Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan.
Med Phys. 2013 Jan;40(1):011715. doi: 10.1118/1.4771931.
To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT).
Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons.
The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The standard deviations of the results from the 6 researchers are 2.3 and 2.6 pixels. The proposed framework takes about 128 ms to detect four markers in the first MV images and about 23 ms to track these markers in each of the subsequent images.
The unified framework for tracking of multiple markers presented here can achieve marker detection accuracy similar to manual detection even in low-contrast cine-MV images. It can cope with shape deformations of fiducial markers at different gantry angles. The fast processing speed reduces the image processing portion of the system latency, therefore can improve the performance of real-time motion compensation.
开发一种用于图像引导放射治疗（IGRT）的低对比度电影 MV 患者图像中植入放射性标记物实时自动跟踪的方法。
在 IGRT 中，最近使用放疗束线 MV 图像进行分次内运动跟踪引起了关注，因为不会引入额外的成像剂量。然而，MV 图像的对比度比千伏（kV）图像低得多，因此，MV 图像中标记物的稳健和自动检测是前提。以前的标记物检测方法都是基于模板匹配或其衍生方法。模板匹配需要匹配因植入和投影角度不同而发生显著变化的物体形状。虽然这些方法需要大量的模板来覆盖各种情况，但由于它们的方法都需要在感兴趣区域进行穷举搜索，因此通常被迫使用较少的模板来减少计算负载。作者通过协同使用现代但经过充分验证的计算机视觉和人工智能技术解决了这个问题；具体来说，作者使用判别分析进行初始化，并使用均值漂移特征空间分析进行连续跟踪，利用连续帧之间的时间相关性来避免穷举搜索，从而可以在初始化后进行更复杂的检测，从而提高准确性，然后在初始化后进行超快的连续跟踪。该方法使用来自两位前列腺 IGRT 患者的 1149 个电影 MV 图像进行评估和验证，并与六位研究人员的手动标记物检测结果进行比较。将手动检测结果的平均值作为比较的基准。
对于两位患者，我们的实时自动跟踪方法从基准的平均均方根误差分别为 1.9 和 2.1 像素（0.26 像素/像素）。六位研究人员结果的标准差分别为 2.3 和 2.6 像素。该框架在第一幅 MV 图像中检测四个标记物大约需要 128 毫秒，在后续每幅图像中跟踪这些标记物大约需要 23 毫秒。
这里提出的多标记物跟踪统一框架可以在低对比度电影 MV 图像中实现与手动检测相似的标记物检测精度。它可以处理不同机架角度下基准标记物的形状变形。快速的处理速度减少了系统延迟中的图像处理部分，从而可以提高实时运动补偿的性能。
