Li Junbo, Cui Yaoyao, Kadour Michael, Noble J Alison
Wolfson Medical Vision Laboratory, Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK.
IEEE Trans Ultrason Ferroelectr Freq Control. 2008 Feb;55(2):319-26. doi: 10.1109/TUFFC.2008.651.
Ultrasound elasticity imaging shows promise as a new way for early detection of cancers by assessing the elastic characteristics of soft tissue. So far the commonly used approach involves solving the so-called inverse elasticity problem of recovering elastic parameters from displacement measurements. We propose a finite-elementbased nonlinear scheme to estimate the elasticity distribution of soft tissue from multi-compressed ultrasound radio frequency (RF) data. An experimental ultrasound workstation has been developed to acquire multi-compressed data. A composite probe was employed as the compression plate. The contact forces and torques were acquired at the same time as imaging. Axial displacements under different static loads are estimated from the RF data before and after deformation using a cross-correlation technique. The confidence of displacement estimates is employed as a weighting factor in solving the objective function describing the inverse elasticity reconstruction problem. A novel splitand- merge strategy is employed over the image sequence in which strain images are used to provide a priori knowledge of the relative stiffness distribution of the tissue to constrain the inverse problem solution. The experimental study has allowed us to investigate the performance of our approach in the controlled environment of simulated and phantom data. For a simulated single inclusion model with 5% axial displacement estimation error, the L2-error between the target and the reconstructed Young's modulus was found to be about 1%. In vivo validation of the proposed method has been carried out and some preliminary results are presented.
超声弹性成像作为一种通过评估软组织弹性特征来早期检测癌症的新方法显示出前景。到目前为止，常用的方法涉及解决从位移测量中恢复弹性参数的所谓逆弹性问题。我们提出了一种基于有限元的非线性方案，用于从多压缩超声射频（RF）数据估计软组织的弹性分布。已开发出一个实验性超声工作站来获取多压缩数据。采用复合探头作为压板。在成像的同时获取接触力和扭矩。使用互相关技术从变形前后的RF数据估计不同静态载荷下的轴向位移。位移估计的置信度在求解描述逆弹性重建问题的目标函数时用作加权因子。在图像序列上采用了一种新颖的分裂合并策略，其中应变图像用于提供组织相对刚度分布的先验知识，以约束逆问题的解。实验研究使我们能够在模拟数据和体模数据的受控环境中研究我们方法的性能。对于具有5％轴向位移估计误差的模拟单包体模型，目标与重建杨氏模量之间的L2误差约为1％。已对所提出的方法进行了体内验证，并给出了一些初步结果。
