首页> 外文期刊>Ultrasound in Medicine and Biology >Speckle classification for sensorless freehand 3-D ultrasound.
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Speckle classification for sensorless freehand 3-D ultrasound.

机译:无传感器徒手3D超声的斑点分类。

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摘要

Despite being a valuable tool for volume measurement and the analysis of complex geometry, the need for an external position sensor is holding up the clinical exploitation of freehand 3-D ultrasound. Some sensorless systems have been developed, using speckle decorrelation for out-of-plane distance estimation, but their accuracy is still not as good as that of sensor-based systems. Here, we examine the widely held belief that accuracy can be improved by limiting the distance measurements to patches of ultrasound data containing fully developed speckle. Without speckle detection, we observe that scan separation is systematically underestimated by 33.1% in biological tissue. We describe a number of speckle detectors and show that they reduce the underestimate to about 25%. We conclude that speckle classification can improve the quality of distance estimation, but not sufficiently to achieve accurate, metric reconstruction of the insonified volume.
机译:尽管它是用于体积测量和复杂几何形状分析的有价值的工具,但对外部位置传感器的需求却阻碍了徒手3D超声的临床开发。已经开发了一些无传感器系统,使用斑点去相关来进行平面外距离估计,但是它们的精度仍然不如基于传感器的系统。在这里,我们检查了一种广泛持有的信念,即可以通过将距离测量限制为包含完全散斑的超声数据斑块来提高精度。没有斑点检测,我们观察到在生物组织中扫描分离被系统低估了33.1%。我们描述了许多斑点检测器,并表明它们将低估率降低至约25%。我们得出的结论是,散斑分类可以提高距离估计的质量,但不足以实现对声波量的准确,度量重建。

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