Estimation theory and model parameter selection for therapeutic treatment plan optimization
Estimation theory and model parameter selection for therapeutic treatment plan optimization
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DOI：
10.1118/1.598749
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发表时间：
1999-11-01
期刊：
影响因子：
3.8
通讯作者：
Boyer, AL
中科院分区：
文献类型：
--
作者：
Xing, L;Li, JG;Boyer, AL
治疗优化通常被表述为一个反问题，其从规定的剂量分布开始，并在目标函数的指导下获得优化解。解决方案是在目标和敏感结构的冲突要求之间进行折衷。在本文中，治疗计划的优化制定为一个离散的，可能是非凸系统的估计问题。引入偏好函数的概念。该方法不是给结构（或一组体素）规定剂量，而是优先考虑具有不同偏好水平的剂量，并将问题简化为选择具有合适估计器的解决方案。偏好函数为系统的统计分析提供了基础，并允许我们将统计分析中开发的各种技术应用于计划优化。结果表明，基于二次目标函数的优化是形式主义的一个特例。本文提出了一种用计算机确定模型参数的一般两步法。该方法提供了一种有效的方法，包括先验知识的优化过程。该方法说明了使用一个简化的两像素系统以及两个临床病例。该方法的一般性，加上有前途的示范，表明该方法具有广泛的影响，放射治疗计划的优化。(C)1999年美国医学物理学家协会。[S0093-2405（99）01011-1]。
Treatment optimization is usually formulated as an inverse problem, which starts with a prescribed dose distribution and obtains an optimized solution under the guidance of an objective function. The solution is a compromise between the conflicting requirements of the target and sensitive structures. In this paper, the treatment plan optimization is formulated as an estimation problem of a discrete and possibly nonconvex system. The concept of preference function is introduced. Instead of prescribing a dose to a structure (or a set of voxels), the approach prioritizes the doses with different preference levels and reduces the problem into selecting a solution with a suitable estimator. The preference function provides a foundation for statistical analysis of the system and allows us to apply various techniques developed in statistical analysis to plan optimization. It is shown that an optimization based on a quadratic objective function is a special case of the formalism. A general two-step method for using a computer to determine the values of the model parameters is proposed. The approach provides an efficient way to include prior knowledge into the optimization process. The method is illustrated using a simplified two-pixel system as well as two clinical cases. The generality of the approach, coupled with promising demonstrations, indicates that the method has broad implications for radiotherapy treatment plan optimization. (C) 1999 American Association of Physicists in Medicine. [S0093-2405(99)01011-1].