Efficient proton arc optimization and delivery through energy layer pre‐selection and post‐filtering Article Swipe
S. Wuyckens
,
Viktor Wase
,
Otte Marthin
,
Johan Sundström
,
Guillaume Janssens
,
Elena Borderias‐Villarroel
,
Kevin Souris
,
Edmond Sterpin
,
Erik Engwall
,
John A. Lee
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/mp.17127
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/mp.17127
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/mp.17127
- OA Status
- green
- Cited By
- 8
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396904434
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396904434Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/mp.17127Digital Object Identifier
- Title
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Efficient proton arc optimization and delivery through energy layer pre‐selection and post‐filteringWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-05-14Full publication date if available
- Authors
-
S. Wuyckens, Viktor Wase, Otte Marthin, Johan Sundström, Guillaume Janssens, Elena Borderias‐Villarroel, Kevin Souris, Edmond Sterpin, Erik Engwall, John A. LeeList of authors in order
- Landing page
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https://doi.org/10.1002/mp.17127Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/2078.1/294945Direct OA link when available
- Concepts
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Proton therapy, Robustness (evolution), Computer science, Proton, Energy (signal processing), Algorithm, Beam (structure), Mathematics, Statistics, Physics, Optics, Chemistry, Quantum mechanics, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2025: 8Per-year citation counts (last 5 years)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |