Minimum variance beamformer (MVB) is an adaptive beamformer which provides images with higherresolution and contrast in comparison with non-adaptive beamformers like delay and sum (DAS). It finds weight vector of beamformer by minimizing output power while keeping the desired signal unchanged. We used the eigen-based MVB and generalized coherence factor (GCF) to further improve the quality of MVB beamformed images. The eigen-based MVB projects the weight vector with a transformation matrixconstructed from eigen-decomposing of the array covariance matrix that increases resolution and contrast. GCF is used to emphasis on coherence part of images that improves the resolution. Four different datasets provided by IUS 2016 beamforming challenge are used to evaluate the proposedmethod. In comparison with DAS with rectangular weight vector, our method improved contrast about 8.52 dB and 6.20 dB for simulation and experimental contrast phantoms, respectively. It also enhanced lateral (axial) resolution about 87% (40%) and 73% (21%) for simulated and experimental resolution phantoms, respectively.
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机译:最小方差波束形成器(MVB)是一种自适应波束形成器,与诸如延迟和求和(DAS)之类的非自适应波束形成器相比,它可以为图像提供更高的分辨率和对比度。它通过在保持所需信号不变的情况下最小化输出功率来找到波束成形器的权重向量。我们使用基于特征的MVB和广义相干因子(GCF)进一步提高了MVB波束形成图像的质量。基于特征的MVB投影权重向量时使用变换矩阵,该矩阵由阵列协方差矩阵的特征分解构成,从而提高了分辨率和对比度。 GCF用于强调图像的相干部分,以提高分辨率。 IUS 2016波束成形挑战赛提供了四个不同的数据集,用于评估提出的方法。与具有矩形权重向量的DAS相比,我们的方法分别将模拟和实验对比体模的对比提高了约8.52 dB和6.20 dB。对于模拟和实验分辨率的幻像,它还分别提高了横向(轴向)分辨率约87%(40%)和73%(21%)。
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