Dpto. Fisiología Médica y Biofísica, Universidad de Sevilla, Seville, Spain; Instituto de Biomedicina de Sevilla, IBIS, Sevilla, Spain.
Instituto de Biomedicina de Sevilla, IBIS, Sevilla, Spain; Medical Radiation Physics, Stockholm University, Karolinska Institutet, Stockholm, Sweden.
Phys Med. 2017 Oct;42:339-344. doi: 10.1016/j.ejmp.2017.04.005. Epub 2017 Apr 12.
To develop a new optimization algorithm to carry out true dose painting by numbers (DPBN) planning based on full Monte Carlo (MC) calculation.
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.
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.
LP optimization at voxel level without dose-volume restrictions can carry out true DPBN planning with the MC accuracy.
开发一种新的优化算法，基于全蒙特卡罗（MC）计算实现真正的剂量画数（DPBN）计划。
提出了四种不同的 PET 数据体素值聚类配置。使用线性规划（LP）公式下的体素级优化方法进行逆规划，并在 CARMEN（一种内部蒙特卡罗治疗计划系统）中实现。
束流分配解决方案满足目标，并且在不同配置之间没有显示出显著差异。在分段解决方案之间观察到更多差异。没有聚类的剂量处方图的计划是更好的解决方案。
无剂量体积限制的体素级 LP 优化可以使用 MC 准确性进行真正的 DPBN 计划。
