Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning.
Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning.
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DOI：
10.3389/fonc.2023.1238824
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发表时间：
2023
影响因子：
4.7
通讯作者：
中科院分区：
文献类型：
--
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我们应用优越化方法的约束调强放射治疗（IMRT）治疗计划问题。优化将可行性搜索投影算法与目标函数约简相结合：底层投影算法通过梯度下降步骤进行扰动，以将算法转向具有较低目标函数值的解决方案，而不是仅通过可行性搜索获得的解决方案。在开源逆向计划工具包matRad中，我们使用成熟的Agmon，Motzkin和Schoenberg（AMS）可行性寻求投影算法和常见的非线性剂量优化目标函数实现了一个原型算法框架。基于该原型，我们将优越化应用于强度调制放射治疗治疗计划，并将其与（i）裸可行性寻求（即，没有任何目标函数）和（ii）使用一阶导数的非线性约束优化。对于这些比较，我们使用TG119水模体，头颈部和前列腺患者的CORT数据集。裸可行性寻求与AMS证实了以前的研究，表明它可以找到的解决方案，几乎等同于所建立的分段最小二乘优化方法。优越化原型解决了线性约束规划问题，具有与通用非线性约束优化器类似的剂量测定性能，同时在约束邻近度和目标函数减少方面显示出平滑收敛。在放射治疗逆向计划中，约束优化是一种有效的替代方法。未来的扩展与其他可行性寻求方法，例如，由于剂量体积限制和更复杂的扰动，可能会释放其高性能反向治疗计划的全部潜力。
We apply the superiorization methodology to the constrained intensity-modulated radiation therapy (IMRT) treatment planning problem. Superiorization combines a feasibility-seeking projection algorithm with objective function reduction: The underlying projection algorithm is perturbed with gradient descent steps to steer the algorithm towards a solution with a lower objective function value compared to one obtained solely through feasibility-seeking. Within the open-source inverse planning toolkit matRad, we implement a prototypical algorithmic framework for superiorization using the well-established Agmon, Motzkin, and Schoenberg (AMS) feasibility-seeking projection algorithm and common nonlinear dose optimization objective functions. Based on this prototype, we apply superiorization to intensity-modulated radiation therapy treatment planning and compare it with (i) bare feasibility-seeking (i.e., without any objective function) and (ii) nonlinear constrained optimization using first-order derivatives. For these comparisons, we use the TG119 water phantom, the head-and-neck and the prostate patient of the CORT dataset. Bare feasibility-seeking with AMS confirms previous studies, showing it can find solutions that are nearly equivalent to those found by the established piece-wise least-squares optimization approach. The superiorization prototype solved the linearly constrained planning problem with similar dosimetric performance to that of a general-purpose nonlinear constrained optimizer while showing smooth convergence in both constraint proximity and objective function reduction. Superiorization is a useful alternative to constrained optimization in radiotherapy inverse treatment planning. Future extensions with other approaches to feasibility-seeking, e.g., with dose-volume constraints and more sophisticated perturbations, may unlock its full potential for high performant inverse treatment planning.
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影响因子：
1.2
作者：
Breedveld S;Heijmen B
通讯作者：
Heijmen B
影响因子：
3.8
作者：
BORTFELD, T;SCHLEGEL, W;RHEIN, B
通讯作者：
RHEIN, B
影响因子：
2.1
作者：
Bonacker, Esther;Gibali, Aviv;Suss, Philipp
通讯作者：
Suss, Philipp
DOI：
10.4153/cjm-1954-037-2
发表时间：
1954-01-01
影响因子：
0.7
作者：
AGMON, S
通讯作者：
AGMON, S
影响因子：
3.8
作者：
Carlsson, F;Forsgren, A
通讯作者：
Forsgren, A