Lung tumor tracking in fluoroscopic video based on optical flow
Lung tumor tracking in fluoroscopic video based on optical flow
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DOI：
10.1118/1.3002323
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发表时间：
2008-12-01
期刊：
影响因子：
3.8
通讯作者：
Jiang, Steve B.
中科院分区：
文献类型：
--
作者：
Xu, Qianyi;Hamilton, Russell J.;Jiang, Steve B.
用于动态多叶准直器递送的呼吸门控和肿瘤跟踪需要在治疗期间准确且实时地定位肺部肿瘤位置。由于外部替代物和内部肿瘤运动之间的相关性的分次内和分次间变化，从诸如腹部表面运动的外部替代物导出肿瘤位置可能具有很大的不确定性。植入的基准标记物可用于以足够的准确度在真实的时间内通过荧光镜跟踪肿瘤。然而，在支气管镜下植入基准点时，这可能不是一个实用的程序。在这项工作中，提出了一种方法来跟踪肺肿瘤块或相关的解剖特征投影在荧光透视图像中，而无需植入基准标记的基础上的光流算法。该算法生成跟踪目标的质心位置，并忽略肿瘤块阴影的形状变化。跟踪从初始图像帧中的分割肿瘤投影开始。然后，计算在治疗递送期间获取的该帧和所有传入帧之间的光流，作为肿瘤质心位移的初始估计。基于运动矢量的平均值将初始帧中的肿瘤轮廓转移到传入帧，并且通过使用具有小搜索范围的模板匹配算法微调轮廓位置来确定其在传入帧中的位置。通过与临床医生确定的每帧轮廓进行比较，验证跟踪结果。对于所研究的5名患者，发现95%帧的位置差异在最佳情况下小于1.4像素（类似于0.7 mm），在最差情况下小于2.8像素（类似于1.4 mm）。
Respiratory gating and tumor tracking for dynamic multileaf collimator delivery require accurate and real-time localization of the lung tumor position during treatment. Deriving tumor position from external surrogates such as abdominal surface motion may have large uncertainties due to the intra- and interfraction variations of the correlation between the external surrogates and internal tumor motion. Implanted fiducial markers can be used to track tumors fluoroscopically in real time with sufficient accuracy. However, it may not be a practical procedure when implanting fiducials bronchoscopically. In this work, a method is presented to track the lung tumor mass or relevant anatomic features projected in fluoroscopic images without implanted fiducial markers based on an optical flow algorithm. The algorithm generates the centroid position of the tracked target and ignores shape changes of the tumor mass shadow. The tracking starts with a segmented tumor projection in an initial image frame. Then, the optical flow between this and all incoming frames acquired during treatment delivery is computed as initial estimations of tumor centroid displacements. The tumor contour in the initial frame is transferred to the incoming frames based on the average of the motion vectors, and its positions in the incoming frames are determined by fine-tuning the contour positions using a template matching algorithm with a small search range. The tracking results were validated by comparing with clinician determined contours on each frame. The position difference in 95% of the frames was found to be less than 1.4 pixels (similar to 0.7 mm) in the best case and 2.8 pixels (similar to 1.4 mm) in the worst case for the five patients studied.