Catching errors with patient-specific pretreatment machine log file analysis

Rangaraj, D; Zhu, Mingyao; Yang, Deshan; Palaniswaamy, Geethpriya; Yaddanapudi, S; Wooten, O.; Brame, S.; Mutic, Sasa

doi:10.1016/j.prro.2012.05.002

Cited by 52 publications

(53 citation statements)

References 16 publications

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“…Log file analysis has also been proposed to improve the efficiency of patient-specific quality assurance [5][6][7][8] and provide insight into machine parameters not possible with phantombased measurements. [9][10][11] In addition, log file analysis can be used to assess the actual/relative delivered dose reconstructed on the patient anatomy using the patient's original CT image set 10,12 or the on-treatment cone-beam CT image set.…”

Section: Introductionmentioning

confidence: 99%

The use of log file analysis within VMAT audits

McGarry

Agnew

Hussein

et al. 2016

BJR

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Objective: This work investigated the delivery accuracy of different Varian linear accelerator models using log file-derived multileaf collimator (MLC) root mean square (RMS) values. Methods: Seven centres independently created a plan on the same virtual phantom using their own planning system, and the log files were analyzed following delivery of the plan in each centre to assess MLC positioning accuracy. A single standard plan was also delivered by the seven centres to remove variations in complexity, and the log files were analyzed for Varian TrueBeams and Clinacs (2300IX or 2100CD models). Results: Varian TrueBeam accelerators had better MLC positioning accuracy (,1.0 mm) than the 2300IX (,2.5 mm) following delivery of the plans created by each centre and also the standard plan. In one case, log files provided evidence that reduced delivery accuracy was not associated with the linear accelerator model but was due to planning issues. Conclusion: Log files are useful in identifying differences between linear accelerator models and isolate errors during end-to-end testing in volumetric-modulated arc therapy (VMAT) audits. Log file analysis can rapidly eliminate the machine delivery from the process and divert attention with confidence to other aspects. Advances in knowledge: Log file evaluation was shown to be an effective method to rapidly verify satisfactory treatment delivery when a dosimetric evaluation fails during end-to-end dosimetry audits. MLC RMS values for Varian TrueBeams were shown to be much smaller than those for Varian Clinacs for VMAT deliveries.

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Section: Introductionmentioning

confidence: 99%

The use of log file analysis within VMAT audits

McGarry

Agnew

Hussein

et al. 2016

BJR

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“…Its primary application is instead to catch certain rare errors such as the junctions of closed MLC leaf pairs left inside the beam field defined by the X and Y jaws in Pinnacle step‐and‐shoot IMRT plans 11. Compared to measurement‐based QA, the results of 3DFC QA are less independent because the beam parameters in the deliveries were measured by the treatment machines instead by independent measurement device.…”

Section: Discussionmentioning

confidence: 99%

“…In comparison to conventional measurement‐based QA, QA using log files offers various advantages including sampling higher spatial and temporal resolutions, not requiring measurement devices or phantoms, providing QA for fractional deliveries to patients, and being readily automated 5, 6. Performing IMRT QA using log files has been claimed to be more effective and efficient than, and complementary to, physical dose measurement‐based QA 7, 8, 9, 10, 11, 12. A major, ongoing debate in the medical physics community is whether IMRT QA using log files can replace conventional measurement‐based methods 5…”

Section: Introductionmentioning

confidence: 99%

“…Beam 2D fluence can also be digitally and proximately reconstructed from treatment plan parameters or LINAC machine log files by integrating across a beam aperture multiplied by the per‐segment beam MU 10, 26, 27, 28. At the authors’ institution, 2D beam fluences digitally reconstructed from the DICOM plans and treatment delivery log files have enabled detection of many errors for IMRT plans, including human operating mistakes (resulting in wrong plans, wrong beams, or wrong beam parameters), flawed and suboptimal treatment plans (containing undeliverable or incorrect machine parameters), data transfer problems (resulting from unintended parameter changes), and other minor false positive errors 11. However, for the case of VMAT QA, such 2D beam fluence verification per beam angle may not be appropriate because instantaneous beam aperture errors for VMAT deliveries were significant (up to 15%) for highly modulated plans even though MLC leaves were well‐within tolerances 29.…”

Section: Introductionmentioning

confidence: 99%

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A method to reconstruct and apply 3D primary fluence for treatment delivery verification

Shi

Mazur

et al. 2016

J Applied Clin Med Phys

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MotivationIn this study, a method is reported to perform IMRT and VMAT treatment delivery verification using 3D volumetric primary beam fluences reconstructed directly from planned beam parameters and treatment delivery records. The goals of this paper are to demonstrate that 1) 3D beam fluences can be reconstructed efficiently, 2) quality assurance (QA) based on the reconstructed 3D fluences is capable of detecting additional treatment delivery errors, particularly for VMAT plans, beyond those identifiable by other existing treatment delivery verification methods, and 3) QA results based on 3D fluence calculation (3DFC) are correlated with QA results based on physical phantom measurements and radiation dose recalculations.MethodsUsing beam parameters extracted from DICOM plan files and treatment delivery log files, 3D volumetric primary fluences are reconstructed by forward‐projecting the beam apertures, defined by the MLC leaf positions and modulated by beam MU values, at all gantry angles using first‐order ray tracing. Treatment delivery verifications are performed by comparing 3D fluences reconstructed using beam parameters in delivery log files against those reconstructed from treatment plans. Passing rates are then determined using both voxel intensity differences and a 3D gamma analysis. QA sensitivity to various sources of errors is defined as the observed differences in passing rates. Correlations between passing rates obtained from QA derived from both 3D fluence calculations and physical measurements are investigated prospectively using 20 clinical treatment plans with artificially introduced machine delivery errors.ResultsStudies with artificially introduced errors show that common treatment delivery problems including gantry angle errors, MU errors, jaw position errors, collimator rotation errors, and MLC leaf position errors were detectable at less than normal machine tolerances. The reported 3DFC QA method has greater sensitivity than measurement‐based QA methods. Statistical analysis‐based Spearman's correlations shows that the 3DFC QA passing rates are significantly correlated with passing rates of physical phantom measurement‐based QA methods.ConclusionAmong measurement‐less treatment delivery verification methods, the reported 3DFC method is less demanding than those based on full dose re‐calculations, and more comprehensive than those that solely checks beam parameters in treatment log files. With QA passing rates correlating to measurement‐based passing rates, the 3DFC QA results could be useful for complementing the physical phantom measurements, or verifying treatment deliveries when physical measurements are not available. For the past 4+ years, the reported method has been implemented at authors’ institution 1) as a complementary metric to physical phantom measurements for pretreatment, patient‐specific QA of IMRT and VMAT plans, and 2) as an important part of the log file‐based automated verification of daily patient treatment deliveries. It has been demonstrated to be useful in catching both treatment plan data transfer errors and treatment delivery problems.

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“…There is an ongoing discussion within the medical physics community regarding the validity of log‐file analysis for patient‐specific quality assurance (QA) of modulated treatment beams 10, 11, 12. Publications examining the reliability and effectiveness of this QA method are available for review 13, 14, 15, 16, 17, 18, 19, 20…”

Section: Introductionmentioning

confidence: 99%

Validation of a modern second‐check dosimetry system using a novel verification phantom

McDonald

Jacqmin

Mart

et al. 2017

J Applied Clin Med Phys

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PurposeTo evaluate the Mobius second‐check dosimetry system by comparing it to ionization‐chamber dose measurements collected in the recently released Mobius Verification Phantom™ (MVP). For reference, a comparison of these measurements to dose calculated in the primary treatment planning system (TPS), Varian Eclipse with the AcurosXB dose algorithm, is also provided. Finally, patient dose calculated in Mobius is compared directly to Eclipse to demonstrate typical expected results during clinical use of the Mobius system.MethodsSeventeen anonymized intensity‐modulated clinical treatment plans were selected for analysis. Dose was recalculated on the MVP in both Eclipse and Mobius. These calculated doses were compared to doses measured using an A1SL ionization‐chamber in the MVP. Dose was measured and analyzed at two different chamber positions for each treatment plan. Mobius calculated dose was then compared directly to Eclipse using the following metrics; target mean dose, target D95%, global 3D gamma pass rate, and target gamma pass rate. Finally, these same metrics were used to analyze the first 36 intensity modulated cases, following clinical implementation of the Mobius system.ResultsThe average difference between Mobius and measurement was 0.3 ± 1.3%. Differences ranged from −3.3 to + 2.2%. The average difference between Eclipse and measurement was −1.2 ± 0.7%. Eclipse vs. measurement differences ranged from −3.0 to −0.1%. For the 17 anonymized pre‐clinical cases, the average target mean dose difference between Mobius and Eclipse was 1.0 ± 1.1%. Average target D95% difference was ‐0.9 ± 2.0%. Average global gamma pass rate, using a criteria of 3%, 2 mm, was 94.4 ± 3.3%, and average gamma pass rate for the target volume only was 80.2 ± 12.3%. Results of the first 36 intensity‐modulated cases, post‐clinical implementation of Mobius, were similar to those seen for the 17 pre‐clinical test cases.ConclusionMobius correctly calculated dose for each tested intensity modulated treatment plan, agreeing with measurement to within 3.5% for all cases analyzed. The dose calculation accuracy and independence of the Mobius system is sufficient to provide a rigorous second‐check of a modern TPS.

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Catching errors with patient-specific pretreatment machine log file analysis

Cited by 52 publications

References 16 publications

The use of log file analysis within VMAT audits

The use of log file analysis within VMAT audits

A method to reconstruct and apply 3D primary fluence for treatment delivery verification

Validation of a modern second‐check dosimetry system using a novel verification phantom

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