超声成像中基于特征空间的前后向最小方差波束形成

针对最小方差(MV)波束形成在算法稳健性和超声成像对比度方面存在的缺点,提出一种将特征空间法和前后向空间平滑法融合用于最小方差波束形成的超声成像方法。首先用前后向空间平滑取代传统的前向空间平滑,得到更精确的协方差矩阵;然后计算最优加权向量,并将该向量投影到由协方差矩阵特征空间构造的信号子空间中;最后利用投影所获得的向量与阵元数据进行运算得到成像回波数据。为了验证算法的有效性,对医学成像中常用的点目标和斑目标进行了成像实验。仿真结果表明:所提出的方法不依赖于对角加载参数的选取,在保持MV算法高分辨率的同时,还有效提高图像的对比度和算法的稳健性。

Eigenspace-based forward-backward minimum variance beamforming applied to ultrasound imaging

In order to compensate for the limitation of minimum variance (MV) beamforming in the aspects of robustness and contrast, an ultrasound imaging method based on eigenspace-based forward-backward minimum variance beamforming is presented in this paper. First, forward-backward (FB) spatial averaging, instead of the conventional forward-only spatial averaging is used to obtain a more accurate covarianee matrix; and then the calculated optimum weight vector is projected onto a signal subspace constructed from the eigenstructure of the covariance matrix; in the end, the obtained vector and the aperture data are calculated to obtain echo data. The experiments based on point pattern and speckle pattern are used to verify the proposed method. The experimental results show that the proposed method is less dependent on the choice of the amount of diagonal loading and enhances the contrast and robustness while the high resolution of the MV beamforming is retained.