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

Area of Science:

• Medical Physics
• Radiation Oncology
• Cancer Treatment

Background:

• Proton arc therapy (PAT) offers improved dose distribution and faster delivery compared to conventional proton treatments.
• Current PAT algorithms often fail to achieve significant speed-up, sometimes increasing delivery time.

Purpose of the Study:

• To minimize PAT delivery time using a hybrid method combining geometry-based energy layer (EL) pre-selection and dose-based EL filtering.
• To compare the performance of this hybrid method against a baseline approach without filtering.

Main Methods:

• Developed three EL filtering methods: unrestricted, switch-up (SU), and switch-up gap (SU gap).
• Integrated filters with the RayStation dynamic proton arc optimization framework's ELSA.
• Evaluated methods using data from eight cancer patients (four oropharyngeal, four lung), assessing plan quality and delivery time against intensity-modulated proton therapy (IMPT).

Main Results:

• The SU gap filtering method reduced delivery time by up to 22% (oropharyngeal) and 15% (lung) while maintaining plan quality.
• Unrestricted filtering also showed significant time reduction; SU filtering had limited impact.
• Target coverage remained robust (within 1% variation), and organ-at-risk doses were comparable or decreased.

Conclusions:

• Developed methods accelerate PAT delivery without compromising plan quality.
• Advancements can improve treatment efficiency and patient throughput in radiation oncology.