Chen Si, Wang Wenxia, Zhang Hui, Wang Jiajun
School of Electronic and Information Engineering, Soochow University, Suzhou, China.
College of Information Engineering, Henan University of Science and Technology, Luoyang, China.
J Ultrasound Med. 2017 Aug;36(8):1707-1721. doi: 10.7863/ultra.16.03008. Epub 2017 May 11.
Tissue axial strain estimation with ultrasound elastography has become a hot field in recent years. However, for keypoints tracking-based elastography algorithms, locating extrema in multimodal ultrasonic radiofrequency signals is still a challenging problem. In this paper, a new method is proposed to locate the local maxima and minima of the RF signals directly without derivation operation. This algorithm can accurately locate extrema even if disturbed peaks resulting from different noise exist. Furthermore, the new algorithm can speed up approximately 79% of the implementation process as compared with the standard cross-correlation method on the same computing platform. In addition, the elastographic signal-to-noise ratio and the contrast-to-noise ratio are also significantly improved with this new method.
近年来，利用超声弹性成像技术进行组织轴向应变估计已成为一个热门领域。然而，对于基于关键点跟踪的弹性成像算法而言，在多模态超声射频信号中定位极值点仍然是一个具有挑战性的问题。本文提出了一种新方法，可直接定位射频信号的局部最大值和最小值，而无需进行求导运算。即使存在由不同噪声导致的干扰峰值，该算法也能准确地定位极值点。此外，与在相同计算平台上的标准互相关方法相比，新算法可将实现过程的速度提高约79%。另外，使用这种新方法还能显著提高弹性成像的信噪比和对比度噪声比。
