改进的图像增强和降噪方法 延时乘和波束形成器在医用光声中的应用 影像学

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摘要翻译：
光声成像(PAI)是一种新兴的生物医学成像方式，能够提供光学和超声(US)成像的高对比度和高分辨率。当短时间激光脉冲照射作为成像目标的组织时，组织会产生美国波，探测到的波可以用来重建组织的光吸收分布。由于接收到的部分PA波由美国波组成，大量的美国成像中的波束形成算法都可以应用于PA成像。延迟与和(DAS)是美国成像中最常见的波束形成算法。然而，使用DAS波束形成器导致图像分辨率低，离轴信号贡献大。为了解决这些问题，提出了一种新的美国成像方法，即延迟乘和(DMAS)算法，并将其作为乳腺癌共焦微波成像中的一种重建算法。将DMAS算法应用于PA成像系统中，结果表明该算法在提高分辨率的同时降低了旁瓣。然而，在存在高噪声的情况下，重建图像仍然受到高噪声贡献的影响。本文在DMAS公式扩展的基础上，提出了一种改进的DMAS波束形成算法。定量和定性的结果表明，该方法在降低对比度的代价下，具有更好的降噪效果和分辨率提高效果。在仿真中，采用两点目标和两个成像深度的横向变化，并在成像介质中的高噪声水平下对其进行评估。与DMAS相比，本文提出的算法可以减少约19 dB的侧向谷值，从而得到更清晰的两点目标。此外，旁瓣电平降低约25分贝。
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英文标题：
《Image Enhancement and Noise Reduction Using Modified
  Delay-Multiply-and-Sum Beamformer: Application to Medical Photoacoustic
  Imaging》
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作者：
Moein Mozaffarzadeh, Ali Mahloojifar, 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：
  Photoacoustic imaging (PAI) is an emerging biomedical imaging modality capable of providing both high contrast and high resolution of optical and UltraSound (US) imaging. When a short duration laser pulse illuminates the tissue as a target of imaging, tissue induces US waves and detected waves can be used to reconstruct optical absorption distribution. Since receiving part of PA consists of US waves, a large number of beamforming algorithms in US imaging can be applied on PA imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in US imaging. However, make use of DAS beamformer leads to low resolution images and large scale of off-axis signals contribution. To address these problems a new paradigm namely Delay-Multiply-and-Sum (DMAS), which was used as a reconstruction algorithm in confocal microwave imaging for breast cancer detection, was introduced for US imaging. Consequently, DMAS was used in PA imaging systems and it was shown this algorithm results in resolution enhancement and sidelobe degrading. However, in presence of high level of noise the reconstructed image still suffers from high contribution of noise. In this paper, a modified version of DMAS beamforming algorithm is proposed based on DAS inside DMAS formula expansion. The quantitative and qualitative results show that proposed method results in more noise reduction and resolution enhancement in expense of contrast degrading. For the simulation, two-point target, along with lateral variation in two depths of imaging are employed and it is evaluated under high level of noise in imaging medium. Proposed algorithm in compare to DMAS, results in reduction of lateral valley for about 19 dB followed by more distinguished two-point target. Moreover, levels of sidelobe are reduced for about 25 dB.
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PDF链接：
https://arxiv.org/pdf/1801.06014

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关键词：影像学 Applications Optimization Experimental Contribution noise resolution 作为 结果表明 效果