NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, Singapore.
Magn Reson Imaging. 2012 Apr;30(3):390-401. doi: 10.1016/j.mri.2011.09.015. Epub 2012 Jan 14.
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.
磁共振弹性成像（MRE）旨在对软组织的机械性能进行成像。然而，剪切模量分布的解释通常令人困惑且繁琐。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。这种依赖经验的方案容易受到个体内和个体间差异的影响。在这项研究中，我们通过参考相应的幅度图像，提出用分段常数水平集来重塑剪切模量分布。通过一种新的混合水平集模型实现了最优的分割和配准，该模型由交替的全局和局部区域竞争组成。在模拟的 MRE 数据集上的实验结果表明，局部频率估计的弹性重建平均误差为 11.33%，微分方程的代数反演的平均误差为 18.87%。分段常数水平集建模可有效提高剪切模量分布的质量，从而便于 MRE 分析和解释。
