plus each MLC leaf (120) are analyzed using Individual and Moving Range (I/MR) charts. The initial warning and alarm rule is as follows: warning (2 out of 3 consecutive values ≥ 2σ hybrid) and alarm (2 out of 3 consecutive values or 3 out of 5 consecutive values ≥ 3σ hybrid). A customized graphical user interface provides a means to review the SPC charts for each parameter and a visual color code to alert the reviewer of parameter status. Forty-five synthetic errors/changes were introduced to test the effectiveness of our initial chart limits. Forty-three of the forty-five errors (95.6 %) were detected in either the I or MR chart for each of the subsystems monitored.ConclusionOur PdM model shows promise in providing a means for reducing unscheduled downtime. Long term monitoring will be required to establish the effectiveness of the model.

BackgroundThis study seeks to increase clinical operational efficiency and accelerator beam consistency by retrospectively investigating the application of statistical process control (SPC) to linear accelerator beam steering parameters to determine the utility of such a methodology in detecting changes prior to equipment failure (interlocks actuated).MethodsSteering coil currents (SCC) for the transverse and radial planes are set such that a reproducibly useful photon or electron beam is available. SCC are sampled and stored in the control console computer each day during the morning warm-up. The transverse and radial - positioning and angle SCC for photon beam energies were evaluated using average and range (Xbar-R) process control charts (PCC). The weekly average and range values (subgroup n = 5) for each steering coil were used to develop the PCC. SCC from September 2009 (annual calibration) until two weeks following a beam steering failure in June 2010 were evaluated. PCC limits were calculated using the first twenty subgroups. Appropriate action limits were developed using conventional SPC guidelines.ResultsPCC high-alarm action limit was set at 6 standard deviations from the mean. A value exceeding this limit would require beam scanning and evaluation by the physicist and engineer. Two low alarms were used to indicate negative trends. Alarms received following establishment of limits (week 20) are indicative of a non-random cause for deviation (Xbar chart) and/or an uncontrolled process (R chart). Transverse angle SCC for 6 MV and 15 MV indicated a high-alarm 90 and 108 days prior to equipment failure respectively. A downward trend in this parameter continued, with high-alarm, until failure. Transverse position and radial angle SCC for 6 and 15 MV indicated low-alarms starting as early as 124 and 116 days prior to failure, respectively.ConclusionRadiotherapy clinical efficiency and accelerator beam consistency may be improved by instituting SPC methods to monitor the beam steering process and detect abnormal changes prior to equipment failure.PACS numbers: 87.55n, 87.55qr, 87.56bd

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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.

The use of image‐based 3D treatment planning has significantly increased the complexity of commercially available treatment‐planning systems (TPSs). Medical physicists have traditionally focused their efforts on understanding the calculation algorithm; this is no longer possible. A quality assurance (QA) program for our 3D treatment‐planning system (ADAC Pinnacle3) is presented. The program is consistent with the American Association of Physicists in Medicine Task Group 53 guidelines and balances the cost‐versus‐benefit equation confronted by the clinical physicist in a community cancer center environment. Fundamental reproducibility tests are presented as required for a community cancer center environment using conventional and 3D treatment planning. A series of nondosimetric tests, including digitizer accuracy, image acquisition and display, and hardcopy output, is presented. Dosimetric tests include verification of monitor units (MUs), standard isodoses, and clinical cases. The tests are outlined for the Pinnacle3 TPS but can be generalized to any TPS currently in use. The program tested accuracy and constancy through several hardware and software upgrades to our TPS. This paper gives valuable guidance and insight to other physicists attempting to approach TPS QA at fundamental and practical levels.PACS numbers: 87.53.Tf, 87.53.Xd

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.

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