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

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

doi:10.1118/1.4735265

Cited by 6 publications

(4 citation statements)

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“…The concept of predictive QA that allows preemptive maintenance based on SPC analysis has been studied for radiotherapy linacs. [14][15][16][17] Predictive QA testing would allow radiotherapy departments to be proactive in their maintenance by remedying faults before clinical tolerances are breached. In theory, this should provide for improved linac performance consistency and reduced unscheduled linac downtime that can be disruptive to departmental workflow.…”

Section: Binny Et Al Applied Spc To Analyze the Qa Output Variation mentioning

confidence: 99%

“…In theory, this should provide for improved linac performance consistency and reduced unscheduled linac downtime that can be disruptive to departmental workflow. Previous predictive QA studies have either been based on readouts from the linac itself [15][16][17] and hence lack independence or have been based on film measurements, 14 which are impractical on a daily basis.…”

Section: Binny Et Al Applied Spc To Analyze the Qa Output Variation mentioning

confidence: 99%

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Predictive quality assurance of a linear accelerator based on the machine performance check application using statistical process control and ARIMA forecast modeling

Khawne¹,

Barnes

Zwan

et al. 2020

J Applied Clin Med Phys

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Purpose: A predictive linac quality assurance system based on the output of the Machine Performance Check (MPC) application was developed using statistical process control and autoregressive integrated moving average forecast modeling. The aim of this study is to demonstrate the feasibility of predictive quality assurance based on MPC tests that allow proactive preventative maintenance procedures to be carried out to better ensure optimal linac performance and minimize downtime. Method and Materials: Daily MPC data were acquired for a total of 490 measurements. The initial 85% of data were used in prediction model learning with the autoregressive integrated moving average technique and in calculating upper and lower control limits for statistical process control analysis. The remaining 15% of data were used in testing the accuracy of the predictions of the proposed system. Two types of prediction were studied, namely, one-step-ahead values for predicting the next day's quality assurance results and six-step-ahead values for predicting up to a week ahead. Results that fall within the upper and lower control limits indicate a normal stage of machine performance, while the tolerance, determined from AAPM TG-142, is the clinically required performance. The gap between the control limits and the clinical tolerances (as the warning stage) provides a window of opportunity for rectifying linac performance issues before they become clinically significant. The accuracy of the predictive model was tested using the rootmean-square error, absolute error, and average accuracy rate for all MPC test parameters. Results: The accuracy of the predictive model is considered high (average rootmean-square error and absolute error for all parameters of less than 0.05). The average accuracy rate for indicating the normal/warning stages was higher than 85.00%. Conclusion: Predictive quality assurance with the MPC will allow preventative maintenance, which could lead to improved linac performance and a reduction in unscheduled linac downtime.

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Section: Binny Et Al Applied Spc To Analyze the Qa Output Variation mentioning

confidence: 99%

Section: Binny Et Al Applied Spc To Analyze the Qa Output Variation mentioning

confidence: 99%

Predictive quality assurance of a linear accelerator based on the machine performance check application using statistical process control and ARIMA forecast modeling

Khawne¹,

Barnes

Zwan

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

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“…Many of the approaches tested relied on tracking individual parameters related leaf behaviour to detect abnormalities. Some parameters investigated were total distance travelled [4] , leaf velocity [5] or frequency of positional discrepancies [6] . The values for these were recorded either via trajectory logs [4] , [6] or with additional testing during daily QA [3] .…”

Section: Introductionmentioning

confidence: 99%

Multivariate log file analysis for multi-leaf collimator failure prediction in radiotherapy delivery

Wojtasik

Bolt

Clark

et al. 2020

Physics and Imaging in Radiation Oncology

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Background and Purpose: Motor failure in multi-leaf collimators (MLC) is a common reason for unscheduled accelerator maintenance, disrupting the workflow of a radiotherapy treatment centre. Predicting MLC replacement needs ahead of time would allow for proactive maintenance scheduling, reducing the impact MLC replacement has on treatment workflow. We propose a multivariate approach to analysis of trajectory log data, which can be used to predict upcoming MLC replacement needs. Materials and methods: Trajectory log files from two accelerators, spanning six and seven months respectively, have been collected and analysed. The average error in each of the parameters for each log file was calculated and used for further analysis. A performance index (PI) was generated by applying moving window principal component analysis to the prepared data. Drops in the PI were thought to indicate an upcoming MLC replacement requirement; therefore, PI was tracked with exponentially weighted moving average (EWMA) control charts complete with a lower control limit. Results: The best compromise of fault detection and minimising false alarm rate was achieved using a weighting parameter (λ) of 0.05 and a control limit based on three standard deviations and an 80 data point window. The approach identified eight out of thirteen logged MLC replacements, one to three working days in advance whilst, on average, raising a false alarm, on average, 1.1 times a month. Conclusions: This approach to analysing trajectory log data has been shown to enable prediction of certain upcoming MLC failures, albeit at a cost of false alarms.

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“…Subsequently, this method was also applied to the QA of linear accelerator [13,14], tomotherapy [15], proton accelerator [16] and intensity modulated radiotherapy plans [17,18,19]. In 2013, Sanghangthum et al [20] proposed to use SPC to establish customized tolerance and action limits.…”

Section: Introductionmentioning

confidence: 99%

Study on the Necessity of Establishing Customized Limits for the Daily QA Program Performed With Daily QA3

Jin¹,

Xing²

2020

Preprint

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Background: Due to no corresponding action limits for some new daily QA items , limits of the monthly or annual QA procedures have to be used for daily QA program at present. The general purpose of this paper is to discuss whether it is feasible to do so.Methods: 3 groups daily quality control process during 2017-2019 performed with Daily QA3 were analyzed using SPC. In Calculation of capability indices for output, daily QA tolerance ±3% in the TG142 was used as specification limit, while for flatness and symmetry, annual QA tolerance ±1% was used. An appropriate number of data points were analyzed , capability indices (Cp, Cpk, Cpm and Cpmk) were calculated, and customized tolerance and action limits were established for each process. Meanwhile, the locations of the UCL、CL、LCL in I-MR charts of daily QA performed by 5 therapists in turns were compared with that of daily QA performed by only one physicist during the same period . Results: Process capability indices of output were all ≥1, some were even up to 3-4. While, for symmetry, most of them failed to meet the requirements. For the same daily QA item, the calculated customized limit of different quality control processes were quite different. For the daily QA performed with only one physicist, the range of UCL and LCL was smaller or the central line was closer to the target value 0 in I-MR charts.Conclusions: It is necessary to establish customized limits for daily QA program performed with Daily QA3, because customized limits and improved operation constancy both can improve the daily quality control level.

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

Cited by 6 publications

References 0 publications

Predictive quality assurance of a linear accelerator based on the machine performance check application using statistical process control and ARIMA forecast modeling

Predictive quality assurance of a linear accelerator based on the machine performance check application using statistical process control and ARIMA forecast modeling

Multivariate log file analysis for multi-leaf collimator failure prediction in radiotherapy delivery

Study on the Necessity of Establishing Customized Limits for the Daily QA Program Performed With Daily QA3

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