Robust Direct Aperture Optimization for Radiation Therapy Treatment Planning

TL;DR

This paper introduces a robust direct aperture optimization model for radiation therapy that integrates uncertainty mitigation, along with a heuristic to generate high-quality plans efficiently, improving treatment plan quality and feasibility.

Contribution

It presents a novel robust DAO model combined with a candidate plan heuristic to handle uncertainties and improve plan quality in radiation therapy.

Findings

The heuristic produces rapid, feasible, high-quality plans.

Incorporating uncertainty improves treatment robustness.

Computational results show enhanced plan quality with the proposed methods.

Abstract

Intensity-modulated radiation therapy (IMRT) allows for the design of customized, highly-conformal treatments for cancer patients. Creating IMRT treatment plans, however, is a mathematically complex process, which is often tackled in multiple, simpler stages. This sequential approach typically separates radiation dose requirements from mechanical deliverability considerations, which may result in suboptimal treatment quality. For patient health to be considered paramount, holistic models must address these plan elements concurrently, eliminating quality loss between stages. This combined direct aperture optimization (DAO) approach is rarely paired with uncertainty mitigation techniques, such as robust optimization, due to the inherent complexity of both parts. This paper outlines a robust DAO (RDAO) model and discusses novel methodologies for efficiently integrating salient constraints. Because the highly-complex RDAO model is difficult to solve, an original candidate plan generation (CPG) heuristic is proposed. The CPG produces rapid, high-quality, feasible plans, which are immediately clinically viable, and can also be used to generate a feasible incumbent solution for warm starting the RDAO model. Computational results obtained using clinical patient datasets with motion uncertainty show the benefit of incorporating the CPG, both in terms of first incumbent solution and final output plan quality.