增强光声的双最小方差波束形成方法 影像学

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摘要翻译：
非自适应延迟和(DAS)波束形成器是重建光声(PA)图像的常用算法之一。但是，由于DAS的旁瓣高，主瓣宽，重建后的PA图像质量并不理想。相比之下，自适应波束形成器，如最小方差(MV)，与DAS相比，可以得到更好的图像。本文提出了一种新的波束形成方法，称为双MV(D-MV)，以提高图像质量。结果表明，在MV波束形成器的输出中，加权子阵之间存在一个求和过程。这种总和可以解释为非自适应DAS波束形成器。提出用MV算法代替现有的DAS算法，以减小DAS波束形成器对加权子阵间离轴信号的贡献。数值结果表明，与MV波束形成器相比，该技术的全宽半最大值(FWHM)和信噪比分别平均提高了28.83μm和4.8dB。实验结果表明，与MV波束形成器相比，D-MV波束形成器的半高宽和信噪比分别提高了0.15mm和1.96dB。
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英文标题：
《Double Minimum Variance Beamforming Method to Enhance Photoacoustic
  Imaging》
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作者：
Roya Paridar, Moein Mozaffarzadeh, Mohammadreza Nasiriavanaki, Mahdi
  Orooji
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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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一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
--

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英文摘要：
  One of the common algorithms used to reconstruct photoacoustic (PA) images is the non-adaptive Delay-and-Sum (DAS) beamformer. However, the quality of the reconstructed PA images obtained by DAS is not satisfying due to its high level of sidelobes and wide mainlobe. In contrast, adaptive beamformers, such as minimum variance (MV), result in an improved image compared to DAS. In this paper, a novel beamforming method, called Double MV (D-MV) is proposed to enhance the image quality compared to the MV. It is shown that there is a summation procedure between the weighted subarrays in the output of the MV beamformer. This summation can be interpreted as the non-adaptive DAS beamformer. It is proposed to replace the existing DAS with the MV algorithm to reduce the contribution of the off-axis signals caused by the DAS beamformer between the weighted subarrays. The numerical results show that the proposed technique improves the full-width-half-maximum (FWHM) and signal-to-noise ratio (SNR) for about 28.83 \mu m and 4.8 dB in average, respectively, compared to MV beamformer. Also, quantitative evaluation of the experimental results indicates that the proposed D-MV leads to 0.15 mm and 1.96 dB improvement in FWHM and SNR, in comparison with MV beamformer.
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PDF链接：
https://arxiv.org/pdf/1802.0372

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关键词：影像学 Experimental Applications Optimization respectively 提高 FWHM 方法 non quality