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

Able, C; Hampton, C.J.; Baydush, Alan H.

doi:10.1118/1.4735268

Cited by 5 publications

(7 citation statements)

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“…SPC techniques can offer a solution in identifying variation within a process [4][5][6][7][8][19][20][21][22][23]25,28,[34][35][36][37][38][39][40][41]. Statistical and graphical analysis using SPC metrics revealed that each performance parameter had its own operating window in which operational consistency of linacs was maintained.…”

Section: Discussionmentioning

confidence: 99%

Operational consistency of medical linear accelerators manufactured and commissioned in series

Nguyen

Baydush

Ververs

et al. 2018

Technical Innovations & Patient Support in Radiation Oncolo

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The purpose of this study was to determine if medical linear accelerators (linac) produced by the same manufacturer exhibit operational consistency within their subsystems and components. Two linacs that were commissioned together and installed at the same facility were monitored. Each machine delivered a daily robust quality assurance (QA) irradiation. Linacs and their components operate consistently, but have different operational parameter levels even when produced by the same manufacturer and commissioned in series. These findings have implications on the feasibility of true clinical beam matching.

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Section: Discussionmentioning

confidence: 99%

Operational consistency of medical linear accelerators manufactured and commissioned in series

Nguyen

Baydush

Ververs

et al. 2018

Technical Innovations & Patient Support in Radiation Oncolo

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“…While I/MR charts are the most sensitive in identifying changes in parameters, our methods for calculating the chart limits and chart analysis rules will determine how effective we can predict when maintenance intervention is necessary. Our experience has shown that the use of traditional SPC chart limits (±3 standard deviation (±3 σ ) from the grand mean) can result in an unacceptable rate of false positive signals [ 5 , 6 ]. Using information on system specifications from the manufacturer, consulting the literature for recent studies on quality control of complex treatment delivery [ 16 , 17 ] (IMRT, VMAT, SBRT, etc.)…”

Section: Methodsmentioning

confidence: 99%

A model for preemptive maintenance of medical linear accelerators—predictive maintenance

et al. 2016

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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.

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“…Our hybrid method for calculating the chart limits and chart analysis rules will determine how effective we can predict when maintenance intervention is necessary. Our experience has shown that the use of traditional SPC chart limits (±3 standard deviation from the grand mean) can result in an unacceptable rate of false positive signals (1,2). Using information on system specifications from the manufacturer, consulting the literature for recent studies on quality control of complex treatment delivery (IMRT, VMAT, SBRT, etc) and white papers on quality assurance of linear accelerators (3) we introduce a hybrid approach to calculating the control chart limits that includes a factor (S p ) that fractionally increases the limits based on the operational parameter specification and/or performance criteria.…”

Section: Methodsmentioning

confidence: 99%

SU-E-T-144: Effective Analysis of VMAT QA Generated Trajectory Log Files for Medical Accelerator Predictive Maintenance

et al. 2014

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Purpose: To determine the effectiveness of SPC analysis for a model predictive maintenance process that uses accelerator generated parameter and performance data contained in trajectory log files. Methods: Each trajectory file is decoded and a total of 131 axes positions are recorded (collimator jaw position, gantry angle, each MLC, etc.). This raw data is processed and either axis positions are extracted at critical points during the delivery or positional change over time is used to determine axis velocity. The focus of our analysis is the accuracy, reproducibility and fidelity of each axis. A reference positional trace of the gantry and each MLC is used as a motion baseline for cross correlation (CC) analysis. A total of 494 parameters (482 MLC related) were analyzed using Individual and Moving Range (I/MR) charts. The chart limits were calculated using a hybrid technique that included the use of the standard 3σ limits and parameter/system specifications. Synthetic errors/changes were introduced to determine the initial effectiveness of I/MR charts in detecting relevant changes in operating parameters. The magnitude of the synthetic errors/changes was based on: TG‐142 and published analysis of VMAT delivery accuracy. Results: All errors introduced were detected. Synthetic positional errors of 2mm for collimator jaw and MLC carriage exceeded the chart limits. Gantry speed and each MLC speed are analyzed at two different points in the delivery. Simulated Gantry speed error (0.2 deg/sec) and MLC speed error (0.1 cm/sec) exceeded the speed chart limits. Gantry position error of 0.2 deg was detected by the CC maximum value charts. The MLC position error of 0.1 cm was detected by the CC maximum value location charts for every MLC. Conclusion: SPC I/MR evaluation of trajectory log file parameters may be effective in providing an early warning of performance degradation or component failure for medical accelerator systems.

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

Cited by 5 publications

References 0 publications

Operational consistency of medical linear accelerators manufactured and commissioned in series

Operational consistency of medical linear accelerators manufactured and commissioned in series

A model for preemptive maintenance of medical linear accelerators—predictive maintenance

SU-E-T-144: Effective Analysis of VMAT QA Generated Trajectory Log Files for Medical Accelerator Predictive Maintenance

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