IEEE Trans Ultrason Ferroelectr Freq Control. 2017 Oct;64(10):1625-1636. doi: 10.1109/TUFFC.2017.2717933. Epub 2017 Jun 21.
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.
在准静态超声弹性成像中，关键步骤是估计在组织发生变形时获得的两个射频 (RF) 数据帧之间的时间延迟。本文提出了一种新的 RF 数据所有样本的同时时间延迟估计 (TDE) 技术，从而利用 RF 数据中的所有信息进行 TDE。提出了一种将 RF 数据强度相似性和位移连续性的先验信息纳入其中的非线性代价函数。该函数的优化涉及到对 RF 数据所有样本的 TDE 进行搜索，这使得优化变得棘手，因为变量的数量可能约为一百万。因此，将优化问题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求解。我们将我们的方法称为 GLobal Ultrasound Elastography (GLUE)，并将其与动态编程分析最小化 (DPAM) 和归一化互相关 (NCC) 技术进行比较。我们的模拟结果表明，轴向应变图的对比噪声比 (CNR) 值分别为 NCC 的 4.94、DPAM 的 14.62 和 GLUE 的 26.31。我们在组织模拟体模的实验数据上的结果表明，轴向应变图的 CNR 值分别为 NCC 的 1.07、DPAM 的 16.01 和 GLUE 的 18.21。最后，我们在体内数据上的结果表明，轴向应变图的 CNR 值分别为 DPAM 的 3.56 和 GLUE 的 13.20。
