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广义旁瓣相消波束形成与特征空间维纳滤波器在医学超声成像中的结合。

Generalized sidelobe canceler beamforming combined with Eigenspace-Wiener postfilter for medical ultrasound imaging.

出版信息

Technol Health Care. 2022;30(S1):501-512. doi: 10.3233/THC-THC228046.

Abstract

BACKGROUND

The beamforming algorithm is key to the image quality of the medical ultrasound system. The generalized sidelobe canceler (GSC) beamforming can improve the image quality in lateral resolution, but the contrast is not improved correspondingly.

OBJECTIVE

In our research, we try to optimize the generalized sidelobe canceler to obtain images that achieve an improvement in both lateral resolution and contrast.

METHODS

We put forward a new beamforming algorithm which combines the generalized sidelobe canceler and Eigenspace-Wiener postfilter. According to eigenspace decomposition of the covariance matrix of the received data, the components of the Wiener postfilter can be calculated from the signal matrix and the noise matrix. Then, the adaptive weight vector of GSC is further constrained by the Eigenspace-Wiener postfilter, which make the output energy of the receiving array closer to the desired signal than the conventional GSC output.

RESULTS

We compare the new beamforming algorithm with delay-and-sum (DS) beamforming, synthetic aperture (SA) beamforming, and GSC beamforming using the simulated and experimental data sets. The quantitative results show that our method reduces the FWHM by 85.5%, 80.5%, and 38.9% while improving the CR by 123.6%, 47.7%, 84.4% on basis of DS, SA, and GSC beamforming, respectively.

CONCLUSIONS

The new beamforming algorithm can obviously improve the imaging quality of medical ultrasound imaging systems in both lateral resolution and contrast.

摘要

背景

波束形成算法是医学超声系统图像质量的关键。广义旁瓣相消器(GSC)波束形成可以提高横向分辨率的图像质量,但对比度并没有相应提高。

目的

在我们的研究中,我们尝试优化广义旁瓣相消器,以获得在横向分辨率和对比度方面都得到改善的图像。

方法

我们提出了一种新的波束形成算法,该算法将广义旁瓣相消器和特征空间维纳后滤波器相结合。根据接收数据协方差矩阵的特征空间分解,可以从信号矩阵和噪声矩阵中计算出维纳后滤波器的分量。然后,通过特征空间维纳后滤波器进一步约束 GSC 的自适应权向量,使接收阵列的输出能量比传统的 GSC 输出更接近期望信号。

结果

我们使用模拟和实验数据集将新的波束形成算法与延迟和求和(DS)波束形成、合成孔径(SA)波束形成和 GSC 波束形成进行了比较。定量结果表明,与 DS、SA 和 GSC 波束形成相比,我们的方法分别将 FWHM 降低了 85.5%、80.5%和 38.9%,同时将 CR 提高了 123.6%、47.7%和 84.4%。

结论

新的波束形成算法可以明显提高医学超声成像系统在横向分辨率和对比度方面的成像质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b86d/9028622/2e2da49ce5e4/thc-30-thc228046-g001.jpg

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