Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach.
Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach.
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DOI：
10.1016/j.cma.2018.12.030
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发表时间：
2019-04
影响因子：
7.2
通讯作者：
Minliang Liu;L. Liang;Wei Sun
中科院分区：
文献类型：
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作者：
Minliang Liu;L. Liang;Wei Sun
患者特异性的主动脉生物力学分析需要对个体患者的体内力学特性进行量化。目前的反演方法试图使用一定的优化方案从体像数据中估计非线性、各向异性的材料参数。然而，由于这种逆方法依赖于迭代非线性优化，这些方法的计算量很高。针对特定患者计算建模的瓶颈，一个潜在的范式改变解决方案是结合机器学习（ML）算法来加快体内材料参数识别的过程。在本文中，我们开发了一种基于ml的方法，从两种不同血压（即收缩压和舒张压）水平下获得的三维主动脉几何形状来估计材料参数。利用机器学习模型建立了两种载荷形状与本构参数之间的非线性关系，并利用有限元模拟数据集对该模型进行了训练和验证。交叉验证用于调整训练/验证数据集上的ml模型结构。使用测试数据集检查ml模型的准确性。
The patient-specific biomechanical analysis of the aorta requires the quantification of thein vivomechanical properties of individual patients. Current inverse approaches have attempted to estimate the nonlinear, anisotropic material parameters fromin vivoimage data using certain optimization schemes. However, since such inverse methods are dependent on iterative nonlinear optimization, these methods are highly computation-intensive. A potential paradigm-changing solution to the bottleneck associated with patient-specific computational modeling is to incorporate machine learning (ML) algorithms to expedite the procedure ofin vivomaterial parameter identification. In this paper, we developed an ML-based approach to estimate the material parameters from three-dimensional aorta geometries obtained at two different blood pressure (i.e., systolic and diastolic) levels. The nonlinear relationship between the two loaded shapes and the constitutive parameters is established by an ML-model, which was trained and tested using finite element (FE) simulation datasets. Cross-validations were used to adjust the ML-model structure on a training/validation dataset. The accuracy of the ML-model was examined using a testing dataset.