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

Able, C; Baydush, Alan H.; Nguyen, Callistus; Gersh, J; Ndlovu, A; Rebo, I; Booth, Jeremy; Pérez, Manuel Asensi; Sintay, B; Munley, Michael T.

doi:10.1186/s13014-016-0602-1

Cited by 21 publications

(23 citation statements)

References 13 publications

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“…Compared to the daily QA plan approach proposed by Able et al ,. our approach is to use trajectory logs originated from daily clinically delivered plans to predict failures.…”

Section: Discussionmentioning

confidence: 99%

“…Compared to the daily QA plan approach proposed by Able et al 1 , our approach is to use trajectory logs originated from daily clinically delivered plans to predict failures. A limitation of our approach as discussed above is its inability to predict failures from full-width leaves.…”

Section: Discussionmentioning

confidence: 99%

“…The use of LINAC‐generated logs for MLC maintenance has been discussed by Able et al ,. where trajectory logs generated from a daily QA plan were associated with service reports for statistical process control.…”

Section: Introductionmentioning

confidence: 99%

“…Presently, four sets of leaf positions are available for quantifying MLC performance [1][2][3][4][5][6][7][8][9][10][11] : (a) DICOM RT-plan's positions generated from treatment planning system; (b) EPID-acquired positions; (c) on-fly generated 4D Integrated Treatment Console (4DITC)-ordered positions; (d) actual positions read from the motor's encoder. Positions (c) and (d) are one-on-one recorded in LINAC-generated logs.…”

Section: Introductionmentioning

confidence: 99%

“…The use of LINAC-generated logs for MLC maintenance has been discussed by Able et al 1 , where trajectory logs generated from a daily QA plan were associated with service reports for statistical process control. Rather than using logs from the daily QA plan, this study utilized logs originated from daily clinical delivery for MLC preventive maintenance, providing richer historical MLC performance data for analysis.…”

Section: Introductionmentioning

confidence: 99%

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Utilizing historical MLC performance data from trajectory logs and service reports to establish a proactive maintenance model for minimizing treatment disruptions

Zhang

Kanchaveli

et al. 2019

Medical Physics

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Purpose This study proposes a proactive maintenance model utilizing historical Multileaf collimator (MLC) performance data to predict potential MLC dysfunctions, promote preemptive maintenance and thereby reduce treatment disruptions. Methods MLC failures were assumed to correlate with MLC performance quantitation from trajectory logs. A cohort of data from service reports and trajectory logs was used to establish a model for predicting MLC dysfunctions. Specifically, the service reports logged by our in‐house engineers recorded failure status, including service date, service reason and actions taken, while trajectory logs recorded the ordered/actual leaf positions in 20‐ms intervals. Leaf performance from trajectory logs was quantified, where an event was defined as detecting a leaf's position deviation ≥a mm. Three a values, 0.05, 0.1, and 0.5 mm, were used as candidates to determine the appropriate threshold for deviation event quantitation. Logged MLC failures from service reports were retrieved and classified into two categories based on the patterns of their deviation events calculated from trajectory logs: (a) failures with continuous deviations: deviation events lasted several days before failure, and (b) failures with a burst of deviations: deviation events only lasted 1 or 2 days and then MLC failed suddenly. The proposed proactive model focused on the failures with continuous deviations since abnormal trends in their deviation events lasted couple of days, allowing preventive maintenance. The model was predefined with three parameters (x, y, z): if a leaf scored ≥x deviation events per day in any y days within up to a z‐day window, the leaf was marked as a “potential failure.” The distributions of the deviation events as functions of time (days or weeks) and leaves using the found a‐value were then associated with logged failures to find model parameters. In a retrospective demonstration, a total of 28 logged failures with continuous deviations and 66 397 trajectory logs from two TBs' 3‐yr records were used to determine the model parameters (x, y, z). The established model was then applied to a third TB for validation. Results Deviation event threshold, a, was determined to be 0.1 mm, and the resulting model parameters were (x = 20, y = 6, z = 10). When validating the third TB's 3‐yr record with 12 logged continuous deviation failures, the model predicted 16 failures: seven were confirmed from the records with a hit rate of 58.3%, while nine were not; further investigation of each unconfirmed failure convinced that some could be actual failures, but somehow not recorded. Conclusion The model offers an addition to preemptive maintenance for reducing treatment disruptions.

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“…Compared to the daily QA plan approach proposed by Able et al ,. our approach is to use trajectory logs originated from daily clinically delivered plans to predict failures.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Utilizing historical MLC performance data from trajectory logs and service reports to establish a proactive maintenance model for minimizing treatment disruptions

Zhang

Kanchaveli

et al. 2019

Medical Physics

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Formulation of objective indices to quantify machine failure risk analysis for interruptions in radiotherapy

Kawahara

Nakano

Saito

et al. 2020

J Applied Clin Med Phys

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Objectives: To evaluate the effect of interruption in radiotherapy due to machine failure in patients and medical institutions using machine failure risk analysis (MFRA). Material and methods: The risk of machine failure during treatment is assigned to three scores (biological effect, B; occurrence, O; and cost of labor and repair parts, C) for each type of machine failure. The biological patient risk (BPR) and the economic institution risk (EIR) are calculated as the product of B and O (B Â O) and C and O (C Â O), respectively. The MFRA is performed in two linear accelerators (linacs).Result: The multileaf collimator (MLC) fault has the highest BPR and second highest EIR. In particular, TrueBeam has a higher BPR and EIR for MLC failures. The total EIR in TrueBeam was significantly higher than that in Clinac iX. The minor interlock had the second highest BPR, whereas a smaller EIR. Meanwhile, the EIR for the LaserGuard fault was the highest, and that for the monitor chamber fault was the second highest. These machine failures occurred in TrueBeam. The BPR and EIR should be evaluated for each linac. Further, the sensitivity of the BPR, it decreased with higher T 1=2 and α/β values. No relative difference is observed in the BPR for each machine failure when T 1=2 and α/β were varied. Conclusion:The risk faced by patients and institutions in machine failure may be reduced using MFRA.Advances in knowledge: For clinical radiotherapy, interruption can occur from unscheduled downtime with machine failures. Interruption causes sublethal damage repair. The current study evaluated the effect of interruption in radiotherapy owing to machine failure on patients and medical institutions using a new method, that is, machine failure risk analysis.

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Failure modes and downtime of radiotherapy LINACs and multileaf collimators in Indonesia

Pawiro

Kasim

et al. 2022

J Applied Clin Med Phys

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Background and purpose The lack of equitable access to radiotherapy (RA) linear accelerators (LINACs) is a substantial barrier to cancer care in low‐ and middle‐income countries (LMICs). These nations are expected to bear up to 75% of cancer‐related deaths globally by 2030. State‐of‐the‐art LINACs in LMICs experience major issues in terms of robustness, with mechanical and electrical breakdowns resulting in downtimes ranging from days to months. While existing research has identified the higher failure frequency and downtimes between LMICs (Nigeria, Botswana) compared to high‐income countries (HICs, the UK), there has been a need for additional data and study particularly relating to multileaf collimators (MLCs). Materials and methods This study presents for the first time the analysis of data gathered through a dedicated survey and workshop including participants from 14 Indonesian hospitals, representing a total of 19 LINACs. We show the pathways to failure of radiotherapy LINACs and frequency of breakdowns with a focus on the MLC subsystem. Results This dataset shows that LINACs throughout Indonesia are out of operation for seven times longer than HICs, and the mean time between failures of a LINAC in Indonesia is 341.58 h or about 14 days. Furthermore, of the LINACs with an MLC fitted, % of all mechanical faults are due to the MLC, and % of cases requiring a replacement component are related to the MLC. Conclusion These results highlight the pressing need to improve robustness of RT technology for use in LMICs, highlighting the MLC as a particularly problematic component. This work motivates a reassessment of the current generation of RT LINACs and demonstrates the need for dedicated efforts toward a future where cancer treatment technology is robust for use in all environments where it is needed.

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

Cited by 21 publications

References 13 publications

Utilizing historical MLC performance data from trajectory logs and service reports to establish a proactive maintenance model for minimizing treatment disruptions

Utilizing historical MLC performance data from trajectory logs and service reports to establish a proactive maintenance model for minimizing treatment disruptions

Formulation of objective indices to quantify machine failure risk analysis for interruptions in radiotherapy

Failure modes and downtime of radiotherapy LINACs and multileaf collimators in Indonesia

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