Minimum-variance (MV) beamforming, as a typical adaptive beamforming method, has been widely studied in medical ultrasound imaging. This method achieves higher spatial resolution than traditional delay-and-sum (DAS) beamforming by minimizing the total output power while maintaining the desired signals. However, it suffers from high computational complexity due to the heavy calculation load when determining the inverse of the high-dimensional matrix. Low-complexity MV algorithms have been studied recently. In this study, we propose a novel MV beamformer based on orthogonal decomposition of the compounded subspace (CS) of the covariance matrix in synthetic aperture (SA) imaging, which aims to reduce the dimensions of the covariance matrix and therefore reduce the computational complexity. Multiwave spatial smoothing is applied to the echo signals for the accurate estimation of the covariance matrix, and adaptive weight vectors are calculated from the low-dimensional subspace of the original covariance matrix. We conducted simulation, experimental and in vivo studies to verify the performance of the proposed method. The results indicate that the proposed method performs well in maintaining the advantage of high spatial resolution and effectively reduces the computational complexity compared with the standard MV beamformer. In addition, the proposed method shows good robustness against sound velocity errors.

中文翻译：

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基于复合子空间正交分解的低复杂度最小方差波束形成器

最小方差（MV）波束成形作为一种典型的自适应波束成形方法，在医学超声成像中得到了广泛的研究。这种方法通过在保持所需信号的同时最小化总输出功率，实现了比传统延迟求和 (DAS) 波束成形更高的空间分辨率。然而，在确定高维矩阵的逆时，由于计算量大，计算复杂度高。最近研究了低复杂度的 MV 算法。在这项研究中，我们提出了一种基于合成孔径（SA）成像中协方差矩阵的复合子空间（CS）的正交分解的新型 MV 波束形成器，旨在减少协方差矩阵的维数，从而降低计算复杂度。对回波信号应用多波空间平滑以精确估计协方差矩阵，并从原始协方差矩阵的低维子空间计算自适应权向量。我们进行了模拟、实验和体内研究，以验证所提出方法的性能。结果表明，与标准MV波束形成器相比，所提出的方法在保持高空间分辨率的优势方面表现良好，并有效降低了计算复杂度。此外，所提出的方法对声速误差具有良好的鲁棒性。实验和体内研究以验证所提出方法的性能。结果表明，与标准MV波束形成器相比，所提出的方法在保持高空间分辨率的优势方面表现良好，并有效降低了计算复杂度。此外，所提出的方法对声速误差具有良好的鲁棒性。实验和体内研究以验证所提出方法的性能。结果表明，与标准MV波束形成器相比，所提出的方法在保持高空间分辨率的优势方面表现良好，并有效降低了计算复杂度。此外，所提出的方法对声速误差具有良好的鲁棒性。