Robust Direct Aperture Optimization for Radiation Therapy Treatment Planning

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