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首页> 外文期刊>Physics in medicine and biology. >Online prediction of respiratory motion: multidimensional processing with low-dimensional feature learning.
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Online prediction of respiratory motion: multidimensional processing with low-dimensional feature learning.

机译：呼吸运动的在线预测：具有低维特征学习的多维处理。

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Accurate real-time prediction of respiratory motion is desirable for effective motion management in radiotherapy for lung tumor targets. Recently, nonparametric methods have been developed and their efficacy in predicting one-dimensional respiratory-type motion has been demonstrated. To exploit the correlation among various coordinates of the moving target, it is natural to extend the 1D method to multidimensional processing. However, the amount of learning data required for such extension grows exponentially with the dimensionality of the problem, a phenomenon known as the 'curse of dimensionality'. In this study, we investigate a multidimensional prediction scheme based on kernel density estimation (KDE) in an augmented covariate-response space. To alleviate the 'curse of dimensionality', we explore the intrinsic lower dimensional manifold structure and utilize principal component analysis (PCA) to construct a proper low-dimensional feature space, where kernel density estimation is feasible with the limited training data. Interestingly, the construction of this lower dimensional representation reveals a useful decomposition of the variations in respiratory motion into the contribution from semiperiodic dynamics and that from the random noise, as it is only sensible to perform prediction with respect to the former. The dimension reduction idea proposed in this work is closely related to feature extraction used in machine learning, particularly support vector machines. This work points out a pathway in processing high-dimensional data with limited training instances, and this principle applies well beyond the problem of target-coordinate-based respiratory-based prediction. A natural extension is prediction based on image intensity directly, which we will investigate in the continuation of this work. We used 159 lung target motion traces obtained with a Synchrony respiratory tracking system. Prediction performance of the low-dimensional feature learning-based multidimensional prediction method was compared against the independent prediction method where prediction was conducted along each physical coordinate independently. Under fair setup conditions, the proposed method showed uniformly better performance, and reduced the case-wise 3D root mean squared prediction error by about 30-40%. The 90% percentile 3D error is reduced from 1.80 mm to 1.08 mm for 160 ms prediction, and 2.76 mm to 2.01 mm for 570 ms prediction. The proposed method demonstrates the most noticeable improvement in the tail of the error distribution.
机译：呼吸运动的准确实时预测对于在放射治疗中针对肺肿瘤靶标的有效运动管理是理想的。最近，已经开发出非参数方法，并且已经证明了它们在预测一维呼吸型运动中的功效。为了利用运动目标的各个坐标之间的相关性，自然可以将一维方法扩展到多维处理。但是，这种扩展所需的学习数据量随问题的维数呈指数增长，这种现象被称为“维数诅咒”。在这项研究中，我们研究了在增强协变量响应空间中基于核密度估计（KDE）的多维预测方案。为了减轻“维数的诅咒”，我们探索了固有的低维流形结构，并利用主成分分析（PCA）来构建适当的低维特征空间，在有限的训练数据下，核密度估计是可行的。有趣的是，这种较低维表示的构造揭示了呼吸运动变化对半周期动力学和随机噪声的贡献的有用分解，因为仅对前者进行预测是明智的。这项工作中提出的降维思想与机器学习（尤其是支持向量机）中使用的特征提取紧密相关。这项工作指出了在训练实例有限的情况下处理高维数据的途径，并且该原理远远超出了基于目标坐标的基于呼吸的预测问题。一个自然的扩展是直接基于图像强度的预测，我们将在后续工作中进行研究。我们使用了通过同步呼吸跟踪系统获得的159个肺目标运动轨迹。将基于低维特征学习的多维预测方法的预测性能与独立预测方法进行了比较，在独立预测方法中，预测是沿每个物理坐标独立进行的。在公平的设置条件下，所提出的方法表现出一致的更好的性能，并减少了约30-40％的逐例3D均方根预测误差。对于160 ms的预测，将90％的3D误差从1.80 mm减小到1.08 mm，对于570 ms的预测，误差从2.76 mm减小到2.01 mm。所提出的方法在误差分布的尾部显示出最明显的改进。

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