Eigenspace-Based Minimum Variance Beamforming Applied to Medical Ultrasound Imaging
Eigenspace-Based Minimum Variance Beamforming Applied to Medical Ultrasound Imaging
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DOI：
10.1109/tuffc.2010.1706
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发表时间：
2010-11-01
影响因子：
3.6
通讯作者：
Mahloojifar, Ali
中科院分区：
文献类型：
--
作者：
Asl, Babak Mohammadzadeh;Mahloojifar, Ali
最近，自适应波束形成方法已被成功地应用于医学超声成像，与非自适应延迟求和（DAS）波束形成器相比，图像质量得到了显着改善。超声成像文献中提出的大多数自适应波束形成器都是基于最小方差（MV）波束形成器，其可以显著提高成像分辨率，尽管它们在增强对比度方面的成功尚未令人满意。波束形成器需要同时提高分辨率和对比度。为此，在本文中，我们已经应用了基于特征空间的MV（EIBMV）波束形成器的医疗超声成像，并已显示出同时提高成像分辨率和对比度。EIBMV波束形成器利用协方差矩阵的特征结构来提高MV波束形成器的性能。通过将MV权重向量投影到从协方差矩阵的特征结构构造的向量子空间上来找到EIBMV的权重向量。使用EIBMV权重代替MV权重导致旁瓣减小和对比度提高，而不损害MV波束形成器的高分辨率。此外，建议EIBMV波束形成器提出了一个令人满意的鲁棒性，对数据不对准造成的导向矢量误差，优于正则化MV波束形成器。
Recently, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared with non-adaptive delay-and-sum (DAS) beamformers. Most of the adaptive beamformers presented in the ultrasound imaging literature are based on the minimum variance (MV) beamformer which can significantly improve the imaging resolution, although their success in enhancing the contrast has not yet been satisfactory. It is desirable for the beamformer to improve the resolution and contrast at the same time. To this end, in this paper, we have applied the eigenspace-based MV (EIBMV) beamformer to medical ultrasound imaging and have shown a simultaneous improvement in imaging resolution and contrast. EIBMV beamformer utilizes the eigenstructure of the covariance matrix to enhance the performance of the MV beamformer. The weight vector of the EIBMV is found by projecting the MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix. Using EIBMV weights instead of the MV ones leads to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer. In addition, the proposed EIBMV beamformer presents a satisfactory robustness against data misalignment resulting from steering vector errors, outperforming the regularized MV beamformer.