Curl-Based Finite Element Reconstruction of the Shear Modulus Without Assuming Local Homogeneity: Time Harmonic Case
Curl-Based Finite Element Reconstruction of the Shear Modulus Without Assuming Local Homogeneity: Time Harmonic Case
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DOI：
10.1109/tmi.2013.2276060
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发表时间：
2013-12-01
影响因子：
10.6
通讯作者：
Salcudean, Septimiu E.
中科院分区：
文献类型：
--
作者：
Honarvar, Mohammad;Sahebjavaher, Ramin;Salcudean, Septimiu E.
在弹性成像中，通过求解基于描述组织运动的波动方程的反问题，从测量的组织位移数据中获得剪切模量。在大多数反演方法中，波动方程采用局部均匀性和不可压缩性假设进行简化。这将导致准确性的损失，从而在生成的弹性图像中产生成像伪影。在本文中，我们提出了一种新的基于卷的有限元方法反演技术，它不依赖于这些简化的假设。与以往的研究一样，我们使用旋度算子来消除波动方程中的膨胀项，但我们没有做局部均匀性的假设。我们使用来自虚拟组织幻影的模拟数据来评估我们的方法，假设时间调和运动和组织的线性，各向同性，弹性行为。结果表明，我们的重建结果优于以往基于均匀性假设的基于卷发的方法。我们还表明，使用我们的方法，在二维情况下，多频测量比单频测量提供更好的结果。CIRS弹性模体的磁共振弹性成像实验结果证实了我们的仿真结果，并以定量和可重复的方式进一步证明了我们的方法是准确和稳健的。
In elasticity imaging, the shear modulus is obtained from measured tissue displacement data by solving an inverse problem based on the wave equation describing the tissue motion. In most inversion approaches, the wave equation is simplified using local homogeneity and incompressibility assumptions. This causes a loss of accuracy and therefore imaging artifacts in the resulting elasticity images. In this paper we present a new curl-based finite element method inversion technique that does not rely upon these simplifying assumptions. As done in previous research, we use the curl operator to eliminate the dilatational term in the wave equation, but we do not make the assumption of local homogeneity. We evaluate our approach using simulation data from a virtual tissue phantom assuming time harmonic motion and linear, isotropic, elastic behavior of the tissue. We show that our reconstruction results are superior to those obtained using previous curl-based methods with homogeneity assumption. We also show that with our approach, in the 2-D case, multi-frequency measurements provide better results than single-frequency measurements. Experimental results from magnetic resonance elastography of a CIRS elastography phantom confirm our simulation results and further demonstrate, in a quantitative and repeatable manner, that our method is accurate and robust.