Abstract: To improve the quality of medical ultrasound imaging, a beamforming method which combines Eigen Space-Based Minimum Variance（ESBMV） with  Spatio-Temporally Coherence Factor（STCF） is proposed. Firstly, minimum variance beamforming is used to obtain covariance matrix and weight vector; then the weight vector of the ESBMV is found by projecting the  MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix; at the same time, the spatio-temporally method is used to calculate the coherence  factor; in the end, the spatio-temporally coherence factor is used to optimize the results of eigenspace-based minimum variance beamforming. Simulations of point scatters and cyst phantom are 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.

Key words: ultrasound imaging, adaptive beamforming, minimum variance, eigenspace, Spatio-Temporally Coherence Factor（STCF）

摘要： 为了进一步提高超声成像的质量，提出了融合特征空间最小波束形成和空时相干系数的成像方法。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信号子空间，得到特征空间方法的加权向量；同时采用空时平滑方法计算相干系数，最后用空时相干系数作为加权系数对特征空间最小方差波束形成的结果进行优化。为了验证算法的有效性，对医学成像上常用的点目标和斑目标进行了成像，仿真实验结果表明：与特征空间最小方差算法和融合特征空间与相干系数的算法相比，提出的方法提高了对比度和稳健性，其代价是略微降低了成像分辨率。

关键词: 超声成像, 自适应波束形成, 最小方差, 特征空间, 空时相干系数