摘要翻译：
近年来，最小方差(MV)波束形成因其高分辨率和对比度在B超成像(USI)中得到了广泛的研究。然而，由于协方差矩阵估计的不准确，使得MV波束形成器的性能在噪声的存在下下降，从而导致图像质量低下。二次谐波成像(SHI)比传统的脉冲回波USI具有许多优点，如提高了轴向和横向分辨率。然而，信噪比低是SHI的一个主要问题。本文将基于特征空间的最小方差(EIBMV)波束形成器用于二次谐波USI。利用脉冲反转(PI)技术实现了组织谐波成像(THI)。使用EIBMV权重而不是MV权重将导致减少旁瓣和提高对比度，而不损害MV波束形成器的高分辨率（即使在存在强噪声的情况下）。此外，我们还研究了计算EIBMV权值的重要参数K、L和{δ}的变化对Shi中得到的分辨率和对比度的影响。用数值数据（点靶和囊肿体模）对结果进行了评价，并为THI指明了合适的EIBMV参数。
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英文标题：
《Effects of Important Parameters Variations on Computing Eigenspace-Based
  Minimum Variance Weights for Ultrasound Tissue Harmonic Imaging》
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作者：
Mehdi Haji Heidari, Moein Mozaffarzadeh, Rayyan Manwar, Mohammadreza
  Nasiriavanaki
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In recent years, the minimum variance (MV) beamforming has been widely studied due to its high resolution and contrast in B-mode Ultrasound imaging (USI). However, the performance of the MV beamformer is degraded at the presence of noise, as a result of the inaccurate covariance matrix estimation which leads to a low quality image. Second harmonic imaging (SHI) provides many advantages over the conventional pulse-echo USI, such as enhanced axial and lateral resolutions. However, the low signal-to-noise ratio (SNR) is a major problem in SHI. In this paper, Eigenspace-based minimum variance (EIBMV) beamformer has been employed for second harmonic USI. The Tissue Harmonic Imaging (THI) is achieved by Pulse Inversion (PI) technique. Using the EIBMV weights, instead of the MV ones, would lead to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer (even at the presence of a strong noise). In addition, we have investigated the effects of variations of the important parameters in computing EIBMV weights, i.e., K, L, and {\delta}, on the resolution and contrast obtained in SHI. The results are evaluated using numerical data (using point target and cyst phantoms), and the proper parameters of EIBMV are indicated for THI.
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PDF链接：
https://arxiv.org/pdf/1802.09316