基于归一化互相关和粒子群算法的超声弹性成像

Jiaqi Wang, Qinghua Huang, Xin Zhang
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引用次数: 6

摘要

超声弹性成像可以提供组织硬度信息,是一种常用的医学成像技术。大多数超声弹性成像技术是基于窗口的方法。准静态超声弹性成像的关键是对压缩前后两个窗口进行相似性度量,从而计算位移。鉴于这种情况,窗口的大小对应变图像的质量有重要的影响。本文提出了一种利用粒子群算法对不同数据搜索最优窗长的合理方法来解决这一问题。利用归一化互相关法可以估计出位移图的最佳窗长。利用空间导数算子估计应变图。对固定窗长为12和50的应变图像以及采用粒子群算法的最佳窗长进行了比较。结果表明,利用粒子群算法搜索最佳窗口长度可以提高应变图像的信噪比和信噪比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ultrasound elastography based on the normalized cross-correlation and the PSO algorithm
Ultrasound elastography is a common medical imaging applied in medical applications since it can provide the tissue hardness information. Most ultrasound elastography techniques are window based methods. The key of the quasi-static ultrasound elastography is to do the similarity measure between two windows from pre- and post-compression to compute the displacement. In view of this situation, the window size has an important influence on the strain images quality. In this paper, a reasonable method utilizing PSO algorithm to search for the optimal window length for different data is brought out to solve this problem. The displacement map can be estimated with the optimal window length using the normalized cross correlation method. And a spatial derivative operator is applied to estimate the strain map. The strain images with the fixed window length 12 and 50 and the optimal window length using PSO algorithm are compared in this paper. Results show that using PSO algorithm to search for the optimal window length can improve the SNR and CNR of strain images.
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