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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Global Time-Delay Estimation in Ultrasound Elastography
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Global Time-Delay Estimation in Ultrasound Elastography

机译：超声弹性成像的全局时延估计

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摘要

A critical step in quasi-static ultrasound elastography is the estimation of time delay between two frames of radio-frequency (RF) data that are obtained while the tissue is undergoing deformation. This paper presents a novel technique for time-delay estimation (TDE) of all samples of RF data simultaneously, thereby exploiting all the information in RF data for TDE. A nonlinear cost function that incorporates similarity of RF data intensity and prior information of displacement continuity is formulated. Optimization of this function involves searching for TDE of all samples of the RF data, rendering the optimization intractable with conventional techniques given that the number of variables can be approximately one million. Therefore, the optimization problem is converted to a sparse linear system of equations, and is solved in real time using a computationally efficient optimization technique. We call our method GLobal Ultrasound Elastography (GLUE), and compare it to dynamic programming analytic minimization (DPAM) and normalized cross correlation (NCC) techniques. Our simulation results show that the contrast-to-noise ratio (CNR) values of the axial strain maps are 4.94 for NCC, 14.62 for DPAM, and 26.31 for GLUE. Our results on experimental data from tissue mimicking phantoms show that the CNR values of the axial strain maps are 1.07 for NCC, 16.01 for DPAM, and 18.21 for GLUE. Finally, our results on in vivo data show that the CNR values of the axial strain maps are 3.56 for DPAM and 13.20 for GLUE.
机译：准静态超声弹性成像中的关键步骤是估算在组织进行变形时获得的两帧射频（RF）数据之间的时间延迟。本文提出了一种同时对RF数据的所有样本进行时延估计（TDE）的新技术，从而将RF数据中的所有信息用于TDE。制定了一个非线性成本函数，该函数结合了RF数据强度的相似性和位移连续性的先验信息。此功能的优化涉及搜索所有RF数据样本的TDE，假设变量数量约为一百万，则使用常规技术难以进行优化。因此，优化问题被转换为稀疏线性方程组，并使用计算效率高的优化技术实时解决。我们将我们的方法称为Global超声弹性成像（GLUE），并将其与动态规划分析最小化（DPAM）和归一化互相关（NCC）技术进行比较。我们的仿真结果表明，轴向应变图的对比度-噪声比（CNR）值对于NCC为4.94，对于DPAM为14.62，对于GLUE为26.31。我们从组织模拟体模得到的实验数据的结果表明，轴向应变图的CNR值对于NCC为1.07，对于DPAM为16.01，对于GLUE为18.21。最后，我们在体内数据上的结果表明，轴向应变图的CNR值对于DPAM为3.56，对于GLUE为13.20。

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