特征空间和符号相干系数融合的最小方差超声波束形成
发布时间：2018-11-15 18:07
【摘要】：为了提高医学超声成像的空间分辨率,提出一种融合了特征空间最小方差与符号相干系数的波束形成方法。首先利用最小方差法计算回波数据的协方差矩阵和加权向量;然后对协方差矩阵进行特征分解得到信号子空间,并将加权向量投影到该空间上;最后计算符号相干系数,用于优化特征空间法得到的回波信号,最终获得超声成像数据。为验证算法的有效性,对医学超声成像中常用的点目标、斑目标进行仿真,对点目标仿体和人体颈动脉组织进行超声成像实验。结果表明:所提出的方法在分辨率、对比度以及稳健性等方面都优于传统的延时叠加算法、最小方差算法、特征空间最小方差法以及特征空间与相干系数融合的方法。
[Abstract]:In order to improve the spatial resolution of medical ultrasonic imaging, a beamforming method combining the minimum variance of feature space and symbolic coherence coefficient is proposed. Firstly, the covariance matrix and weighted vector of echo data are calculated by the method of minimum variance, then the signal subspace is obtained by eigenfactorization of the covariance matrix, and the weighted vector is projected onto the space. Finally, the symbolic coherence coefficient is calculated to optimize the echo signal obtained by the eigenspace method, and finally the ultrasonic imaging data are obtained. In order to verify the effectiveness of the algorithm, the point targets and spot targets commonly used in medical ultrasound imaging were simulated, and ultrasound imaging experiments were carried out on the point target imitating body and human carotid artery tissue. The results show that the proposed method is superior to the traditional delay superposition algorithm, the minimum variance algorithm, the feature space minimum variance method and the fusion method of feature space and coherence coefficient in terms of resolution, contrast and robustness.
【作者单位】： 浙江大学生物医学工程教育部重点实验室;
【基金】：中央高校基本科研业务费专项资金(2014FZA5019,2015FZA5019) 国家科技支撑计划(2011BAI12B02)资助
【分类号】：R445.1
[Abstract]:In order to improve the spatial resolution of medical ultrasonic imaging, a beamforming method combining the minimum variance of feature space and symbolic coherence coefficient is proposed. Firstly, the covariance matrix and weighted vector of echo data are calculated by the method of minimum variance, then the signal subspace is obtained by eigenfactorization of the covariance matrix, and the weighted vector is projected onto the space. Finally, the symbolic coherence coefficient is calculated to optimize the echo signal obtained by the eigenspace method, and finally the ultrasonic imaging data are obtained. In order to verify the effectiveness of the algorithm, the point targets and spot targets commonly used in medical ultrasound imaging were simulated, and ultrasound imaging experiments were carried out on the point target imitating body and human carotid artery tissue. The results show that the proposed method is superior to the traditional delay superposition algorithm, the minimum variance algorithm, the feature space minimum variance method and the fusion method of feature space and coherence coefficient in terms of resolution, contrast and robustness.
【作者单位】： 浙江大学生物医学工程教育部重点实验室;
【基金】：中央高校基本科研业务费专项资金(2014FZA5019,2015FZA5019) 国家科技支撑计划(2011BAI12B02)资助
【分类号】：R445.1
【参考文献】
相关期刊论文 前2条
1 吴文焘;蒲杰;吕q，
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