A combined FEM/genetic algorithm for vascular soft tissue elasticity estimation.
A combined FEM/genetic algorithm for vascular soft tissue elasticity estimation.
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DOI：
10.1007/s10558-006-9013-5
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发表时间：
2006-09-01
影响因子：
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通讯作者：
Kaazempur Mofrad, Mohammad R
中科院分区：
文献类型：
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作者：
Khalil, Ahmad S;Bouma, Brett E;Kaazempur Mofrad, Mohammad R
组织弹性重建是结合成像、弹性成像和计算建模来建立软组织力学属性图的参数估计工作。一种应用是表征病变动脉中的动脉粥样硬化斑块，其中需要弹性特性的分布来进行应力分析和斑块稳定性评估。将用于软组织力学分析的有限元模型与用于参数估计的遗传算法相结合，提出了一种软组织弹性重建的计算方案。通过将离散的弹性值简化为集总材料区域，即斑块成分，可以使用稳健的自适应策略来求解涉及复杂和非均匀解空间的弹性反问题。利用遗传算法的一个优点是它坚持全局收敛。该算法易于实现，适用于更复杂的材料模型和几何形状。它的目的是在多分辨率方案中提供对低分辨率弹性值的准确初始猜测，或者作为对失败的传统弹性估计努力的替代。
Tissue elasticity reconstruction is a parameter estimation effort combining imaging, elastography, and computational modeling to build maps of soft tissue mechanical properties. One application is in the characterization of atherosclerotic plaques in diseased arteries, wherein the distribution of elastic properties is required for stress analysis and plaque stability assessment. In this paper, a computational scheme is proposed for elasticity reconstruction in soft tissues, combining finite element modeling (FEM) for mechanical analysis of soft tissues and a genetic algorithm (GA) for parameter estimation. With a model reduction of the discrete elasticity values into lumped material regions, namely the plaque constituents, a robust, adaptive strategy can be used to solve inverse elasticity problems involving complex and inhomogeneous solution spaces. An advantage of utilizing a GA is its insistence on global convergence. The algorithm is easily implemented and adaptable to more complex material models and geometries. It is meant to provide either accurate initial guesses of low-resolution elasticity values in a multi-resolution scheme or as a replacement for failing traditional elasticity estimation efforts.