Department of Electrical & Computer Engineering, University of Western Ontario, London, ON, Canada.
Phys Med Biol. 2010 Dec 21;55(24):7489-508. doi: 10.1088/0031-9155/55/24/007. Epub 2010 Nov 19.
In breast elastography, breast tissue usually undergoes large compression resulting in significant geometric and structural changes. This implies that breast elastography is associated with tissue nonlinear behavior. In this study, an elastography technique is presented and an inverse problem formulation is proposed to reconstruct parameters characterizing tissue hyperelasticity. Such parameters can potentially be used for tumor classification. This technique can also have other important clinical applications such as measuring normal tissue hyperelastic parameters in vivo. Such parameters are essential in planning and conducting computer-aided interventional procedures. The proposed parameter reconstruction technique uses a constrained iterative inversion; it can be viewed as an inverse problem. To solve this problem, we used a nonlinear finite element model corresponding to its forward problem. In this research, we applied Veronda-Westmann, Yeoh and polynomial models to model tissue hyperelasticity. To validate the proposed technique, we conducted studies involving numerical and tissue-mimicking phantoms. The numerical phantom consisted of a hemisphere connected to a cylinder, while we constructed the tissue-mimicking phantom from polyvinyl alcohol with freeze-thaw cycles that exhibits nonlinear mechanical behavior. Both phantoms consisted of three types of soft tissues which mimic adipose, fibroglandular tissue and a tumor. The results of the simulations and experiments show feasibility of accurate reconstruction of tumor tissue hyperelastic parameters using the proposed method. In the numerical phantom, all hyperelastic parameters corresponding to the three models were reconstructed with less than 2% error. With the tissue-mimicking phantom, we were able to reconstruct the ratio of the hyperelastic parameters reasonably accurately. Compared to the uniaxial test results, the average error of the ratios of the parameters reconstructed for inclusion to the middle and external layers were 13% and 9.6%, respectively. Given that the parameter ratios of the abnormal tissues to the normal ones range from three times to more than ten times, this accuracy is sufficient for tumor classification.
在乳腺弹性成像中，乳腺组织通常会经历较大的压缩，导致显著的几何和结构变化。这意味着乳腺弹性成像与组织的非线性行为有关。在本研究中，提出了一种弹性成像技术，并提出了一种反问题公式来重建表征组织超弹性的参数。这些参数可能可用于肿瘤分类。该技术还具有其他重要的临床应用，如在体内测量正常组织的超弹性参数。这些参数对于规划和进行计算机辅助介入性手术至关重要。所提出的参数重建技术使用受约束的迭代反演；它可以被视为一个反问题。为了解决这个问题，我们使用了与正向问题相对应的非线性有限元模型。在本研究中，我们应用了 Veronda-Westmann、Yeoh 和多项式模型来模拟组织的超弹性。为了验证所提出的技术，我们进行了涉及数值和组织模拟体模的研究。数值体模由一个与圆柱体相连的半球组成，而我们则通过具有非线性力学行为的冻融循环的聚乙烯醇构建了组织模拟体模。这两个体模都由三种类型的软组织组成，分别模拟脂肪、纤维腺体组织和肿瘤。模拟和实验结果表明，使用所提出的方法可以准确地重建肿瘤组织的超弹性参数。在数值体模中，所有三个模型对应的超弹性参数的重建误差都小于 2%。对于组织模拟体模，我们能够合理准确地重建超弹性参数的比值。与单轴测试结果相比，重建的夹杂物与中层和外层之间的参数比值的平均误差分别为 13%和 9.6%。考虑到异常组织与正常组织的参数比值范围为三倍到十倍以上，这种准确性足以用于肿瘤分类。
