Three-dimensional (3-D) ultrasound elastography can provide 3-D tissue stiffness information that may be used during clinical diagnoses. In the framework of strain elastography, motion tracking plays an important role. In this study, an improved 3-D region-growing motion tracking (RGMT) algorithm based on a concept of exterior boundary points was developed. In principle, the proposed method first determines displacement at some seed points by strictly checking the local correlation and continuity in the neighborhood of those seeds. Subsequent displacement estimation is then conducted from these initial seeds to obtain displacements associated with other locations. This RGMT algorithm is designed to use more known information-including displacements and correlation values of all known-displacement neighboring points-to estimate the displacement of an unknown-displacement point, whereas previous RGMT methods employed information from only one such point. The algorithm was tested on 3-D ultrasound volumetric data acquired from a simulation, a tissue-mimicking phantom, and five human subjects. Motion-compensated cross correlations (MCCCs), strain contrast, and displacement Laplacian values (representing smoothness of an estimated displacement field) were calculated and used to evaluate the merits of the proposed RGMT method. Compared with a previously published RGMT method, the results show that the proposed RGMT method can provide smaller displacement errors and smoother displacements and improve strain contrast while maintaining reasonably high MCCC values, indicating good motion tracking quality. The proposed method is also computationally more efficient. In summary, our preliminary results demonstrated that the proposed RGMT algorithm is capable of obtaining high-quality 3-D strain elastographic data using modified clinical equipment.

中文翻译：

---

使用3D超声弹性成像的更多先验信息的改进的区域生长运动跟踪方法。

三维（3-D）超声弹性成像可以提供可在临床诊断中使用的3-D组织硬度信息。在应变弹性成像的框架中，运动跟踪起着重要的作用。在这项研究中，基于外部边界点的概念开发了一种改进的3-D区域生长运动跟踪（RGMT）算法。原则上，所提出的方法首先通过严格检查那些种子附近的局部相关性和连续性来确定某些种子点的位移。然后从这些初始种子进行后续位移估计，以获得与其他位置相关的位移。该RGMT算法设计为使用更多已知信息（包括所有已知位移相邻点的位移和相关值）来估计未知位移点的位移，而以前的RGMT方法仅使用一个这样的点的信息。该算法在从模拟，组织模拟体模和五个人类受试者获得的3-D超声体积数据上进行了测试。计算了运动补偿互相关（MCCC），应变对比度和位移拉普拉斯值（代表估计的位移场的平滑度），并将其用于评估所提出的RGMT方法的优点。与先前发布的RGMT方法相比，结果表明，所提出的RGMT方法可以提供较小的位移误差和更平滑的位移，并改善应变对比度，同时保持合理的高MCCC值，表明运动跟踪质量良好。所提出的方法在计算上也更加有效。总而言之，我们的初步结果表明，提出的RGMT算法能够使用改进的临床设备获得高质量的3D应变弹性成像数据。