IEEE Trans Med Imaging. 2018 Jul;37(7):1664-1677. doi: 10.1109/TMI.2018.2795085.
We present a novel and efficient approach for robust estimation of displacement in real-time strain imaging for freehand ultrasound elastography by utilizing pre- and post-deformation ultrasound images. We define a quality factor for image lines and find the line with the highest value of quality factor to serve as the seed line for generating the displacement map. We also develop an analytical framework for coarse-to-fine displacement estimation, obtain an initial estimate of the seed line's displacement with subsample precision, and propagate it to the entire image to obtain a high quality strain image. Our fast strategy for estimating the seed line's displacement enables us to enhance the robustness without sacrificing the speed by identifying a new seed line when the quality falls below a given threshold. This is more efficient than the existing approaches that utilize multiple seed lines to improve robustness. Simulations, phantom experiments, and clinical studies show high signal-to-noise-ratio and contrast-to-noise-ratio values in our method for a wide range of average strain levels (1%-10%). Phantom experiments also demonstrate that our method is robust against corrupt and decorrelated data. Our method is superior to the existing real-time methods as it can produce high-quality strain images for up to 10% average strain levels at the rate of 20 frames/s on conventional CPUs.
我们提出了一种新颖而高效的方法,通过利用预变形和后变形超声图像,实现自由-hand 超声弹性成像中实时应变成像的位移稳健估计。我们定义了图像线的质量因子,并找到质量因子值最高的线作为生成位移图的种子线。我们还开发了一种从粗到精的位移估计分析框架,用亚采样精度获得种子线位移的初始估计,并将其传播到整个图像,以获得高质量的应变图像。我们快速估计种子线位移的策略通过在质量低于给定阈值时识别新的种子线,在不牺牲速度的情况下提高了鲁棒性。这比现有的利用多条种子线来提高鲁棒性的方法更有效。模拟、体模实验和临床研究表明,我们的方法在广泛的平均应变速率范围内(1%-10%)具有高信噪比和对比度噪声比。体模实验还表明,我们的方法对损坏和去相关的数据具有鲁棒性。我们的方法优于现有的实时方法,因为它可以在传统 CPU 上以 20 帧/秒的速度生成高达 10%平均应变速率的高质量应变图像。