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.
调强放射治疗（IMRT）是癌症治疗中最常用的技术之一。该技术利用直线加速器将辐射直接作用于肿瘤中的癌细胞，从而减少辐射对肿瘤周围器官的影响。IMRT问题的复杂性迫使研究人员将其分解为三个依次处理的子问题。采用这种顺序方法，我们首先需要确定一个射束角度配置，即肿瘤辐射的照射点（射束角度）集合。这第一个问题被称为射束角度优化（BAO）问题。接着，我们必须优化从每个角度向肿瘤传递的辐射强度。这第二个问题被称为注量图优化（FMO）问题。最后，我们需要为每个射束角度生成一组孔径，使得上一步计算出的强度能够实际传递。这第三个问题被称为序列化问题。依次解决这三个子问题，临床医生可以从物理角度获得一个可执行的治疗计划。然而，所获得的治疗计划通常包含过多的孔径，导致治疗时间过长。避免这一问题的一种策略是直接孔径优化（DAO）问题。在DAO问题中，其核心思想是将FMO问题与序列化问题合并。因此，在优化辐射强度时，会考虑传递过程的物理约束。DAO问题通常被建模为一个混合整数优化问题，其目标是在考虑多叶准直器设备施加的物理约束下，确定孔径形状及其相应的辐射强度。通过解决DAO问题，可以在无需额外序列化步骤的情况下，生成临床上可接受的治疗方案以供患者使用。在本研究中，我们提出使用著名的粒子群优化（PSO）算法来解决DAO问题。我们的方法结合了数学规划来优化强度，并利用PSO来优化孔径形状。此外，我们引入了一种修复启发式方法，以最小的治疗计划影响来增强孔径形状。我们将所提出的算法应用于前列腺癌病例，并将我们的结果与顺序方法获得的结果进行比较。结果表明，与顺序方法相比，PSO获得了具有竞争力的结果，减少了辐射时间（射束开启时间），并以更高的效率利用了可用孔径。
Particle Swarm Optimisation Applied to the Direct Aperture Optimisation Problem in Radiation Therapy
肿瘤(癌症)患者之家