Simulation of brain mass effect with an arbitrary Lagrangian and Eulerian FEM.
Simulation of brain mass effect with an arbitrary Lagrangian and Eulerian FEM.
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使用任意拉格朗日和欧拉有限元模拟脑质量效应。
DOI：
10.1007/978-3-642-15745-5_34
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Lin,Weili
中科院分区：
文献类型：
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作者：
Chen,Yasheng;Ji,Songbai;Wu,Xunlei;An,Hongyu;Zhu,Hongtu;Shen,Dinggang;Lin,Weili
质量效应引起的颅内应力分布的估计对于出血性中风或脑肿瘤患者的管理至关重要，这些患者可能会因脑组织压迫而遭受严重的继发性脑损伤。结合MRI所提供的生理参数，如组织灌注，对颅内应力分布进行无创、定量和区域性的估计，可以更好地了解脑组织在质量效应下的反应。由于与大脑的生物力学建模相关的多重挑战，服务于这一特定目的的定量和声音测量仍然难以捉摸。传统的拉格朗日框架有限元法（LFEM）面临的一个挑战是，质量效应的扩展导致的网格变形可能会在达到所需的压力载荷之前过早终止模拟。在这项工作中，我们采用了任意拉格朗日和欧拉有限元方法（ALEF）与显式动力学解模拟脑质量的压力负荷引起的影响。该方法由三个阶段组成：1）拉格朗日阶段变形网格像LFEM，2）网格平滑阶段，以减少网格失真，和3）欧拉阶段映射状态变量从旧网格到平滑的。在具有模拟几何形状的2D模拟中，与LFEM相比，这种方法能够模拟更大的变形。我们进一步将这种方法应用于具有3D真实的脑几何形状的模拟，以量化脑内von Mises应力的分布。
Estimation of intracranial stress distribution caused by mass effect is critical to the management of hemorrhagic stroke or brain tumor patients, who may suffer severe secondary brain injury from brain tissue compression. Coupling with physiological parameters that are readily available using MRI, eg, tissue perfusion, a non-invasive, quantitative and regional estimation of intracranial stress distribution could offer a better understanding of brain tissue’s reaction under mass effect. A quantitative and sound measurement serving this particular purpose remains elusive due to multiple challenges associated with biomechanical modeling of the brain. One such challenge for the conventional Lagrangian frame based finite element method (LFEM) is that the mesh distortion resulted from the expansion of the mass effects can terminate the simulation prematurely before the desired pressure loading is achieved. In this work, we adopted an arbitrary Lagrangian and Eulerian FEM method (ALEF) with explicit dynamic solutions to simulate the expansion of brain mass effects caused by a pressure loading. This approach consists of three phases: 1) a Lagrangian phase to deform mesh like LFEM, 2) a mesh smoothing phase to reduce mesh distortion, and 3) an Eulerian phase to map the state variables from the old mesh to the smoothed one. In 2D simulations with simulated geometries, this approach is able to model substantially larger deformations compared to LFEM. We further applied this approach to a simulation with 3D real brain geometry to quantify the distribution of von Mises stress within the brain.