Evaluation of the truebeam machine performance check (<scp>MPC</scp>): <scp>OBI</scp> X‐ray tube alignment procedure

Barnes, Michael P.; Pomare, Dennis; Menk, F. W.; Moraro, Buiron; Greer, Peter B.

doi:10.1002/acm2.12445

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…In King's study it was found that over a 3-year period mean pixel variations were between 0.29 and 0.6 % and that more than 99% of all pixels showed variations less than 1 %. Also the MPC studies of 29 These events were not accounted for in this study.…”

Section: Discussionmentioning

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

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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: Discussionmentioning

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

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“…The alignment of the On-Board Imager (OBI) X-ray tube is important for ensuring imaging to treatment isocenter coincidence, which in turn is important for accurate Image Guided Radiation Therapy (IGRT). Barnes et al (2018) (10) evaluated OBI X-Ray tube alignment procedure in relation to MPC. The authors discussed the new procedure and examined the clinical significance of X-Ray tube misalignment.…”

Section: Discussionmentioning

confidence: 99%

Evaluation and validation of the machine performance check application for TrueBeam 2.5 Linac

Nigam¹

2020

IJST

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Aim: To evaluate the Machine Performance Check (MPC) 1.1 application for TrueBeam 2.5 and to establish MPC application as a comprehensive daily QA tool after verification. Methodology: An automated daily Quality check application MPC using IsoCal Phantom provided by Varian Medical System (VMS) to perform the essential tests such as beam consistency that is dosimetric performance and mechanical check were performed on TrueBeam. The data was collected over a period of four months (84 days). MPC acquired data consists of a series of generation of images using MV and kV at various gantry, collimator, and couch positions. The collected data is analyzed for daily reproducibility as well as comparison with ion chamber measurements. Findings: The results showed that the reproducibility of MPC was less than the tolerance values for the respective mechanical parameters. The MPC measured isocenter with mean value of 0.35 mm is within tolerance. The Beam Consistency showed that over a period of four months, the mean value of output measurement of 6 MV photon beam as measured by the MPC and Farmer Type Chamber FC65-G differ by less than 1%. It is evident that the MPC proved to be fast and easy to use QA tool for daily checking of machine performance. But it is evident from present study that, inter comparison with a Farmer Type chamber is needed, if the MPC has to replace the other QA tools.

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“…The QC tests are highly automated, and the user only should set up the IsoCal phantom and bracket on the treatment couch, then beam on for the predetermined energy. MPC application has been evaluated as a Linac daily QC tool by some investigators 10–14 . In this study, the records of 24 QC items based on MPC were collected at our institution using Varian Edge for more than 3 years, from August 2017 to October 2020.…”

Section: Methodsmentioning

confidence: 99%

“…MPC application has been evaluated as a Linac daily QC tool by some investigators. 10 , 11 , 12 , 13 , 14 In this study, the records of 24 QC items based on MPC were collected at our institution using Varian Edge for more than 3 years, from August 2017 to October 2020. A total of 867 data were collected to predict the record for the next 5 days.…”

Section: Methodsmentioning

confidence: 99%

Predicting machine's performance record using the stacked long short‐term memory (LSTM) neural networks

Liu

Wei

et al. 2022

J Applied Clin Med Phys

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Purpose The record of daily quality control (QC) items shows machine performance patterns and potentially provides warning messages for preventive actions. This study developed a neural network model that could predict the record and trend of data variations quantitively. Methods and materials The record of 24 QC items for a radiotherapy machine was investigated in our institute. The QC records were collected daily for 3 years. The stacked long short‐term memory (LSTM) model was used to develop the neural network model. A total of 867 records were collected to predict the record for the next 5 days. To compare the stacked LSTM, the autoregressive integrated moving average model (ARIMA) was developed on the same data set. The accuracy of the model was quantified by the mean absolute error (MAE), root‐mean‐square error (RMSE), and coefficient of determination (R2). To validate the robustness of the model, the record of four QC items was collected for another radiotherapy machine, which was input into the stacked LSTM model without changing any hyperparameters and ARIMA model. Results The mean MAE, RMSE, and0.28emR2${\rm{\;}}{R^2}$ with 24 QC items were 0.013, 0.020, and 0.853 in LSTM, while 0.021, 0.030, and 0.618 in ARIMA, respectively. The results showed that the stacked LSTM outperforms the ARIMA. Moreover, the mean MAE, RMSE, and0.28emR2${\rm{\;}}{R^2}$ with four QC items were 0.102, 0.151, and 0.770 in LSTM, while 0.162, 0.375, and 0.550 in ARIMA, respectively. Conclusions In this study, the stacked LSTM model can accurately predict the record and trend of QC items. Moreover, the stacked LSTM model is robust when applied to another radiotherapy machine. Predicting future performance record will foresee possible machine failure, allowing early machine maintenance and reducing unscheduled machine downtime.

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Evaluation of the truebeam machine performance check (MPC): OBI X‐ray tube alignment procedure

Cited by 13 publications

References 11 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

Evaluation and validation of the machine performance check application for TrueBeam 2.5 Linac

Predicting machine's performance record using the stacked long short‐term memory (LSTM) neural networks

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