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A fast minimum variance beamforming method using principal component analysis

机译：基于主成分分析的快速最小方差波束形成方法

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摘要

Minimum variance (MV) beamforming has been studied for improving the performance of a diagnostic ultrasound imaging system. However, it is not easy for the MV beamforming to be implemented in a real-time ultrasound imaging system because of the enormous amount of computation time associated with the covariance matrix inversion. In this paper, to address this problem, we propose a new fast MV beamforming method that almost optimally approximates the MV beamforming while reducing the computational complexity greatly through dimensionality reduction using principal component analysis (PCA). The principal components are estimated offline from pre-calculated conventional MV weights. Thus, the proposed method does not directly calculate the MV weights but approximates them by a linear combination of a few selected dominant principal components. The combinational weights are calculated in almost the same way as in MV beamforming, but in the transformed domain of beamformer input signal by the PCA, where the dimension of the transformed covariance matrix is identical to the number of some selected principal component vectors. Both computer simulation and experiment were carried out to verify the effectiveness of the proposed method with echo signals from simulation as well as phantom and in vivo experiments. It is confirmed that our method can reduce the dimension of the covariance matrix down to as low as 2 ?? 2 while maintaining the good image quality of MV beamforming.
机译：为了改善诊断超声成像系统的性能，已经研究了最小方差（MV）波束成形。然而，由于与协方差矩阵求逆相关的大量计算时间，因此在实时超声成像系统中实现MV波束成形并不容易。在本文中，为了解决这个问题，我们提出了一种新的快速MV波束成形方法，该方法几乎可以最佳地逼近MV波束成形，同时通过使用主成分分析（PCA）进行降维来大大降低计算复杂度。根据预先计算的常规MV权重离线估算主分量。因此，所提出的方法不直接计算MV权重，而是通过一些选定的主要主成分的线性组合来近似估计它们。组合权重的计算几乎与MV波束成形中的方式相同，但在PCA的波束成形器输入信号的变换域中，其中变换后的协方差矩阵的维数等于某些选定主成分矢量的数量。计算机仿真和实验均通过仿真回声信号以及幻像和体内实验验证了所提方法的有效性。可以肯定的是，我们的方法可以将协方差矩阵的维数降低到2≤。 2，同时保持MV波束形成的良好图像质量。

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