A novel adaptive energy switching algorithm for proton arc therapy based on the machine-specific delivery...

Area of Science:

• Medical Physics
• Radiation Oncology
• Medical Imaging

Background:

• Spot-scanning proton arc therapy (SPArc) faces challenges in treatment delivery efficiency.
• Optimizing energy layer switching is crucial for reducing beam delivery time in proton therapy.
• The University Medical Center Groningen (UMCG) upgraded its system to enable fast energy layer ascending switching (ELAS).

Purpose of the Study:

• Introduce a novel adaptive energy switching SPArc optimization algorithm (SPArc-AES).
• Tailor the algorithm to machine-specific delivery characteristics of proton therapy systems.
• Improve SPArc treatment delivery efficiency and plan quality.

Main Methods:

• Developed the SPArc-AES algorithm based on polynomial increasing energy layer ascending switching.
• Utilized K-Medoids clustering and simulated annealing for energy delivery sequence optimization.
• Evaluated plan quality, robustness, and delivery efficiency on ten patient cases, comparing with SPArc_seq.

Main Results:

• SPArc-AES significantly improved treatment delivery efficiency compared to SPArc_seq.
• Reduced energy layer switching time by 34.03% and beam delivery time by 31.10%.
• Achieved better target dose conformality and lower organ-at-risk doses without additional constraints.

Conclusions:

• The novel SPArc-AES algorithm enhances SPArc optimization efficiency.
• Machine-specific delivery characteristics are leveraged for improved performance.
• Eliminating unnecessary constraints on energy layer switching improves efficiency and plan quality.