Peng Bo, Lai Junliang, Wang Ling, Liu Dong C
College of Computer Science, Sichuan University, Chengdu, Sichuan, China School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan, China.
School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan, China.
Biomed Mater Eng. 2014;24(6):2801-10. doi: 10.3233/BME-141098.
Ultrasound elastography has been widely applied in clinical diagnosis. To produce high-quality elastograms, displacement estimation is important to generate ne displacement map from the original ratio-frequency signals. Traditional displacement estimation methods are based on the local information of signals pair, such as cross-correlation method, phase zero estimation. However, the tissue movement is nonlocal during realistic elasticity process due to the compression coming from the surface. Recently, regularized cost functions have been broadly used in ultrasound elastography. In this paper, we tested the using of analytic minimization of adaptive regularized cost function, a combination of different regularized cost functions, to correct the displacement estimation calculated by cross-correlation method directly or by lateral displacement guidance. We have demonstrated that the proposed method exhibit obvious advantages in terms of imaging quality with higher levels of elastographic signal-to-noise ratio and elastographic contrast-to-noise ratio in the simulation and phantom experiments respectively.
超声弹性成像已广泛应用于临床诊断。为了生成高质量的弹性图,位移估计对于从原始射频信号生成新的位移图很重要。传统的位移估计方法基于信号对的局部信息,如互相关法、相位零估计。然而,在实际弹性过程中,由于表面的压缩,组织运动是非局部的。最近,正则化代价函数在超声弹性成像中得到了广泛应用。在本文中,我们测试了使用自适应正则化代价函数的解析最小化,即不同正则化代价函数的组合,直接校正通过互相关法或横向位移引导计算得到的位移估计。我们已经证明,在模拟和体模实验中,所提出的方法在成像质量方面分别具有明显优势,具有更高水平的弹性图信噪比和弹性图对比噪声比。