Subramaniam Suba R, Hon Tsz K, Georgakis Apostolos, Papadakis George
Division of Engineering, King's College London, WC2R 2LS, UK.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4028-31. doi: 10.1109/IEMBS.2010.5628095.
In ultrasound elastography, tissue axial strains are obtained through the differentiation of axial displacements. However, the application of the gradient operator amplifies the noise present in the displacement rendering unreadable axial strains. In this paper a novel denoising scheme based on repeated filtering in consecutive fractional Fourier transform domains is proposed for the accurate estimation of axial strains. The presented method generates a time-varying cutoff threshold that can accommodate the discrete non-stationarities present in the displacement signal. This is achieved by means of a filter circuit which is composed of a small number of ordinary linear low-pass filters and appropriate fractional Fourier transforms. We show that the proposed method can improve the contrast-to-noise ratio (CNR(e)) of the elastogram outperforming conventional low-pass filters.
在超声弹性成像中,通过轴向位移的微分来获取组织轴向应变。然而,梯度算子的应用会放大位移中存在的噪声,使得轴向应变难以读取。本文提出了一种基于在连续分数傅里叶变换域中重复滤波的新型去噪方案,用于准确估计轴向应变。所提出的方法生成一个时变截止阈值,该阈值能够适应位移信号中存在的离散非平稳性。这是通过一个由少量普通线性低通滤波器和适当的分数傅里叶变换组成的滤波电路来实现的。我们表明,所提出的方法能够提高弹性图的对比度噪声比(CNR(e)),优于传统的低通滤波器。