Schlaefer A, Schweikard A
Department of Radiation Oncology, Stanford University, Stanford, California 94305-5847, USA.
Med Phys. 2008 May;35(5):2094-103. doi: 10.1118/1.2900716.
Achieving good conformality and a steep dose gradient around the target volume remains a key aspect of radiosurgery. Clearly, this involves a trade-off between target coverage, conformality of the dose distribution, and sparing of critical structures. Yet, image guidance and robotic beam placement have extended highly conformal dose delivery to extracranial and moving targets. Therefore, the multi-criteria nature of the optimization problem becomes even more apparent, as multiple conflicting clinical goals need to be considered coordinate to obtain an optimal treatment plan. Typically, planning for robotic radiosurgery is based on constrained optimization, namely linear programming. An extension of that approach is presented, such that each of the clinical goals can be addressed separately and in any sequential order. For a set of common clinical goals the mapping to a mathematical objective and a corresponding constraint is defined. The trade-off among the clinical goals is explored by modifying the constraints and optimizing a simple objective, while retaining feasibility of the solution. Moreover, it becomes immediately obvious whether a desired goal can be achieved and where a trade-off is possible. No importance factors or predefined prioritizations of clinical goals are necessary. The presented framework forms the basis for interactive and automated planning procedures. It is demonstrated for a sample case that the linear programming formulation is suitable to search for a clinically optimal treatment, and that the optimization steps can be performed quickly to establish that a Pareto-efficient solution has been found. Furthermore, it is demonstrated how the stepwise approach is preferable compared to modifying importance factors.
在放射外科中,实现良好的适形性以及在靶区周围形成陡峭的剂量梯度仍然是一个关键方面。显然,这涉及到靶区覆盖、剂量分布适形性和关键结构保护之间的权衡。然而,图像引导和机器人束流放置已将高度适形的剂量输送扩展到颅外和移动靶区。因此,优化问题的多标准性质变得更加明显,因为需要协调考虑多个相互冲突的临床目标以获得最佳治疗方案。通常,机器人放射外科治疗计划基于约束优化,即线性规划。本文提出了该方法的一种扩展,使得每个临床目标都可以单独且以任何顺序进行处理。对于一组常见的临床目标,定义了其到数学目标和相应约束的映射。通过修改约束并优化一个简单目标来探索临床目标之间的权衡,同时保持解的可行性。此外,是否能够实现期望的目标以及何处可能需要进行权衡变得一目了然。无需临床目标的重要性因素或预定义优先级。所提出的框架构成了交互式和自动化治疗计划程序的基础。通过一个示例案例表明,线性规划公式适用于寻找临床最优治疗方案,并且可以快速执行优化步骤以确定已找到帕累托有效解。此外,还展示了与修改重要性因素相比,逐步方法更可取的原因。