通过明确编码呼吸信号的循环性质,可以在放射治疗中准确地预测呼吸运动,使用最小的训练数据
Andreas Renner1,2, Ingo Gulyas1,3, Martin Buschmann1
1Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
Physics and imaging in radiation oncology
|June 17, 2024
概括
这项研究表明,将呼吸周期参数纳入人工神经网络训练可以显著减少对大型数据集的需求. 这种方法可以准确地预测放射治疗的呼吸运动,即使从健康志愿者的有限数据.
科学领域:
- 医学物理 医学物理
- 放射治疗技术 放射治疗技术
- 计算生物学 计算生物学
背景情况:
- 在放射治疗中,积极的呼吸运动管理对于治疗的准确性至关重要.
- 运动监控,量化和缓解的延迟可以达到几百毫秒.
- 人工神经网络 (ANN) 可以预测呼吸运动并克服延迟问题,但通常需要广泛的训练数据.
研究的目的:
- 证明编码呼吸信号的周期性减少了ANN所需的训练数据集大小.
- 为了使运动预测模型能够使用健康志愿者的数据进行高效的训练.
主要方法:
- 长期短期记忆 (LSTM) 模型使用25名健康志愿者的70个呼吸信号进行了训练和验证.
- 在标准的LSTM模型和一个包含分解的呼吸信号参数 (相位,振幅,基线) 的模型之间进行了比较.
- 在55个独立患者的呼吸信号 (肺部和肝脏病例) 上测试了模型.
主要成果:
- 使用分解呼吸信号的模型在验证错误中实现了显著的降低,将绝对根平均平方误差 (RMSE) 从0.34mm降至0.12mm.
- 对患者数据的测试显示,500毫秒的预测地平线的平均绝对RMSE为0.16±0.11毫米.
- 这项研究成功地训练了一种运动预测模型,使用了来自健康志愿者的不到100个数据集.
结论:
- 将呼吸周期参数明确编码到训练数据中,可以实现显著更小的数据集.
- 这种方法可以在放射治疗应用中准确预测呼吸运动.
- 这些发现有助于在放射性瘤学中更有效地开发运动管理技术.
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