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R-MFE-TCN:在呼吸运动期间,身体表面和瘤之间的相关性预测模型

Xuehu Wang^1,2, Chang Yang^1,2, Ziqi Liu^1,2

¹College of Electronic and Information Engineering, Hebei University, Baoding, China.

Medical physics

|May 27, 2024

概括

这项研究引入了一种新的模型,用于预测手术期间的呼吸运动,提高了微创治疗的准确性,例如射频剥离 (RFA). 该R-MFE-TCN增强了实时瘤跟踪和手术精度.

科学领域:

医学成像和图像导向治疗
人工智能在医学中的应用
手术导航系统手术导航系统

背景情况:

至少侵入性治疗,如2D CT图像导向射频切除 (RFA) 肝脏瘤的静态成像和瘤运动由于呼吸受到限制.
在自由呼吸条件下准确的实时瘤定位是图像引导手术的关键挑战.

研究的目的:

为准确的呼吸相关性预测提出里曼和多变量特征增强时间卷积网络 (R-MFE-TCN).
为了增强混合现实手术辅助系统,具有精确的呼吸运动预测能力.

主要方法:

利用以呼吸为导向的里曼信息增强策略来增加数据集多样性.
开发了一个多变量特征增强 (MFE) 模块和双通道网络,以保留关键的呼吸数据和特征.
雇员多头自我注意力用于周期性呼吸道峰值到谷间信息和PSO用于超参数优化.

主要成果:

R-MFE-TCN在患者数据集的预测准确性和稳定性方面取得了显著的改进.
实现了长窗口预测的低延迟偏差,平均RMSE为0.0453毫米,MAE为0.0361毫米,400毫秒.
在呼吸系相关性预测准确度方面表现优于现有研究方法.

结论:

R-MFE-TCN模型有效地解决了手术辅助中的呼吸相关性预测挑战.
拟议的方法符合严格的准确性要求,用于在临床环境中预测呼吸延迟.
在需要精确的呼吸运动跟踪的各种临床场景中,R-MFE-TCN具有更广泛的应用潜力.

关键词:

相关性预测模型的相关性.深度学习是一种深度学习.无线电频率剥离方式瘤是一个瘤.

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