Particle Swarm Optimisation Applied to the Direct Aperture Optimisation Problem in Radiation Therapy

Simple Summary Intensity Modulated Radiation Therapy (IMRT) is a cancer treatment that targets cancer cells while protecting nearby healthy organs using a linear accelerator. Traditional IMRT planning involves a sequential process: optimizing beam intensities (Fluence Map Optimization) for a set of angles and then sequencing (Multi-Leaf Sequencing). Unfortunately, treatment plans obtained by the sequencing step are severely impaired. One approach that addresses the problem described is the Direct Aperture Optimisation (DAO) approach. The DAO problem aims at simultaneously determining deliverable aperture shapes and a set of radiation intensities. This approach considers physical and delivery time constraints, allowing clinically acceptable treatment plans to be generated. In this work, we adapt the Particle Swarm Optimisation to solve the DAO and introduce a reparation heuristic to enhance treatment plans. We tested our method on prostate cancer patients and found that it delivers radiation more efficiently than the traditional approach, reducing treatment time and improving outcomes.Abstract Intensity modulated radiation therapy (IMRT) is one of the most used techniques for cancer treatment. Using a linear accelerator, it delivers radiation directly at the cancerogenic cells in the tumour, reducing the impact of the radiation on the organs surrounding the tumour. The complexity of the IMRT problem forces researchers to subdivide it into three sub-problems that are addressed sequentially. Using this sequential approach, we first need to find a beam angle configuration that will be the set of irradiation points (beam angles) over which the tumour radiation is delivered. This first problem is called the Beam Angle Optimisation (BAO) problem. Then, we must optimise the radiation intensity delivered from each angle to the tumour. This second problem is called the Fluence Map Optimisation (FMO) problem. Finally, we need to generate a set of apertures for each beam angle, making the intensities computed in the previous step deliverable. This third problem is called the Sequencing problem. Solving these three sub-problems sequentially allows clinicians to obtain a treatment plan that can be delivered from a physical point of view. However, the obtained treatment plans generally have too many apertures, resulting in long delivery times. One strategy to avoid this problem is the Direct Aperture Optimisation (DAO) problem. In the DAO problem, the idea is to merge the FMO and the Sequencing problem. Hence, optimising the radiation's intensities considers the physical constraints of the delivery process. The DAO problem is usually modelled as a Mixed-Integer optimisation problem and aims to determine the aperture shapes and their corresponding radiation intensities, considering the physical constraints imposed by the Multi-Leaf Collimator device. In solving the DAO problem, generating clinically acceptable treatments without additional sequencing steps to deliver to the patients is possible. In this work, we propose to solve the DAO problem using the well-known Particle Swarm Optimisation (PSO) algorithm. Our approach integrates the use of mathematical programming to optimise the intensities and utilizes PSO to optimise the aperture shapes. Additionally, we introduce a reparation heuristic to enhance aperture shapes with minimal impact on the treatment plan. We apply our proposed algorithm to prostate cancer cases and compare our results with those obtained in the sequential approach. Results show that the PSO obtains competitive results compared to the sequential approach, receiving less radiation time (beam on time) and using the available apertures with major efficiency.

Automated summary

Intensity Modulated Radiation Therapy (IMRT) is a cancer treatment that targets cancer cells while protecting nearby healthy organs. Traditional IMRT planning has limitations, and the Direct Aperture Optimization (DAO) approach can improve treatment outcomes. This study proposes using Particle Swarm Optimization (PSO) and a reparation heuristic to solve the DAO problem, and tests the method on prostate cancer patients.