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The Agility multileaf collimator (MLC) mounted in Elekta linear accelerators features some unique design characteristics, such as large leaf thickness, eccentric curvature at the leaf tip, and defocused leaf sides ('tilting'). These characteristics offer several advantages but modeling them in treatment planning systems (TPSs) is challenging.The goals of this study were to investigate the challenges faced when modeling the Agility in two commercial TPSs (Monaco and RayStation) and to explore how the implemented MLC models could be improved in the future.Four linear accelerators equipped with the Agility, located at different centers, were used for the study. Three centers use the RayStation TPS and the other one uses Monaco. For comparison purposes, data from four Varian linear accelerators with the Millennium 120 MLC were also included. Average doses measured with asynchronous sweeping gap tests were used to characterize and compare the characteristics of the Millennium and the Agility MLCs and to assess the MLC model in the TPSs. The FOURL test included in the ExpressQA package, provided by Elekta, was also used to evaluate the tongue-and-groove with radiochromic films. Finally, raytracing was used to investigate the impact of the MLC geometry and to understand the results obtained for each MLC.The geometry of the Agility produces dosimetric effects associated with the rounded leaf end up to a distance 20 mm away from the leaf tip end measured at the isocenter plane. This affects the tongue-and-groove shadowing, which progressively increases along the distance to the tip end. The RayStation and Monaco TPSs did not account for this effect, which made trade-offs in the MLC parameters necessary and greatly varied the final MLC parameters used by different centers. Raytracing showed that these challenging leaf tip effects were directly related to the MLC geometry and that the characteristics mainly responsible for the large leaf tip effects of the Agility were its tilting design and its small source-to-collimator distance.The MLC models implemented in RayStation and Monaco could not accurately reproduce the leaf tip effects for the Agility. Therefore, trade-offs are needed and the optimal MLC parameters are dependent on the specific characteristics of treatment plans. Refining the MLC models for the Agility to better approximate the measured leaf tip and tongue-and-groove effects would extend the validity of the MLC model, reduce the variability in the MLC parameters used by the community, and facilitate the standardization of the MLC configuration process. |
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