摘要翻译：
组织反射率函数(TRF)与系统点扩展函数(PSF)的卷积导致的超声图像分辨率差是医学超声成像中的一个主要问题。本文提出了一种基于相关约束缺失数据估计的盲多通道频域最小均方（md-bMCFLMS）算法，以消除PSF对超声射频数据的影响。在第一步中，提出了一种基于块的MCFLMS（bMCFLMS）算法来估计TRFs和PSF，并在第二步中用于估计缺失数据。在md-bMCFLMS算法中，利用该缺失数据构造了一个改进的代价函数，进一步提高了图像的分辨率。为了解决PSF的非平稳性，与文献中的分块方法不同，本文提出了一种时间有效的分块方法。这里描述的分块方法使用块位置独立的固定大小矩阵，并且可以并行实现。然而，bMCFLMS算法由于信道噪声和TRF估计误差的传播而出现不收敛。在md-bMCFLMS算法中，由于估计误差增大，这种情况更加严重。针对这一问题，本文提出了一种新的约束条件，该约束条件基于测量的RF数据与估计的TRF之间的相关性。利用仿真体模、实验体模和活体数据对我们提出的盲反卷积算法的有效性进行了测量。
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英文标题：
《Ultrasonic Tissue Reflectivity Function Estimation Using Correlation
  Constrained Multichannel FLMS Algorithm with Missing RF Data》
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作者：
Jayanta Dey, Md. Kamrul Hasan
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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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英文摘要：
  Poor resolution of ultrasound images due to convolution of the tissue reflectivity function (TRF) with the system point spread function (PSF) is a major issue in medical ultrasound imaging. In this paper, we propose a correlation constrained missing-data estimation based blind multichannel frequency- domain least-mean-squares (md-bMCFLMS) algorithm to undo the effect of PSF on the ultrasound radio-frequency (RF) data. In the first step, a block-based MCFLMS (bMCFLMS) algorithm is proposed to estimate the TRFs and the PSF which are used in the second step to estimate the missing data. This missing data is used in the md-bMCFLMS algorithm to construct a modified cost function for further improvement of the image resolution. To account for the nonstationarity of the PSF, unlike the blocking approach described in the literature, we introduce a time-efficient blocking method in this paper. The blocking approach described here uses a block position independent fixed size matrix and can be implemented parallely. The bMCFLMS algorithm, however, shows misconvergence due to both channel noise and propagation of TRF estimation error from the previous blocks. This phe- nomenon is more intense in the case of md-bMCFLMS algorithm because of increased estimation error. To address this problem, a novel constraint based on the correlation between the measured RF data and estimated TRF is proposed in this paper. The efficacy of our proposed blind deconvolution algorithm is measured using simulation phantom, experimental phantom and in-vivo data.
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PDF链接：
https://arxiv.org/pdf/1712.01015