生物医学工程研究2016，Vol.35Issue(4)：219-223,5.DOI:10.19529/j.cnki.1672-6278.2016.04.01
融合特征空间最小方差波束形成和广义相干系数的超声成像方法
Eigenspace-based Minimum Variance Beamforming Combined with General Coherence Factor for Ultrasound Beamforming
摘要
Abstract
To improve the quality of medical ultrasound imaging,a beamforming method which combines eigenspace-based mini-mum variance (ESBMV)with general coherence factor(GCF)was proposed.Firstly,minimum variance beamforming was used to obtain covariance matrix and weight vector;then the weight vector of the ESBMV was found by projecting the MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix;at the same time ,the data was transformed from array space to beamspace to calculate the general factor;in the end ,the general factor was used to optimize the results of eigenspace-based mini-mum variance beamforming.Simulations of point scatters and cyst phantom were used to verify the proposed method.The results show that the proposed method provides improved contrast,better speckle performance and more robustness than the ESBMV and ESBMV-CF beamforming method,at the expense of slightly lower resolution.关键词
医学超声成像/自适应波束形成/最小方差/特征空间/广义相干系数Key words
Medical ultrasound imaging/Adaptive beamforming/Minimum variance/Eigenspace/General coherence factor分类
医药卫生引用本文复制引用
孟德明,陈昕,戴明,陈思平..融合特征空间最小方差波束形成和广义相干系数的超声成像方法[J].生物医学工程研究,2016,35(4):219-223,5.基金项目
国家自然科学基金资助项目（61372006）。 （）
