Predictive quality assurance of a linear accelerator based on the machine performance check application using statistical process control and ARIMA forecast modeling. Issue 8 (15th June 2020)

• Record Type: Journal Article
• Authors: Puyati, Wayo; Khawne, Amnach; Barnes, Michael; Zwan, Benjamin; Greer, Peter; Fuangrod, Todsaporn
• Is Part Of: Journal of applied clinical medical physics. Volume 21:Issue 8(2020)
• Page Start: 73
• Page End: 82
• Publication Date: 2020-06-15
• Subjects: autoregressive integrated moving average forecast modeling -- machine performance check -- predictive quality assurance -- statistical process control; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.12917
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: 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 root‐mean‐square error, absolute error, and average accuracy rate for all MPC test parameters. Results: The accuracy of the predictive model is considered high (average root‐mean‐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.