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System and method for learning models of radiotherapy treatment plans to predict radiotherapy dose distributions

机译:用于学习放射疗法计划的模型以预测放射疗法剂量分布的系统和方法

摘要

The present disclosure relates to systems and methods for developing radiotherapy treatment plans though the use of machine learning approaches and neural network components. A neural network is trained using one or more three-dimensional medical images, one or more three-dimensional anatomy maps, and one or more dose distributions to predict a fluence map or a dose map. During training the neural network receives a predicted dose distribution determined by the neural network that is compared to an expected dose distribution. Iteratively the comparison is performed until a predetermined threshold is achieved. The trained neural network is then utilized to provide a three-dimensional dose distribution.
机译:本公开涉及通过使用机器学习方法和神经网络组件来制定放射疗法治疗计划的系统和方法。使用一个或多个三维医学图像,一个或多个三维解剖图以及一个或多个剂量分布来训练神经网络,以预测注量图或剂量图。在训练期间,神经网络接收由神经网络确定的预测剂量分布,并将其与预期剂量分布进行比较。反复进行比较,直到达到预定阈值为止。然后,利用受过训练的神经网络来提供三维剂量分布。

著录项

  • 公开/公告号AU2017324627B2

    专利类型

  • 公开/公告日2019-12-05

    原文格式PDF

  • 申请/专利权人 ELEKTA INC.;

    申请/专利号AU20170324627

  • 发明设计人 HIBBARD LYNDON S.;

    申请日2017-08-11

  • 分类号A61N5/10;G06N3/02;G06N3/04;G06N3/08;

  • 国家 AU

  • 入库时间 2022-08-21 11:12:56

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