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Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

机译：预测呼吸运动的高维状态：歧管学习方法

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摘要

The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.
机译：图像引导放射治疗中的高维成像系统的发展为实时全体积运动监测的最终目标提供了重要的途径。辐射处理期间的有效运动管理通常需要预测来解释系统延迟和额外的信号/图像处理时间。预测高维呼吸运动是挑战，由于运动模式的复杂性与维度的诅咒相结合。诸如PCA之类的线性尺寸减小方法已被用于构造来自高维数据的线性子空间，然后在低维子空间上有效预测。在这项研究中，我们将这样的基本原理扩展到更通用的歧管，并提出了一种利用歧管学习的高维运动预测框架，其允许人们与具有可比尺寸的线性方法相比学习更多的描述性功能。具体地，核PCA用于构造适当的低维特征歧管，其中可以执行准确和有效的预测。定点迭代预图像估计方法用于恢复原始状态空间中的预测值。我们评估并与基于PCA的方法进行了评估并与基于PCA的方法从3D摄影测量系统捕获的点云重建的水平集表面上进行了比较。在根均方向误差方面评估预测精度。与基于PCA的方法相比，我们所提出的方法对于200ms和600毫秒的寻找长度来实现一致的更高预测精度（亚毫米），并且性能增益具有统计学意义。

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