首页> 外文期刊>Computer Modeling in Engineering & Sciences >Patient-Specific Carotid Plaque Progression Simulation Using 3D Meshless Generalized Finite Difference Models with Fluid-Structure Interactions Based on Serial In Vivo MRI Data
【24h】

Patient-Specific Carotid Plaque Progression Simulation Using 3D Meshless Generalized Finite Difference Models with Fluid-Structure Interactions Based on Serial In Vivo MRI Data

机译:基于串行体内MRI数据的3D无网格广义有限差分模型与流体-结构相互作用的患者特定颈动脉斑块进展模拟

获取原文
获取原文并翻译 | 示例
           

摘要

Previously, we introduced a computational procedure based on three-dimensional meshless generalized finite difference (MGFD) method and serial magnetic resonance imaging (MRI) data to quantify patient-specific carotid atherosclerotic plaque growth functions and simulate plaque progression. Structure-only models were used in our previous report. In this paper, fluid-stricture interaction (FSI) was added to improve on prediction accuracy. One participating patient was scanned three times (T1, T2, and T3, at intervals of about 18 months) to obtain plaque progression data. Blood flow was assumed to laminar, Newtonian, viscous and incompressible. The Navier-Stokes equations with arbitrary Lagrangian-Eulerian (ALE) formulation were used as the governing equations. Plaque material was assumed to be uniform, homogeneous, isotropic, linear, and nearly incompressible. The linear elastic model was used. The 3D FSI plaque model was discretized and solved using a meshless generalized finite difference (GFD) method. Growth functions with a) morphology alone; b) morphology and plaque wall stress (PWS); morphology and flow shear stress (FSS), and d) morphology, PWS and FSS were introduced to predict future plaque growth based on previous time point data. Starting from the T2 plaque geometry, plaque progression was simulated by solving the FSI model and adjusting plaque geometry using plaque growth functions iteratively until T3 is reached. Numerically simulated plaque progression agreed very well with the target T3 plaque geometry with errors ranging from 8.62%, 7.22%, 5.77% and 4.39%, with the growth function including morphology, plaque wall stress and flow shear stress terms giving the best predictions. Adding flow shear stress term to the growth function improved the prediction error from 7.22% to 4.39%, a 40% improvement. We believe this is the first time 3D plaque progression FSI simulation based on multi-year patient-tracking data was reported. Serial MRI-based progression simulation adds time dimension to plaque vulnerability assessment and will improve prediction accuracy for potential plaque rupture risk.
机译:以前,我们介绍了一种基于三维无网格广义有限差分(MGFD)方法和串行磁共振成像(MRI)数据的计算程序,以量化患者特定的颈动脉粥样硬化斑块生长功能并模拟斑块进展。我们先前的报告中使用了仅结构模型。在本文中,增加了流固耦合(FSI)以提高预测精度。对一名参与患者进行了3次扫描(T1,T2和T3,间隔约18个月),以获取斑块进展数据。假定血流为层流,牛顿性,粘性和不可压缩的。使用具有任意拉格朗日-欧拉(ALE)公式的Navier-Stokes方程作为控制方程。斑块材料被认为是均匀,均质,各向同性,线性和几乎不可压缩的。使用线性弹性模型。使用无网格广义有限差分(GFD)方法离散化并求解3D FSI斑块模型。生长功能与a)单独的形态; b)形态和斑块壁应力(PWS);形态和流动剪切应力(FSS)以及d)形态,PWS和FSS被引入以根据先前的时间点数据来预测将来的菌斑生长。从T2斑块的几何形状开始,通过求解FSI模型并使用斑块生长函数迭代地调整斑块的几何形状来模拟斑块的进展,直到达到T3。数值模拟的斑块进展与目标T3斑块几何形状非常吻合,误差范围为8.62%,7.22%,5.77%和4.39%,其增长函数包括形态学,斑块壁应力和流动切应力,可提供最佳预测。将流量剪切应力项添加到增长函数可以将预测误差从7.22%提高到4.39%,提高了40%。我们相信这是首次报告基于多年患者追踪数据的3D斑块进展FSI模拟。基于串行MRI的进展模拟为斑块易损性评估增加了时间维度,并将提高潜在斑块破裂风险的预测准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号