IEEE Trans Med Imaging. 2017 Jun;36(6):1347-1358. doi: 10.1109/TMI.2017.2685522. Epub 2017 Apr 6.
Most strain imaging techniques follow a pipeline strategy: in the first step, tissue displacement is estimated from radio-frequency (RF) frames, and in the second step, a spatial derivative operation is applied. There are two main issues that arise from this framework. First, the gradient operation amplifies noise, and therefore, smoothing techniques have to be adopted. Second, strain estimation does not exploit the original RF data. It rather relies solely on the noisy displacement field. In this paper, a novel technique is proposed that utilizes both the displacement field and the RF frames to accurately obtain the strain estimates. The normalized cross correlation (NCC) metric between two corresponding windows around the samples of the pre- and post-compressed images is employed to generate a dissimilarity measurement. The derivative of NCC with respect to the strain is analytically derived using the chain rule. This allows an efficient minimization of the dissimilarity metric with respect to the strain using the gradient descent optimization technique. The effectiveness of the proposed method is investigated through simulation data, phantom experiments, and in vivo patient data. The experimental results show that exploiting the information in RF data significantly improves the strain estimates.
在第一步中，从射频 (RF) 帧中估计组织位移，在第二步中，应用空间导数操作。该框架存在两个主要问题。首先，梯度操作会放大噪声，因此必须采用平滑技术。其次，应变估计并没有利用原始的 RF 数据。它仅仅依赖于嘈杂的位移场。本文提出了一种利用位移场和 RF 帧来准确获取应变估计的新技术。使用两个对应窗口之间的归一化互相关 (NCC) 度量来生成差异度量，这两个窗口分别围绕预压缩和后压缩图像的样本。利用链式法则推导出 NCC 对应变的导数。这允许使用梯度下降优化技术有效地最小化差异度量相对于应变的度量。通过仿真数据、体模实验和体内患者数据研究了所提出方法的有效性。实验结果表明，利用 RF 数据中的信息可以显著提高应变估计的精度。
