Statistical process control for electron beam monitoring

López-Tarjuelo, Juan; Luquero-Llopis, Naika; García-Mollá, Rafael; Quirós-Higueras, Juan David; Bouché-Babiloni, Ana; Juan-Senabre, X.J.; Marco-Blancas, N. de; Ferrer-Albiach, Carlos; Santos-Serra, Agustín

doi:10.1016/j.ejmp.2015.05.006

Cited by 13 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One such set of techniques is statistical process control (SPC) techniques, which are commonly used to detect changes in the performance of manufacturing processes and have been applied successfully in several process-intensive medical settings. 8 For radiotherapy, SPC has been used to monitor linear accelerator beam steering, 9 couch motion, 10 electron-beam monitoring, 11 multileaf collimator performance, 12 fiducial registration error, 13 and patient-specific QA, 14 among other applications. SPC has been recently been incorporated into some commercial machine QA tools.…”

Section: Introductionmentioning

confidence: 99%

8+ Year Performance of the Gamma Knife Perfexion/Icon Patient Positioning System and Possibilities for Preemptive Fault Detection Using Statistical Process Control

et al. 2021

View full text Add to dashboard Cite

Background: The large fractional doses, steep dose gradients, and small targets found in intracranial radiosurgery require extremely low beam delivery uncertainty. In the case of Gamma Knife radiosurgery (GKRS), this includes minimizing patient positioning system (PPS) positioning uncertainty. Existing QA techniques are recipe based, and feature point in time pass/fail tolerances. However, modern treatment machines, including the Gamma Knife Perfexion/Icon systems, record extensive internal data in treatment logs. These data can be analyzed through statistical process control (SPC) methods which are designed to detect changes in process behavior. The purpose of this study was to characterize the long-term (8+ year) performance of a Perfexion/Icon unit and use SPC methods to determine if performance changes could be detected at levels lower than existing QA and internal manufacturer performance tolerances. Methods: In-house software was developed to parse Perfexion/Icon log-files and store relevant information on shot delivery in a relational database. A last-in, first-out (LIFO) queuing algorithm was created to heuristically match messages associated with a given delivered shot. Filtering criteria were developed to filter QA and uncompleted shots. The resulting matched shots were extracted. Achieved versus planned PPS position was determined for each PPS motor as well as for the vector magnitude difference in PPS position. Exponentially weighted moving average (EWMA) control charts were plotted to determine when process behavior changed over time. Results: 53833 shots were delivered over an 8+ year span in the study. The mean vector magnitude PPS difference was 32.7 µm, with 97.5% of all shots within 70.1 µm. Several changes in PPS positioning behavior were observed over time, corresponding with control system faults on several occasions requiring PPS recalibration. EWMA control charts clearly demonstrate that these faults could be identified and possibly predicted as many as 3 years before there were faults beyond control system tolerance. Conclusion:The PPS of Gamma Knife Perfexion/Icon systems has extremely low positioning uncertainties. EWMA control chart method can be utilized to track PPS performance over time and can potentially detect changes in performance that may indicate a component requiring maintenance. This would allow planned service visits to mitigate problems and prevent unplanned downtime.

show abstract

Section: Introductionmentioning

confidence: 99%

8+ Year Performance of the Gamma Knife Perfexion/Icon Patient Positioning System and Possibilities for Preemptive Fault Detection Using Statistical Process Control

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The stability of their output is also better, with day-to-day variations equal to or less than 0.4 %, i.e. 1 SD [ 26 ]. This is one order of magnitude less than the output stability of mobile linacs [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

Implementation of an intraoperative electron radiotherapy in vivo dosimetry program

et al. 2016

Self Cite

View full text Add to dashboard Cite

BackgroundIntraoperative electron radiotherapy (IOERT) is a highly selective radiotherapy technique which aims to treat restricted anatomic volumes during oncological surgery and is now the subject of intense re-evaluation. In vivo dosimetry has been recommended for IOERT and has been identified as a risk-reduction intervention in the context of an IOERT risk analysis. Despite reports of fruitful experiences, information about in vivo dosimetry in intraoperative radiotherapy is somewhat scarce. Therefore, the aim of this paper is to report our experience in developing a program of in vivo dosimetry for IOERT, from both multidisciplinary and practical approaches, in a consistent patient series. We also report several current weaknesses.MethodsReinforced TN-502RDM-H mobile metal oxide semiconductor field effect transistors (MOSFETs) and Gafchromic MD-55-2 films were used as a redundant in vivo treatment verification system with an Elekta Precise fixed linear accelerator for calibrations and treatments. In vivo dosimetry was performed in 45 patients in cases involving primary tumors or relapses. The most frequent primary tumors were breast (37 %) and colorectal (29 %), and local recurrences among relapses was 83 %. We made 50 attempts to measure with MOSFETs and 48 attempts to measure with films in the treatment zones. The surgical team placed both detectors with supervision from the radiation oncologist and following their instructions.ResultsThe program was considered an overall success by the different professionals involved. The absorbed doses measured with MOSFETs and films were 93.8 ± 6.7 % and 97.9 ± 9.0 % (mean ± SD) respectively using a scale in which 90 % is the prescribed dose and 100 % is the maximum absorbed dose delivered by the beam. However, in 10 % of cases we experienced dosimetric problems due to detector misalignment, a situation which might be avoided with additional checks. The useful MOSFET lifetime length and the film sterilization procedure should also be controlled.ConclusionsIt is feasible to establish an in vivo dosimetry program for a wide set of locations treated with IOERT using a multidisciplinary approach according to the skills of the professionals present and the detectors used; oncological surgeons’ commitment is key to success in this context. Films are more unstable and show higher uncertainty than MOSFETs but are cheaper and are useful and convenient if real-time treatment monitoring is not necessary.

show abstract

“…10 Meanwhile, López-Tarjuelo et al adopted SPC in the daily quality control of linac electron beams. 11 Fuangrod et al applied SPC in constructing a clinically significant threshold of a real-time treatment verification system. 12 Recently, SPC has been used with MPC data in another study by Binny et al 13 In that study, SPC analysis was conducted for MPC data across six TrueBeam linacs for 12 months in an attempt to determine MPC tolerances.…”

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

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.

show abstract

Statistical process control for electron beam monitoring

Cited by 13 publications

References 18 publications

8+ Year Performance of the Gamma Knife Perfexion/Icon Patient Positioning System and Possibilities for Preemptive Fault Detection Using Statistical Process Control

8+ Year Performance of the Gamma Knife Perfexion/Icon Patient Positioning System and Possibilities for Preemptive Fault Detection Using Statistical Process Control

Implementation of an intraoperative electron radiotherapy in vivo dosimetry program

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

Contact Info

Product

Resources

About