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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Blind Deconvolution of Ultrasound Images Using -Norm-Constrained Block-Based Damped Variable Step-Size Multichannel LMS Algorithm
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Blind Deconvolution of Ultrasound Images Using -Norm-Constrained Block-Based Damped Variable Step-Size Multichannel LMS Algorithm

机译：基于范数约束的阻尼可变步长多通道LMS算法对超声图像进行盲卷积

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摘要

The problem of improving the ultrasound image resolution by undoing the effect of convolution on backscattered radio-frequency (RF) data caused by the point spread function (PSF) of ultrasonic imaging system is one of the key problems in the reconstruction of the medical ultrasound images. In this paper, the tissue reflectivity functions (TRFs) are directly estimated from the noisy and nonstationary RF data using the block-based multichannel least-mean square ( -bMCLMS) algorithm without any prior knowledge of the PSF. To account for the nonstationarity and incomplete acquisition problem of the ultrasound RF data a modified block-based cross-relation equation has been developed. An -norm regularized cost function based on the proposed modified cross-relation equation is then formulated for blind estimation of the TRFs using the new -bMCLMS algorithm. A damped variable step-size is also developed to compensate for the noise effect and to improve the convergence speed of the algorithm. The PSF is then estimated from multiple lateral blocks of RF data using the regularized multiple-input/output inverse theorem, which is known to be suitable for both minimum and nonminimum phase signals. The salient feature of the proposed method is that no basis function is required for TRFs and/or PSF. The efficacy of the proposed method is examined using the simulation/experimental phantom data and in vivo RF data and evaluated in terms of the quality metrics: resolution gain (RG), normalized projection misalignment (NPM), and shifted normalized mean square error (snMSE). The results show that the RG and NPM improvements of TRFs estimation of - nd dB, respectively, and the snMSE improvement of the PSF estimation of the order can be achieved in our technique as compared with the other techniques in the literature.
机译：通过消除卷积对超声成像系统的点扩展函数（PSF）引起的后向散射射频（RF）数据的影响来提高超声图像分辨率的问题是重建医学超声图像的关键问题之一。在本文中，无需使用PSF的任何先验知识，就可以使用基于块的多通道最小均方（-bMCLMS）算法直接从嘈杂的非平稳RF数据中估计组织反射率函数（TRF）。为了解决超声RF数据的非平稳性和不完全采集问题，开发了一种基于块的互相关方程。然后，使用新的-bMCLMS算法，基于所提出的改进的交叉关系方程制定了-范数正则化成本函数，用于TRF的盲估计。还开发了阻尼可变步长，以补偿噪声影响并提高算法的收敛速度。然后，使用正则化的多输入/输出逆定理，从RF数据的多个横向块估计PSF，已知该定理适用于最小和非最小相位信号。所提出的方法的显着特征是，TRF和/或PSF不需要基函数。使用模拟/实验体模数据和体内RF数据检查了所提出方法的功效，并根据质量指标进行了评估：分辨率增益（RG），归一化投影失准（NPM）和移位归一化均方误差（snMSE） ）。结果表明，与文献中的其他技术相比，我们的技术可以分别实现对-nd dB的TRF估计的RG和NPM改进，以及阶数PSF估计的snMSE改进。

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