Modeling shear modulus distribution in magnetic resonance elastography with piecewise constant level sets
Modeling shear modulus distribution in magnetic resonance elastography with piecewise constant level sets
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DOI：
10.1016/j.mri.2011.09.015
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发表时间：
2012-04-01
影响因子：
2.5
通讯作者：
Kobayashi, Etsuko
中科院分区：
文献类型：
--
作者：
Li, Bing Nan;Chui, Chee Kong;Kobayashi, Etsuko
磁共振弹性成像（MRE）是为软组织的力学特性成像而设计的。然而，对剪切模量分布的解释往往令人困惑和繁琐。为了进行可靠的评估，通常的做法是指定感兴趣的区域并考虑区域弹性。这种经验依赖的协议容易受到个人和人际变异性的影响。在这项研究中，我们提出了用分段常数水平集来重塑剪切模量分布，并参考相应的震级图像。采用一种由全局和局部交替竞争构成的混合水平集模型来实现最优的分割和配准。在模拟MRE数据集上的实验结果表明，局部频率估计弹性重构的平均误差为11.33%，微分方程代数反演弹性重构的平均误差为18.87%。分段常水平集建模可以有效地提高剪切模量分布质量，便于MRE分析和解释。(C) 2012爱思唯尔公司版权所有。
Magnetic resonance elastography (MRE) is designed for imaging the mechanical properties of soft tissues. However, the interpretation of shear modulus distribution is often confusing and cumbersome. For reliable evaluation, a common practice is to specify the regions of interest and consider regional elasticity. Such an experience-dependent protocol is susceptible to intrapersonal and interpersonal variability. In this study we propose to remodel shear modulus distribution with piecewise constant level sets by referring to the corresponding magnitude image. Optimal segmentation and registration are achieved by a new hybrid level set model comprised of alternating global and local region competitions. Experimental results on the simulated MRE data sets show that the mean error of elasticity reconstruction is 11.33% for local frequency estimation and 18.87% for algebraic inversion of differential equation. Piecewise constant level set modeling is effective to improve the quality of shear modulus distribution, and facilitates MRE analysis and interpretation. (C) 2012 Elsevier Inc. All rights reserved.