Graduate School of Engineering, Tohoku University, Sendai, Japan.
Phys Med Biol. 2018 Sep 10;63(18):185007. doi: 10.1088/1361-6560/aada71.
In radiation therapy, for accurate radiation dose delivery to a target tumor and reduction of the extra exposure of normal tissues, real-time tumor tracking is typically an important technique in lung cancer treatment since lung tumors move with patients' respiration. To observe a tumor motion in real time, x-ray fluoroscopic devices can be employed, and various tracking techniques have been proposed to track tumors. However, development of a fast and accurate tracking method for clinical use is still a challenging task since the obscured image of the tumor can cause decreased tracking accuracy and can result in additional processing time for remedying the accuracy. In this study, a new key-point-based tumor tracking method, which is sufficiently fast and accurate, is presented. Given an x-ray image sequence, the proposed method employs a difference-of-Gaussians filtering technique to detect key points in the tumor region of the first frame which are robust against noise and outliers in the subsequent frames. In the subsequent frames, these key points are tracked using a fast optical flow technique, and tumor motion is estimated via their movement. To evaluate the performance, the proposed method has been tested on several clinical kV and MV x-ray image sequences. The experimental results showed that the average of the root mean square errors of tracking were [Formula: see text] and [Formula: see text] for kV and MV x-ray image sequences, respectively. This tracking performance was more accurate than previous tracking methods. In addition, the average processing times for each frame were [Formula: see text] and [Formula: see text] for kV and MV image sequences, respectively, and the proposed method was faster than previous methods as well as shorter than frame acquisition interval. Therefore, the proposed method has the potential for both highly accurate and fast tumor tracking in clinical applications.
在放射治疗中，为了将精确的辐射剂量输送到目标肿瘤，并减少正常组织的额外暴露，实时肿瘤跟踪通常是肺癌治疗中的一项重要技术，因为肺肿瘤会随患者的呼吸而移动。为了实时观察肿瘤的运动，可以使用 X 射线透视设备，并且已经提出了各种跟踪技术来跟踪肿瘤。然而，由于肿瘤的模糊图像会导致跟踪精度降低，并可能导致额外的处理时间来纠正精度，因此开发一种快速准确的跟踪方法用于临床仍然是一项具有挑战性的任务。在这项研究中，提出了一种新的基于关键点的肿瘤跟踪方法，该方法足够快速和准确。给定一个 X 射线图像序列，该方法采用高斯差分滤波技术在第一帧中检测肿瘤区域中的关键点，这些关键点对后续帧中的噪声和异常值具有鲁棒性。在后续帧中，使用快速光流技术跟踪这些关键点，并通过它们的运动估计肿瘤运动。为了评估性能，已经在几个临床千伏和兆伏 X 射线图像序列上测试了该方法。实验结果表明，跟踪的均方根误差的平均值分别为[公式：见正文]和[公式：见正文]，用于千伏和兆伏 X 射线图像序列。这种跟踪性能比以前的跟踪方法更准确。此外，对于每个帧的平均处理时间分别为[公式：见正文]和[公式：见正文]，用于千伏和兆伏图像序列，并且该方法比以前的方法更快，并且短于帧采集间隔。因此，该方法有可能在临床应用中实现高度准确和快速的肿瘤跟踪。
