Evaluation of machine log files/MC-based treatment planning and delivery QA as compared to ArcCHECK QA

• Verfasst von: Stanhope, Carl [VerfasserIn]
• Verfasst von: Drake, Douglas G. [VerfasserIn]
• Verfasst von: Liang, Jian [VerfasserIn]
• Verfasst von: Alber, Markus [VerfasserIn]
• Verfasst von: Söhn, Matthias [VerfasserIn]
• Verfasst von: Habib, Charbel [VerfasserIn]
• Verfasst von: Willcut, Virgil [VerfasserIn]
• Verfasst von: Yan, Di [VerfasserIn]
• Titel: Evaluation of machine log files/MC-based treatment planning and delivery QA as compared to ArcCHECK QA
• Verf.angabe: Carl W. Stanhope, Douglas G. Drake, Jian Liang, Markus Alber, Matthias Söhn, Charbel Habib, Virgil Willcut, Di Yan
• E-Jahr: 2018
• Jahr: 20 April 2018
• Umfang: 11 S.
• Fussnoten: Gesehen am 08.04.2020
• Titel Quelle: Enthalten in: Medical physics
• Ort Quelle: Hoboken, NJ : Wiley, 1974
• Jahr Quelle: 2018
• Band/Heft Quelle: 45(2018), 7, Seite 2864-2874
• ISSN Quelle: 2473-4209
• ISSN Quelle: 1522-8541
• DOI: doi:10.1002/mp.12926
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: ArcCHECK
• Sach-SW: log file
• Sach-SW: Monte Carlo
• Sach-SW: quality assurance
• K10plus-PPN: 1694254178

Abstract

Purpose: A treatment planning/delivery QA tool using linac log files (LF) and Monte Carlo (MC) dose calculation is investigated as a standalone alternative to phantom-based patient-specific QA (ArcCHECK (AC)). Methods Delivering a variety of fields onto MapCHECK2 and ArcCHECK, diode sensitivity dependence on dose rate (in-field) and energy (primarily out-of-field) was quantified. AC and LF QAs were analyzed with respect to delivery complexity by delivering 12 × 12 cm static fields/arcs comprised of varying numbers of abutting sub-fields onto ArcCHECK. About 11 clinical dual-arc VMAT patients planned using Pinnacle's convolution-superposition (CS) were delivered on ArcCHECK and log file dose (LF-CS and LF-MC) calculated. To minimize calculation time, reduced LF-CS sampling (1/2/3/4° control point spacing) was investigated. Planned (“Plan”) and LF-reconstructed CS and MC doses were compared with each other and AC measurement via statistical [mean ± StdDev(σ)] and gamma analyses to isolate dosimetric uncertainties and quantify the relative accuracies of AC QA and MC-based LF QA. Results Calculation and ArcCHECK measurement differed by up to 1.5% in-field due to variation in dose rate and up to 5% out-of-field. For the experimental segment-varying plans, despite CS calculation deviating by as much as 13% from measurement, Plan-MC and LF-MC doses generally matched AC measurement within 3%. Utilizing 1° control point spacing, 2%/2 mm LF-CS vs AC pass rates (97%) were slightly lower than Plan-CS vs AC pass rates (97.5%). Utilizing all log file samples, 2%/2 mm LF-MC vs AC pass rates (97.3%) were higher than Plan-MC vs AC (96.5%). Phantom-dependent, calculation algorithm-dependent (MC vs CS), and delivery error-dependent dose uncertainties were 0.8 ± 1.2%, 0.2 ± 1.1%, and 0.1 ± 0.9% respectively. Conclusion Reconstructing every log file sample with no increase in computational cost, MC-based LF QA is faster and more accurate than CS-based LF QA. Offering similar dosimetric accuracy compared to AC measurement, MC-based log files can be used for treatment planning QA.