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一种用于超声应变成像的改进二维多分辨率混合算法。

A Modified 2D Multiresolution Hybrid Algorithm for Ultrasound Strain Imaging.

机构信息

Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China.

University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Biomed Res Int. 2017;2017:2856716. doi: 10.1155/2017/2856716. Epub 2017 Dec 20.

Abstract

Ultrasound elastography is an imaging modality to evaluate elastic properties of soft tissue. Recently, 1D quasi-static elastography method has been commercialized by some companies. However, its performance is still limited on high strain level. In order to improve the precision of estimation during high compression, some algorithms have been proposed to expand the 1D window to a 2D window for avoiding the side-slipping. But they are usually more computationally expensive. In this paper, we proposed a modified 2D multiresolution hybrid method for displacement estimation, which can offer an efficient strain imaging with stable and accurate results. A FEM phantom with a stiffer circular inclusion is simulated for testing the algorithm. The elastographic contrast-to-noise rate (CNRe) is calculated for quantitatively comparing the performance of the proposed algorithm with conventional 1D elastography using phase zero estimation and the 1D elastography using downsampled (d-s) baseband signals. Results show that the proposed method is robust and performs similarly as other algorithms in low strain but is superior when high level strain is applied. Particularly, the CNRe of our algorithm is 15 times higher than original method under 4% strain level. Furthermore, the execution time of our algorithm is five times faster than other algorithms.

摘要

超声弹性成像是一种评估软组织弹性特性的成像方式。最近,一些公司已经将一维准静态弹性成像方法商业化。然而,其性能在高应变水平下仍然有限。为了在高压缩下提高估计的精度,已经提出了一些算法来将一维窗口扩展到二维窗口,以避免侧面滑动。但它们通常计算成本更高。在本文中,我们提出了一种改进的二维多分辨率混合方法用于位移估计,该方法可以提供高效的应变成像,具有稳定和准确的结果。使用有限元模拟了一个具有更硬圆形包含物的仿体,以测试该算法。计算弹性对比度噪声比(CNRe)用于定量比较使用相位零估计的传统一维弹性成像和使用下采样(d-s)基带信号的一维弹性成像的性能。结果表明,该方法在低应变下稳健且性能与其他算法相似,但在应用高应变时表现更好。特别是,在 4%应变水平下,我们的算法的 CNRe 比原始方法高 15 倍。此外,我们的算法的执行时间比其他算法快五倍。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea11/5751392/ab258643f2c6/BMRI2017-2856716.001.jpg

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