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基于光流的荧光透视视频中的肺部肿瘤跟踪

Lung tumor tracking in fluoroscopic video based on optical flow.

作者信息

Xu Qianyi, Hamilton Russell J, Schowengerdt Robert A, Alexander Brian, Jiang Steve B

机构信息

Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania, 19111, USA.

出版信息

Med Phys. 2008 Dec;35(12):5351-9. doi: 10.1118/1.3002323.

Abstract

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 (approximately 0.7 mm) in the best case and 2.8 pixels (approximately 1.4 mm) in the worst case for the five patients studied.

摘要

动态多叶准直器放疗中的呼吸门控和肿瘤追踪需要在治疗过程中对肺部肿瘤位置进行准确实时定位。通过外部替代指标(如腹部表面运动)推导肿瘤位置可能存在较大不确定性,因为外部替代指标与内部肿瘤运动之间的相关性在分次内和分次间存在变化。植入的基准标记可用于通过荧光透视实时追踪肿瘤,具有足够的准确性。然而,通过支气管镜植入基准标记可能不是一个实际可行的操作。在这项工作中,提出了一种基于光流算法在无植入基准标记的情况下追踪荧光透视图像中投影的肺部肿瘤块或相关解剖特征的方法。该算法生成被追踪目标的质心位置,而忽略肿瘤块阴影的形状变化。追踪从初始图像帧中分割出的肿瘤投影开始。然后,计算该初始帧与治疗过程中获取的所有传入帧之间的光流,作为肿瘤质心位移的初始估计。基于运动向量的平均值将初始帧中的肿瘤轮廓转移到传入帧,并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。通过与临床医生在每一帧上确定的轮廓进行比较来验证追踪结果。在所研究的五名患者中,在最佳情况下,95% 的帧中的位置差异小于1.4像素(约0.7毫米),在最坏情况下小于2.8像素(约1.4毫米)。

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