Optimizing spot-scanning proton arc therapy with a novel spot sparsity approach

One of the main challenges of utilizing spot-scanning proton arc therapy (SPArc) in routine clinics is treatment delivery efficiency. Spot reduction, which relies on spot sparsity optimization (SSO), is crucial for achieving high delivery efficiency in SPArc.

This study aims to develop a novel SSO approach based on the alternating directions method of multipliers (ADMM) for SPArc to achieve high treatment delivery efficiency and maintain optimal dosimetric plan quality.

In this study, SSO for SPArc is based on the least-square dose fidelity term with L0-norm regularization. The novel optimization approach is based on the ADMM framework, in which the minimum monitor unit constraint was considered to improve the plan quality. A state-of-the-art SSO method, the primal-dual active set with continuation (PDASC) algorithm published previously, was utilized as a benchmark. Two SPArc plan groups with the same beam assignment and clinical constraint were generated, in which the former group was SPArc plan with SSO utilizing ADMM, denoted as $$ , and the later group was SPArc with SSO utilizing PDASC, denoted as $$ . Nine clinical cases included five different cancer sites (brain, lung, liver, prostate, and head&neck cancer) were used. The SSO method's performance was evaluated in terms of spot sparsity level (the number of zero-valued elements divided by the total number of elements), beam delivery time, dosimetric plan quality, and plan robustness.

Compared to the $$ plan, the $$ plan exhibits superior sparsity and higher delivery efficiency while maintaining good plan quality.

This study introduces a novel spot sparsity optimization approach using the ADMM framework to improve the delivery efficiency of SPArc. Compared to the existing state-of-the-art SSO method, such an approach could further enhance delivery efficiency while maintaining good plan quality, which could promote the implementation of SPArc in the clinic's.