Zhang Xiaoyong, Homma Noriyasu, Ichiji Kei, Takai Yoshihiro, Yoshizawa Makoto
Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai 980-8579, Japan.
Research Institute of Electrical Communication, Tohoku University, Sendai 980-8579, Japan.
Med Phys. 2015 May;42(5):2510-23. doi: 10.1118/1.4918578.
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.
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.
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.
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.
开发一种无标记跟踪算法，用于在兆伏（MV）电子射野影像装置（EPID）图像中跟踪肿瘤边界，以进行图像引导放射治疗。
开发了一种基于水平集方法（LSM）的算法来跟踪EPID图像序列中的肿瘤边界。给定一个EPID图像序列，在第一帧中手动指定一条初始曲线。在区域可缩放能量拟合函数的驱动下，初始曲线自动朝着肿瘤边界演化，并在能量函数达到最小值时停留在所需边界上。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，并重新使用LSM来检测后续帧中的肿瘤边界，从而无需用户干预即可继续跟踪处理。该跟踪算法在三个图像数据集上进行了测试，包括一个4D体模EPID图像序列、四个具有不同噪声水平的数字可变形体模图像序列以及四个在肺癌治疗中采集的临床EPID图像序列。基于两个指标评估跟踪准确性：跟踪结果与真实情况之间的质心定位误差（CLE）和体积重叠指数（VOI）。
对于4D体模图像序列，CLE为0.23±0.20毫米，VOI为95.6%±0.2%。对于数字体模图像序列，总CLE和VOI分别为0.11±0.08毫米和96.7%±0.7%。此外，对于临床EPID图像序列，所提出的算法在CLE方面达到0.32±0.77毫米，在VOI方面达到72.1%±5.5%。这些结果证明了作者所提出方法在EPID图像中的肿瘤定位和边界跟踪方面的有效性。此外，与两种现有的跟踪算法相比，所提出的方法在肿瘤定位方面实现了更高的准确性。
在本文中，作者提出了一项利用基于LSM的算法在EPID图像中跟踪肿瘤边界的可行性研究。在体模和临床EPID图像上进行的实验结果证明了该跟踪算法对可见肿瘤靶标的有效性。与先前的跟踪方法相比，作者的算法有潜力提高放射治疗中的跟踪准确性。此外，照射野内的实时肿瘤边界信息对于进一步的应用（如自适应束流输送、剂量评估）可能会很有用。
