Tracking tumor boundary in MV-EPID images without implanted markers: A feasibility study
Tracking tumor boundary in MV-EPID images without implanted markers: A feasibility study
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DOI：
10.1118/1.4918578
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发表时间：
2015-05-01
期刊：
影响因子：
3.8
通讯作者：
Yoshizawa, Makoto
中科院分区：
文献类型：
--
作者：
Zhang, Xiaoyong;Homma, Noriyasu;Yoshizawa, Makoto
目的：建立一种无标记跟踪算法，用于图像引导放射治疗的巨压电子门静脉成像装置（EPID）图像中肿瘤边界的跟踪。方法：提出一种基于水平集方法（LSM）的EPID图像序列肿瘤边界跟踪算法。给定EPID图像序列，在第一帧中手动指定初始曲线。在区域可扩展的能量拟合函数的驱动下，初始曲线自动向肿瘤边界演化，并在能量函数达到最小值时停在所需的边界上。对于后续帧，跟踪算法利用前一帧的跟踪结果更新初始曲线，并重用LSM检测后续帧中的肿瘤边界，从而在没有用户干预的情况下继续跟踪处理。在3个图像数据集上对跟踪算法进行了测试，包括一个4维幻影EPID图像序列、4个不同噪声水平的数字变形幻影图像序列和4个肺癌治疗中获得的临床EPID图像序列。跟踪精度基于两个指标：质心定位误差（CLE）和跟踪结果与地面真实度之间的体积重叠指数（VOI）。结果：对于4维幻像序列，CLE为0.23 +/- 0.20 mm， VOI为95.6% +/- 0.2%。对于数字幻影图像序列，总CLE和VOI分别为0.11 +/- 0.08 mm和96.7% +/- 0.7%。此外，对于临床EPID图像序列，本文算法在CLE中达到0.32 +/- 0.77 mm，在VOI中达到72.1% +/- 5.5%。这些结果证明了作者提出的方法在EPID图像的肿瘤定位和边界跟踪方面的有效性。此外，与现有的两种跟踪算法相比，本文方法在肿瘤定位方面达到了更高的精度。结论：本文提出了一种基于lsm的EPID图像肿瘤边界跟踪算法的可行性研究。在幻影和临床EPID图像上的实验结果表明，该算法对可见肿瘤目标的跟踪是有效的。与以往的跟踪方法相比，作者的算法具有提高放射治疗跟踪精度的潜力。此外，放射场内的实时肿瘤边界信息将潜在地用于进一步的应用，例如自适应光束传递，剂量评估。(C) 2015年美国医学物理学家协会。
Purpose: To develop a markerless tracking algorithm to track the tumor boundary in megavoltage (MV)-electronic portal imaging device (EPID) images for image-guided radiation therapy.Methods: A level set method (LSM)-based algorithm is developed to track tumor boundary in EPID image sequences. Given an EPID image sequence, an initial curve is manually specified in the first frame. Driven by a region-scalable energy fitting function, the initial curve automatically evolves toward the tumor boundary and stops on the desired boundary while the energy function reaches its minimum. For the subsequent frames, the tracking algorithm updates the initial curve by using the tracking result in the previous frame and reuses the LSM to detect the tumor boundary in the subsequent frame so that the tracking processing can be continued without user intervention. The tracking algorithm is tested on three image datasets, including a 4-D phantom EPID image sequence, four digitally deformable phantom image sequences with different noise levels, and four clinical EPID image sequences acquired in lung cancer treatment. The tracking accuracy is evaluated based on two metrics: centroid localization error (CLE) and volume overlap index (VOI) between the tracking result and the ground truth.Results: For the 4-D phantom image sequence, the CLE is 0.23 +/- 0.20 mm, and VOI is 95.6% +/- 0.2%. For the digital phantom image sequences, the total CLE and VOI are 0.11 +/- 0.08 mm and 96.7% +/- 0.7%, respectively. In addition, for the clinical EPID image sequences, the proposed algorithm achieves 0.32 +/- 0.77 mm in the CLE and 72.1% +/- 5.5% in the VOI. These results demonstrate the effectiveness of the authors' proposed method both in tumor localization and boundary tracking in EPID images. In addition, compared with two existing tracking algorithms, the proposed method achieves a higher accuracy in tumor localization.Conclusions: In this paper, the authors presented a feasibility study of tracking tumor boundary in EPID images by using a LSM-based algorithm. Experimental results conducted on phantom and clinical EPID images demonstrated the effectiveness of the tracking algorithm for visible tumor target. Compared with previous tracking methods, the authors' algorithm has the potential to improve the tracking accuracy in radiation therapy. In addition, real-time tumor boundary information within the irradiation field will be potentially useful for further applications, such as adaptive beam delivery, dose evaluation. (C) 2015 American Association of Physicists in Medicine.