Forecasting respiratory motion with accurate online support vector regression (SVRpred)
Forecasting respiratory motion with accurate online support vector regression (SVRpred)
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DOI：
10.1007/s11548-009-0355-5
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发表时间：
2009-09-01
影响因子：
3
通讯作者：
Schweikard, Achim
中科院分区：
文献类型：
--
作者：
Ernst, Floris;Schweikard, Achim
目的为了在图像引导的机器人放射外科手术中准确地提供辐射，需要高精度的预测算法。提出了一种新的预测方法，并对其进行了评价。材料与方法开发了一种基于支持向量回归(SVR)的新预测方法SVRpred，并进行了测试。模拟人类呼吸运动的计算机生成的数据被用于实验室测试，预测范围为150ms。随后，使用CyberKnife(R)对该算法在实际放射外科治疗期间记录的呼吸运动信号进行了评估。结果SVRpred算法在真实数据(分别高出15个和16个百分点)和噪声污染的模拟数据(分别高出13个和48个百分点)上明显优于MULIN和wLMS算法。仅在无噪声的人工数据上，SVRpred算法的性能确实优于MULIN算法，但不如wLMS算法。结论该算法是一种可行的预测人体呼吸运动信号的工具，其性能明显优于以往的算法。唯一的缺点是计算复杂度高，预测速度慢。需要高性能的计算机来使用该算法对高分辨率采样的信号进行实时预测。
Object To accurately deliver radiation in image-guided robotic radiosurgery, highly precise prediction algorithms are required. A new prediction method is presented and evaluated.Materials and methods SVRpred, a new prediction method based on support vector regression (SVR), has been developed and tested. Computer-generated data mimicking human respiratory motion with a prediction horizon of 150 ms was used for lab tests. The algorithm was subsequently evaluated on a respiratory motion signal recorded during actual radiosurgical treatment using the CyberKnife (R). The algorithm's performance was compared to the MULIN prediction methods and Wavelet-based multi scale autoregression (wLMS).Results The SVRpred algorithm clearly outperformed both the MULIN and the wLMS algorithms on both real (by 15 and 16 percentage points, respectively) and noise-corrupted simulated data (by 13 and 48 percentage points, respectively). Only on noise-free artificial data, the SVRpred algorithm did perform as well as the MULIN algorithms but not as well as the wLMS algorithm.Conclusion This new algorithm is a feasible tool for the prediction of human respiratory motion signals significantly outperforming previous algorithms. The only drawback is the high computational complexity and the resulting slow prediction speed. High performance computers will be needed to use the algorithm in live prediction of signals sampled at a high resolution.