Simulation of realistic linac motion improves the accuracy of a Monte Carlo based VMAT plan QA system

• Published in: Radiotherapy and oncology Vol. 109; no. 3; pp. 377 - 383
• Main Authors: Boylan, Christopher J, Aitkenhead, Adam H, Rowbottom, Carl G, Mackay, Ranald I
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.12.2013
• Subjects: Computer Simulation; Head and Neck Neoplasms - radiotherapy; Hematology, Oncology and Palliative Medicine; Humans; Male; Monte Carlo; Monte Carlo Method; Particle Accelerators; Phantoms, Imaging; Prostatic Neoplasms - radiotherapy; Quality assurance; Quality Assurance, Health Care; Radiometry - methods; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted - methods; Reproducibility of Results; Software; Treatment verification; VMAT; VMAT; Quality assurance; Monte Carlo; Treatment verification
• DOI: 10.1016/j.radonc.2013.08.046
• ISSN: 0167-8140

Abstract Purpose To investigate the use of a software-based pre-treatment QA system for VMAT, which incorporates realistic linac motion during delivery. Methods A beam model was produced using the GATE platform for GEANT4 Monte Carlo dose calculations. Initially validated against static measurements, the model was then integrated with a VMAT delivery emulator, which reads plan files and generates a set of dynamic delivery instructions analogous to the linac control system. Monte Carlo simulations were compared to measurements on dosimetric phantoms for prostate and head and neck VMAT plans. Comparisons were made between calculations using fixed control points, and simulations of continuous motion utilising the emulator. For routine use, the model was incorporated into an automated pre-treatment QA system. Results The model showed better agreement with measurements when incorporating linac motion: mean gamma pass ( Γ < 1) over 5 prostate plans was 100.0% at 3%/3 mm and 97.4% at 2%/2 mm when compared to measurement. For the head and neck plans, delivered to the anatomical phantom, gamma passes were 99.4% at 4%/4 mm and 94.94% at 3%/3 mm. For example simulations within patient CT data, gamma passes were observed which are within our centre’s tolerance for pre-treatment QA. Conclusions Through comparison to phantom measurements, it was found that the incorporation of a realistic linac motion improves the accuracy of the model compared to the simulation of fixed control points. The ability to accurately calculate dose as a second check of the planning system, and determine realistic delivery characteristics, may allow for the reduction of machine-based pre-treatment plan QA for VMAT.