Dose painting by means of Monte Carlo treatment planning at the voxel level
Dose painting by means of Monte Carlo treatment planning at the voxel level
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DOI：
10.1016/j.ejmp.2017.04.005
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发表时间：
2017-10-01
影响因子：
3.4
通讯作者：
Leal, A.
中科院分区：
文献类型：
--
作者：
Jimenez-Ortega, E.;Ureba, A.;Leal, A.
目的：开发一种新的优化算法，以基于完整的蒙特卡罗（MC）计算来执行真实剂量绘制数字（DPBN）规划。方法：提出了四种对来自 PET 数据的体素值进行不同聚类的配置。线性规划 (LP) 公式下体素级别的优化方法用于逆向规划，并在内部蒙特卡罗治疗规划系统 CARMEN 中实施。结果：Beamlet 解决方案实现了目标，并且不同配置之间没有显示出显着差异。分段解决方案之间观察到更多差异。不进行聚类的剂量处方图规划是更好的解决方案。结论：在体素水平上进行LP优化，不受剂量体积限制，可以进行真正的具有MC精度的DPBN规划。 (C) 2017 年意大利医疗协会。由爱思唯尔有限公司出版。保留所有权利。
Purpose: To develop a new optimization algorithm to carry out true dose painting by numbers (DPBN) planning based on full Monte Carlo (MC) calculation.Methods: Four configurations with different clustering of the voxel values from PET data were proposed. An optimization method at the voxel level under Lineal Programming (LP) formulation was used for an inverse planning and implemented in CARMEN, an in-house Monte Carlo treatment planning system.Results: Beamlet solutions fulfilled the objectives and did not show significant differences between the different configurations. More differences were observed between the segment solutions. The plan for the dose prescription map without clustering was the better solution.Conclusions: LP optimization at voxel level without dose-volume restrictions can carry out true DPBN planning with the MC accuracy. (C) 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.