一种基于后传神经网络的呼吸运动建模方法.
Shan Jiang1, Bowen Li1, Zhiyong Yang1
1School of Mechanical Engineering, Tianjin University, Tianjin, China.
概括
一种新的基于回向传播的神经网络的呼吸运动建模方法 (BP-RMM) 准确地跟踪呼吸期间的肺组织运动. 这种人工智能驱动的方法显示了外科导航的高精度,即使在深呼吸时也存在最小的错误.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 开发精确的呼吸运动模型对于肺部外科导航至关重要.
- 现有的方法经常与自由呼吸的复杂性作斗争,包括深呼吸和呼气.
- 精确跟踪肺组织运动对于最小化侵袭性和改善结果至关重要.
研究的目的:
- 开发和验证基于反向传播神经网络的呼吸运动建模方法 (BP-RMM).
- 为了在整个呼吸循环中精确追踪肺组织内的任意点.
- 为了提高肺部运动预测的准确性和稳定性,用于潜在的手术应用.
主要方法:
- 利用人工智能算法来处理来自四维计算机断层扫描 (4DCT) 的内部和外部呼吸数据.
- 采用了通过多项式插值来增强数据的方法,以提高数据集的稳定性.
- 构建了一个反向传播的神经网络,用于全面跟踪肺组织运动.
主要成果:
- 该BP-RMM在跟踪肺组织运动方面表现出显著的准确性.
- 深呼吸阶段的平均目标注册误差 (TRE) 在75个标记点上为1.819毫米.
- 正常呼吸阶段的TRE明显较低,最小误差为0.511毫米.
结论:
- BP-RMM是一种高度准确和强大的呼吸运动建模方法.
- 经过验证的方法显示了作为一种提高肺内外科导航的工具的前景.
- 这种人工智能驱动的方法为在手术过程中跟踪肺动态提供了更高的精度.
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