线阵光声成像中High的图像改善 分辨率相干因子加权技术

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摘要翻译：
在光声成像(PAI)中，最常用的波束形成算法是延迟与和(DAS)算法，由于其实现简单。然而，它导致了低质量的图像受高水平的旁瓣的影响。相干因子(CF)可以用来消除DAS重建图像中的旁瓣，但与最小方差(MV)等高分辨率波束形成器相比，分辨率的提高不够。作为线阵PAI中的一种加权算法，提出了采用高分辨率CF(HRCF)加权技术，即用MV代替传统CF公式中现有的DAS。数值和实验证明了HRCF具有较高的性能。模拟结果表明，在深度为40 mm时，与DAS+CF和MV+CF相比，HRCF的全宽半最大值分别提高了91%和15%，信噪比分别提高了40%和14%。与DAS+CF和MV+CF相比，HRCF在20 mm深度处的对比度分别提高了62%和21%
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英文标题：
《Image Improvement in Linear-Array Photoacoustic Imaging using High
  Resolution Coherence Factor Weighting Technique》
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作者：
Moein Mozaffarzadeh, Mohammad Mehrmohammadi, Bahador Makkiabadi
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最新提交年份：
2017
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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：Physics        物理学
二级分类：Medical Physics        医学物理学
分类描述：Radiation therapy. Radiation dosimetry. Biomedical imaging modelling.  Reconstruction, processing, and analysis. Biomedical system modelling and analysis. Health physics. New imaging or therapy modalities.
放射治疗。辐射剂量学。生物医学成像建模。重建、处理和分析。生物医学系统建模与分析。健康物理学。新的成像或治疗方式。
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英文摘要：
  In Photoacoustic imaging (PAI), the most prevalent beamforming algorithm is delay-and-sum (DAS) due to its simple implementation. However, it results in a low quality image affected by the high level of sidelobes. Coherence factor (CF) can be used to address the sidelobes in the reconstructed images by DAS, but the resolution improvement is not good enough compared to the high resolution beamformers such as minimum variance (MV). As a weighting algorithm in linear-array PAI, it was proposed to use high-resolution-CF (HRCF) weighting technique in which MV is used instead of the existing DAS in the formula of the conventional CF. The higher performance of HRCF was proved numerically and experimentally. The quantitative results obtained with the simulations show that at the depth of 40 mm, in comparison with DAS+CF and MV+CF, HRCF improves the full-width-half-maximum of about 91 % and 15 % and the signal-to-noise ratio about 40 % and 14 %, respectively. Moreover, the contrast ratio at the depth of 20 mm has been improved about 62 % and 21 % by HRCF, compared to DAS+CF and MV+CF, respectively
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PDF链接：
https://arxiv.org/pdf/1710.02751

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关键词：High 分辨率 Applications respectively Optimization 加权 传统 high HRCF algorithm