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使用深度学习和计划优化与有限元控制的自动剂量预测,用于强度调制辐射治疗.

Yichao Shen1, Xingni Tang1, Sara Lin2

  • 1Department of Radiation Oncology, Taizhou Hospital, Taizhou, Zhejiang, People's Republic of China.

Medical physics
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PubMed
概括

基于有限元素的优化改善了强度调制放射治疗 (IMRT) 计划的深度学习剂量预测. 这种自动化方法可以降低有风险的器官剂量,同时保持计划目标体积覆盖率.

关键词:
自动强度调节辐射疗法规划深度学习的剂量预测.有限元素的元素是有限的.

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科学领域:

  • 医学物理 医学物理
  • 辐射疗法 辐射疗法
  • 人工智能的人工智能

背景情况:

  • 深度学习 (DL) 模型模拟voxel剂量,用于自动化放射治疗计划.
  • 使用voxel剂量功能的计划优化需要进一步调查.

研究的目的:

  • 评估直接优化策略使用有限元素 (FE) 遵循DL剂量预测强度调节放射治疗 (IMRT) 规划.

主要方法:

  • 一个双UNet DL模型预测了220名宫癌患者的剂量分配.
  • 风险器官 (OAR) 和身体区域内的有限元素 (FE) 用于定义优化目标.
  • 一个两步优化过程限制了OAR和身体避开区域,与直接DL预测进行比较.

主要成果:

  • 基于FE的优化显著降低了OAR (膀,直肠,小肠,股骨头) 的平均剂量,同时确保PTV的均性和一致性.
  • 针对FE优化的计划 (方法1) 与关键OAR的直接DL预测计划 (方法2) 相比,产生了较低的平均剂量.

结论:

  • 基于有限元素的直接优化有效降低了OAR剂量,并在DL剂量预测后确保了足够的PTV覆盖.
  • 这种自动化方法提供了快速,无需手动调整的计划优化,特别有利于低剂量地区.