Efficient proton arc optimization and delivery through energy layer pre-selection and post-filtering.

Autor: Wuyckens S; UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging and Radiation Oncology Laboratory, Brussels, Belgium., Wase V; RaySearch Laboratories, Stockholm, Sweden., Marthin O; RaySearch Laboratories, Stockholm, Sweden., Sundström J; RaySearch Laboratories, Stockholm, Sweden., Janssens G; UCLouvain, Institute of Information and Communication Technologies, Louvain-La-Neuve, Belgium.; Ion Beam Applications SA, Louvain-La-Neuve, Belgium., Borderias-Villarroel E; UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging and Radiation Oncology Laboratory, Brussels, Belgium., Souris K; UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging and Radiation Oncology Laboratory, Brussels, Belgium.; Ion Beam Applications SA, Louvain-La-Neuve, Belgium., Sterpin E; UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging and Radiation Oncology Laboratory, Brussels, Belgium.; KULeuven, Department of Oncology, Laboratory of experimental radiotherapy, Leuven, Belgium.; Particle Therapy Interuniversity Center Leuven - PARTICLE, Leuven, Belgium., Engwall E; RaySearch Laboratories, Stockholm, Sweden., Lee JA; UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging and Radiation Oncology Laboratory, Brussels, Belgium.
Jazyk: angličtina
Zdroj: Medical physics [Med Phys] 2024 Jul; Vol. 51 (7), pp. 4982-4995. Date of Electronic Publication: 2024 May 14.
DOI: 10.1002/mp.17127
Abstrakt: Background: Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time.
Purpose: This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering, and comparing its performance to a baseline approach without filtering.
Methods: Three methods of EL filtering were developed: unrestricted, switch-up (SU), and switch-up gap (SU gap) filtering. The unrestricted method filters the lowest weighted EL while the SU gap filtering removes the EL around a new SU to minimize the gantry rotation braking. The SU filtering removes the lowest weighted group of EL that includes a SU. These filters were combined with the RayStation dynamic proton arc optimization framework energy layer selection and spot assignment (ELSA). Four bilateral oropharyngeal and four lung cancer patients' data were used for evaluation. Objective function values, target coverage robustness, organ-at-risk doses and normal tissue complication probability evaluations, as well as comparisons to intensity-modulated proton therapy (IMPT) plans, were used to assess plan quality.
Results: The SU gap filtering algorithm performed best in five out of the eight cases, maintaining plan quality within tolerance while reducing beam delivery time, in particular for the oropharyngeal cohort. It achieved up to approximately 22% and 15% reduction in delivery time for oropharyngeal and lung treatment sites, respectively. The unrestricted filtering algorithm followed closely. In contrast, the SU filtering showed limited improvement, suppressing one or two SU without substantial delivery time shortening. Robust target coverage was kept within 1% of variation compared to the PAT baseline plan while organs-at-risk doses slightly decreased or kept about the same for all patients.
Conclusions: This study provides insights to accelerate PAT delivery without compromising plan quality. These advancements could enhance treatment efficiency and patient throughput.
(© 2024 American Association of Physicists in Medicine.)
Databáze: MEDLINE
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