Thitaikumar Arun, Krouskop Thomas A, Garra Brian S, Ophir Jonathan
Department of Diagnostic and Interventional Imaging, The University of Texas Medical School, Ultrasonics Laboratory, Houston, TX, USA.
Phys Med Biol. 2007 May 7;52(9):2615-33. doi: 10.1088/0031-9155/52/9/019. Epub 2007 Apr 17.
Ultrasound elastography produces strain images of compliant tissues under quasi-static compression. In axial-shear strain elastography, the local axial-shear strain resulting from application of quasi-static axial compression to an inhomogeneous material is imaged. The overall hypothesis of this work is that the pattern of axial-shear strain distribution around the inclusion/background interface is completely determined by the bonding at the interface after normalization for inclusion size and applied strain levels, and that it is feasible to extract certain features from the axial-shear strain elastograms to quantify this pattern. The mechanical model used in this study consisted of a single stiff circular inclusion embedded in a homogeneous softer background. First, we performed a parametric study using finite-element analysis (FEA) (no ultrasound involved) to identify possible features that quantify the pattern of axial-shear strain distribution around an inclusion/background interface. Next, the ability to extract these features from axial-shear strain elastograms, estimated from simulated pre- and post-compression noisy RF data, was investigated. Further, the feasibility of extracting these features from in vivo breast data of benign and malignant tumors was also investigated. It is shown using the FEA study that the pattern of axial-shear strain distribution is determined by the degree of bonding at the inclusion/background interface. The results suggest the feasibility of using normalized features that capture the region of positive and negative axial-shear strain area to quantify the pattern of the axial-shear strain distribution. The simulation results showed that it was feasible to extract the features, as identified in the FEA study, from axial-shear strain elastograms. However, an effort must be made to obtain axial-shear strain elastograms with the highest signal-to-noise ratio (SNR(asse)) possible, without compromising the resolution. The in vivo results demonstrated the feasibility of producing and extracting features from the axial-shear strain elastograms from breast data. Furthermore, the in vivo axial-shear strain elastograms suggest an additional feature not identified in the simulations that may potentially be used for distinguishing benign from malignant tumors-the proximity of the axial-shear strain regions to the inclusion/background interface identified in the sonogram.
超声弹性成像可在准静态压缩下生成顺应性组织的应变图像。在轴向剪切应变弹性成像中，对非均匀材料施加准静态轴向压缩所产生的局部轴向剪切应变会被成像。本研究的总体假设是，在对夹杂物尺寸和施加的应变水平进行归一化之后，夹杂物/背景界面周围的轴向剪切应变分布模式完全由界面处的结合情况决定，并且从轴向剪切应变弹性图中提取某些特征以量化这种模式是可行的。本研究中使用的力学模型由嵌入均匀较软背景中的单个刚性圆形夹杂物组成。首先，我们使用有限元分析（FEA）（不涉及超声）进行了参数研究，以确定量化夹杂物/背景界面周围轴向剪切应变分布模式的可能特征。接下来，研究了从模拟的压缩前和压缩后有噪声的射频数据估计得到的轴向剪切应变弹性图中提取这些特征的能力。此外，还研究了从良性和恶性肿瘤的体内乳腺数据中提取这些特征的可行性。有限元分析研究表明，轴向剪切应变分布模式由夹杂物/背景界面处的结合程度决定。结果表明，使用捕获正负轴向剪切应变区域的归一化特征来量化轴向剪切应变分布模式是可行的。模拟结果表明，从轴向剪切应变弹性图中提取有限元分析研究中确定的特征是可行的。然而，必须努力在不影响分辨率的情况下获得具有尽可能高的信噪比（SNR(asse)）的轴向剪切应变弹性图。体内结果证明了从乳腺数据生成和提取轴向剪切应变弹性图特征的可行性。此外，体内轴向剪切应变弹性图显示出一个在模拟中未识别出的额外特征，该特征可能潜在地用于区分良性和恶性肿瘤——轴向剪切应变区域与超声图中识别出的夹杂物/背景界面的接近程度。
