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首页> 外文期刊>Ultrasound in Medicine and Biology >A 3-D Region-Growing Motion-Tracking Method for Ultrasound Elasticity Imaging
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A 3-D Region-Growing Motion-Tracking Method for Ultrasound Elasticity Imaging

机译:用于超声弹性成像的三维区域生长运动跟踪方法

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A 3-D region-growing motion-tracking (RGMT) method for ultrasound elasticity imaging is described. This 3-D RGMT method first estimates the displacements at a sparse subset of points, called seeds; uses an objective measure to determine, among those seeds, which displacement estimates to trust; and then performs RGMT in three dimensions to estimate displacements for the remaining points in the field. During the growing process in three dimensions, the displacement estimate at one grid point is employed to guide the displacement estimation of its neighboring points using a 3-D small search region. To test this algorithm, volumetric ultrasound radiofrequency echo data were acquired from one phantom and fivein vivohuman breasts. Displacement estimates obtained with the 3-D RGMT method were compared with a published 2-D RGMT methodviamotion-compensated cross-correlation (MCCC) of pre- and post-deformation radiofrequency echo signals. For data from experiments with the phantom, the MCCC values in the entire tracking region of interest averaged approximately 0.95, and the contrast-to-noise ratios averaged 4.6 for both tracking methods. For all five patients, the average MCCC values within the region of interest obtained with the 3-D RGMT were consistently higher than those obtained with the 2-D RGMT method. These results indicate that the 3-D RGMT algorithm is able to track displacements with increased accuracy and generate higher-quality 3-D elasticity images than the 2-D RGMT method.
机译:描述了用于超声弹性成像的三维区域生长运动跟踪(RGMT)方法。该3-D RGMT方法首先估计点稀疏的点,称为种子;使用客观措施来确定这些种子,其中位移估计值得信任;然后在三维中执行RGMT以估计字段中剩余点的位移。在三维过程中的生长过程中,使用一个网格点的位移估计用于使用三维小搜索区域引导其相邻点的位移估计。为了测试该算法,从一个幻影和五个vivohuman乳房获得体积超声射频回声数据。将用3-D RGMT方法获得的位移估计与预先变形的射频回波信号的发表的2-D RGMT方法补偿互相关(MCCC)进行了比较。对于来自幻像的实验的数据,对感兴趣的整个跟踪区域中的MCCC值平均为大约0.95,并且对于两个跟踪方法,对比噪声比平均为4.6。对于所有五个患者,用3-D RGMT获得的利息区域内的平均MCCC值始终高于用2-D RGMT方法获得的患者。这些结果表明,3-D RGMT算法能够跟踪高精度提高的位移,并产生比2-D RGMT方法更高的3-D弹性图像。

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