Research Institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hong Kong.
IEEE Trans Ultrason Ferroelectr Freq Control. 2010 Sep;57(9):1943-51. doi: 10.1109/TUFFC.2010.1642.
Ultrasound elastography has become a wellknown optional imaging method for the diagnosis of tissue abnormalities in various body parts. It images the elasticity of compliant tissues by estimating the local displacements and strains using pre- and post-compression RF echo signals. In this paper, taking the RF signal as image intensity and RF samples as pixels, we present a motion estimation framework to compute the axial tissue displacements and strains. This method takes advantage of both the block matching algorithm (BMA) and local optical flow techniques. For two frames of RF signals, coarse motion estimates are first computed using BMA. The motion estimates obtained are then used to warp the first frame toward the second one, thus making the warped frame more spatially correlated to the second one. Next, the Lucas-Kanade optical flow method is employed to compute the residual motion between the warped frame and the original second frame, with inherent sub-pixel precision. Finally, the displacements from the two steps are combined. The warp-and-refine procedure can be iterated if the residual motion is larger than a predefined empirical threshold. To test its feasibility, we first applied the method to simulated data. The results show that our method is robust to relatively large motions and is capable of generating accurate motion estimation with subsample spatial resolution. These methods have been deployed and are being tested on a commercialized ultrasound machine that previously did not have elastography functions. Quality real-time display of elastography along with freehand scanning has been accomplished. The proposed framework provides an alternative method for motion estimation with good performance, and it can potentially be improved using hardware to realize the BMA.
超声弹性成像是一种广为人知的可选成像方法，可用于诊断身体各部位的组织异常。它通过估计局部位移和应变，使用预压缩和后压缩 RF 回波信号来对顺应性组织的弹性进行成像。在本文中，我们以 RF 信号作为图像强度，以 RF 样本作为像素，提出了一种运动估计框架，用于计算轴向组织位移和应变。该方法结合了块匹配算法 (BMA) 和局部光流技术。对于两帧 RF 信号，首先使用 BMA 计算粗运动估计。然后，将获得的运动估计用于将第一帧向第二帧变形，从而使变形帧与第二帧更具空间相关性。接下来，使用 Lucas-Kanade 光流法计算变形帧与原始第二帧之间的残余运动，具有固有的亚像素精度。最后，将两步的位移组合起来。如果残余运动大于预设的经验阈值，则可以迭代进行变形和细化过程。为了测试其可行性，我们首先将该方法应用于模拟数据。结果表明，我们的方法对相对较大的运动具有鲁棒性，能够以亚采样空间分辨率生成准确的运动估计。这些方法已经部署并在商业化的超声机器上进行了测试，该机器以前没有弹性成像功能。已经实现了弹性成像的实时质量显示和自由手扫描。所提出的框架提供了一种具有良好性能的运动估计替代方法，并且可以使用硬件来改进 BMA 实现。
