Abstract
The latest generation of linear accelerators allows the simultaneous motion of gantry and couch leading to highly noncoplanar arc trajectories. The use of noncoplanar trajectories in arc radiotherapy was recently proposed to combine the benefits of arc treatment plans, such as short treatment times, with the benefits of step-and-shoot noncoplanar intensity-modulated radiation therapy (IMRT) treatment plans, such as improved organ sparing. In this paper, a two-step approach for the optimization of highly noncoplanar arc trajectories is presented and tested using a complex nasopharyngeal tumor case already treated at the Portuguese Institute of Oncology of Coimbra. In the first step, a set of noncoplanar beam directions is calculated resorting to one of the beam angle optimization (BAO) algorithms proposed in our previous works for step-and-shoot IMRT. In the second step, anchored in the points (beam directions) calculated in the first step, the proposed optimization strategy determines iteratively more anchor points that will define the noncoplanar arc trajectory, considering the dosimetric criteria used for the noncoplanar BAO search rather than geometric or time criteria commonly used. For the patient tested, the resulting noncoplanar arc therapy plan has undoubtedly greater overall quality compared to both the coplanar arc therapy plan and the typically used coplanar equispaced step-and-shoot IMRT plan.
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Acknowledgments
This work has been supported by project grant POCI-01-0145-FEDER-028030 and by the Fundação para a Ciência e a Tecnologia (FCT) under project grant UID/Multi/00308/2019. The authors show gratitude to Ben Heijmen and Sebastiaan Breedveld for giving permission and helping them to install Erasmus-iCycle.
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Rocha, H., Dias, J., Ventura, T., Ferreira, B., do Carmo Lopes, M. (2019). An Optimization Approach for Noncoplanar Intensity-Modulated Arc Therapy Trajectories. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_15
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