Fluoroscopic tumor tracking for image-guided lung cancer radiotherapy
Fluoroscopic tumor tracking for image-guided lung cancer radiotherapy
复制标题
用于图像引导肺癌放射治疗的荧光镜肿瘤追踪
DOI：
10.1088/0031-9155/54/4/011
复制
发表时间：
2009-02-21
影响因子：
3.5
通讯作者：
Jiang, Steve B.
中科院分区：
文献类型：
--
作者：
Lin, Tong;Cervino, Laura I.;Jiang, Steve B.
真实的实时准确的肺部肿瘤跟踪是肺癌图像引导放射治疗的关键。现有的肺肿瘤跟踪方法可以粗略地分为三类：（1）从外部替代物导出肿瘤位置;（2）以荧光镜或电磁方式跟踪植入的基准标记;（3）在没有植入的基准标记的情况下以荧光镜跟踪肺肿瘤。第一种方法的准确性不足，而第二种方法由于气胸的风险可能不会被广泛接受。以往的无标记跟踪研究主要基于模板匹配方法，当肿瘤边界不清晰时，模板匹配方法可能会失效。在本文中，我们提出了一种新的无标记的肿瘤跟踪算法，它利用肿瘤的位置和替代解剖特征之间的相关性的图像。代理功能的位置不直接跟踪，相反，我们使用主成分分析包含它们的感兴趣的区域，以获得其运动模式的参数表示。然后，可以通过回归从替代物的参数表示预测肿瘤位置。在这项研究中，四个回归方法进行了测试：线性和二次多项式回归，人工神经网络（ANN）和支持向量机（SVM）。基于10例肺癌患者的透视序列的实验结果表明，所提出的跟踪算法的平均跟踪误差为2.1像素，在95%置信水平下的最大误差为4.6像素（像素大小约为0.5 mm）。
Accurate lung tumor tracking in real time is a keystone to image-guided radiotherapy of lung cancers. Existing lung tumor tracking approaches can be roughly grouped into three categories: (1) deriving tumor position from external surrogates; (2) tracking implanted fiducial markers fluoroscopically or electromagnetically; (3) fluoroscopically tracking lung tumor without implanted fiducial markers. The first approach suffers from insufficient accuracy, while the second may not be widely accepted due to the risk of pneumothorax. Previous studies in fluoroscopic markerless tracking are mainly based on template matching methods, which may fail when the tumor boundary is unclear in fluoroscopic images. In this paper we propose a novel markerless tumor tracking algorithm, which employs the correlation between the tumor position and surrogate anatomic features in the image. The positions of the surrogate features are not directly tracked; instead, we use principal component analysis of regions of interest containing them to obtain parametric representations of their motion patterns. Then, the tumor position can be predicted from the parametric representations of surrogates through regression. Four regression methods were tested in this study: linear and two-degree polynomial regression, artificial neural network (ANN) and support vector machine (SVM). The experimental results based on fluoroscopic sequences of ten lung cancer patients demonstrate a mean tracking error of 2.1 pixels and a maximum error at a 95% confidence level of 4.6 pixels (pixel size is about 0.5 mm) for the proposed tracking algorithm.