Purpose: Spot-scanning Proton Arc (SPArc) plan normally contains thousands of spot numbers in which is the delivery time is proportional to the number of spots. It is critical to find an optimal SPArc with a fast delivery speed while maintaining a good plan quality. Thus, we developed a novel evolutionary algorithm to directly search the optimal spot sparsity solution in the balance of plan quality and beam delivery time (BDT).
Methods: The planning platform included a plan quality objective, a selector and a generator. A selector was designed to filter or add the spot according to the expected spot number, based on the user’s input of delivery time. The generator is based on the MATLAB optimization toolbox trust-region-reflective solver. The selector and generator are used alternatively to optimize a spot sparsity solution. Two clinical cases, such as brain tumor and lung cancer, were used for testing purposes. Serials of user-defined BDTs from 15s to 120s were used as the inputs. The relationship between the plan’s cost function value and BDT was evaluated in these two cases.
Results: The evolutionary SPArc algorithm could optimize a SPArc plan based on clinical user inputs using treatment delivery time directly. The plan quality remains optimal in the brain and lung cases until the delivery time was shorter than 25s and 70s, respectively. The plan quality degraded as the input delivery time became too short, indicating that the plan lacked enough spot or degree of optimization freedom.
Conclusion: This is the first SPArc planning framework to directly optimize plan quality with the delivery time as an input for the new generation of proton therapy systems. This work paved the roadmap for implementing such new technology in a routine clinic and provided a planning platform to explore the trade-off between the delivery time and plan quality.
Protons, Optimization, Treatment Planning
TH- External Beam- Particle/high LET therapy: Proton therapy – dose optimization