Neural Network Approaches for Soft Biological Tissue and Organ Simulations.
Neural Network Approaches for Soft Biological Tissue and Organ Simulations.
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用于软生物组织和器官模拟的神经网络方法。
DOI：
10.1115/1.4055835
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Zhang,Wenbo
中科院分区：
文献类型：
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作者：
Sacks,MichaelS;Motiwale,Shruti;Goodbrake,Christian;Zhang,Wenbo
考虑到软组织和器官的功能复杂性，很明显，计算机模拟对于理解它们以及开发治疗和替代品的合理基础至关重要。这种模拟的一个关键方面是考虑其复杂的，非线性的，各向异性的力学行为。虽然软组织材料模型已发展到高保真度，但计算机模拟实现通常使用有限元（FE）方法完成，该方法对于转化临床时间范围而言仍然非常缓慢。作为一个潜在的路径，解决高保真度的模拟能够在临床相关的时间范围内执行的发展，我们回顾了使用神经网络（NN）的软组织和器官模拟使用两种方法。在第一种方法中，我们展示了NN如何学习详细的细观结构软组织材料模型的响应。NN材料模型不仅再现了完整的各向异性力学响应，而且还表现出相当大的效率提高，因为它是在一系列可实现的纤维结构上训练的。在第二种方法中，我们进一步使用基于物理的代理模型直接学习位移场解，而无需原始训练数据或FE仿真数据集。在这种方法中，我们利用有限元网格来定义域并执行必要的积分，而不是有限元方法（FEM）本身。我们证明了这种方法，称为神经网络有限元（NNFE），结果在一个训练的NNFE模型与相应的“地面实况”FE解决方案在整个生理变形范围内的立方体心肌标本具有良好的协议。更重要的是，NNFE方法提供了一个显着减少计算时间的有限元网格尺寸范围。具体而言，随着FE网格尺寸从2744增加到175，615个元素，NNFE计算时间从0.1108 s增加到0.1393 s，而“地面实况”FE模型从4.541 s增加到719.9 s，具有相同的有效精度。这些结果表明，NNFE运行时间显着减少相比，传统的大变形为基础的有限元求解方法。然后，我们将展示如何将基于非均匀有理B样条（NURBS）的方法直接集成到NNFE方法中，作为处理真实的器官几何形状的一种手段。虽然这些方法和相关方法还处于早期阶段，但它们提供了一种在临床相关时间范围内执行复杂器官级模拟的方法，而不会影响准确性。
Given the functional complexities of soft tissues and organs, it is clear that computational simulations are critical in their understanding and for the rational basis for the development of therapies and replacements. A key aspect of such simulations is accounting for their complex, nonlinear, anisotropic mechanical behaviors. While soft tissue material models have developed to the point of high fidelity, in-silico implementation is typically done using the finite element (FE) method, which remains impractically slow for translational clinical time frames. As a potential path toward addressing the development of high fidelity simulations capable of performing in clinically relevant time frames, we review the use of neural networks (NN) for soft tissue and organ simulation using two approaches. In the first approach, we show how a NN can learn the responses for a detailed meso-structural soft tissue material model. The NN material model not only reproduced the full anisotropic mechanical responses but also demonstrated a considerable efficiency improvement, as it was trained over a range of realizable fibrous structures. In the second approach, we go a step further with the use of a physics-based surrogate model to directly learn the displacement field solution without the need for raw training data or FE simulation datasets. In this approach we utilize a finite element mesh to define the domain and perform the necessary integrations, but not the finite element method (FEM) itself. We demonstrate with this approach, termed neural network finite element (NNFE), results in a trained NNFE model with excellent agreement with the corresponding “ground truth” FE solutions over the entire physiological deformation range on a cuboidal myocardium specimen. More importantly, the NNFE approach provided a significantly decreased computational time for a range of finite element mesh sizes. Specifically, as the FE mesh size increased from 2744 to 175,615 elements, the NNFE computational time increased from 0.1108 s to 0.1393 s, while the “ground truth” FE model increased from 4.541 s to 719.9 s, with the same effective accuracy. These results suggest that NNFE run times are significantly reduced compared with the traditional large-deformation-based finite element solution methods. We then show how a nonuniform rational B-splines (NURBS)-based approach can be directly integrated into the NNFE approach as a means to handle real organ geometries. While these and related approaches are in their early stages, they offer a method to perform complex organ-level simulations in clinically relevant time frames without compromising accuracy.