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Tracking the motion trajectories of junction structures in 4D CT images of the lung

机译：在肺部4D CT图像中跟踪结点结构的运动轨迹

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摘要

Respiratory motion poses a major challenge in lung radiotherapy. Based on 4D CT images, a variety of intensity-based deformable registration techniques have been proposed to study the pulmonary motion. However, the accuracy achievable with these approaches can be sub-optimal because the deformation is defined globally in space. Therefore, the accuracy of the alignment of local structures may be compromised. In this work, we propose a novel method to detect a large collection of natural junction structures in the lung and use them as the reliable markers to track the lung motion. Specifically, detection of the junction centers and sizes is achieved by analysis of local shape profiles on one segmented image. To track the temporal trajectory of a junction, the image intensities within a small region of interest surrounding the center are selected as its signature. Under the assumption of the cyclic motion, we describe the trajectory by a closed B-spline curve and search for the control points by maximizing a metric of combined correlation coefficients. Local extrema are suppressed by improving the initial conditions using random walks from pair-wise optimizations. Several descriptors are introduced to analyze the motion trajectories. Our method was applied to 13 real 4D CT images. More than 700 junctions in each case are detected with an average positive predictive value of greater than 90%. The average tracking error between automated and manual tracking is sub-voxel and smaller than the published results using the same set of data.
机译：呼吸运动对肺部放疗提出了重大挑战。基于4D CT图像，已提出了多种基于强度的可变形配准技术来研究肺运动。但是，由于变形是在空间中全局定义的，因此使用这些方法可获得的精度可能不是最佳的。因此，局部结构的对准精度可能受到损害。在这项工作中，我们提出了一种新颖的方法来检测肺中大量的自然连接结构，并将其用作跟踪肺运动的可靠标记。具体地，通过分析一个分割图像上的局部形状轮廓来实现对接合中心和尺寸的检测。为了跟踪路口的时间轨迹，选择围绕中心的小区域内的图像强度作为其签名。在循环运动的假设下，我们通过闭合的B样条曲线描述轨迹，并通过最大化组合相关系数的度量来搜索控制点。通过使用成对优化中的随机游走改善初始条件，可以抑制局部极值。引入了几个描述符来分析运动轨迹。我们的方法被应用于13个真实的4D CT图像。在每种情况下，检测到700个以上的交叉点，其平均阳性预测值大于90％。自动跟踪和手动跟踪之间的平均跟踪误差为亚体素，并且小于使用同一组数据的已发布结果。

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