Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Comput Methods Programs Biomed. 2024 Nov;256:108381. doi: 10.1016/j.cmpb.2024.108381. Epub 2024 Aug 22.
Material characterization of brain white matter (BWM) is difficult due to the anisotropy inherent to the three-dimensional microstructure and the various interactions between heterogeneous brain-tissue (axon, myelin, and glia). Developing full scale finite element models that accurately represent the relationship between the micro and macroscale BWM is however extremely challenging and computationally expensive. The anisotropic properties of the microstructure of BWM computed by building unit cells under frequency domain viscoelasticity comprises of 36 individual constants each, for the loss and storage moduli. Furthermore, the architecture of each unit cell is arbitrary in an infinite dataset.
In this study, we extend our previous work on developing representative volume elements (RVE) of the microstructure of the BWM in the frequency domain to develop 3D deep learning algorithms that can predict the anisotropic composite properties. The deep 3D convolutional neural network (CNN) algorithms utilizes a voxelization method to obtain geometry information from 3D RVEs. The architecture information encoded in the voxelized location is employed as input data while cross-referencing the RVEs' material properties (output data). We further develop methods by incorporating parallel pathways, Residual Neural Networks and inception modulus that improve the efficiency of deep learning algorithms.
This paper presents different CNN algorithms in predicting the anisotropic composite properties of BWM. A quantitative analysis of the individual algorithms is presented with the view of identifying optimal strategies to interpret the combined measurements of brain MRE and DTI.
The proposed Multiscale 3D ResNet (M3DR) algorithm demonstrates high learning ability and performance over baseline CNN algorithms in predicting BWM tissue properties. The hybrid M3DR framework also overcomes the significant limitations encountered in modeling brain tissue using finite elements alone including those such as high computational cost, mesh and simulation failure. The proposed framework also provides an efficient and streamlined platform for implementing complex boundary conditions, modeling intrinsic material properties and imparting interfacial architecture information.
由于三维微观结构固有的各向异性以及异质脑组织（轴突、髓鞘和神经胶质）之间的各种相互作用，脑白质（BWM）的材料特性表征具有一定难度。然而，开发能够准确表示微观和宏观 BWM 之间关系的全尺度有限元模型极具挑战性，并且计算成本高昂。在频域粘弹性下构建单元胞以计算 BWM 微观结构的各向异性特性时，涉及到 36 个单独的常数，用于损耗和储能模量。此外，在无限数据集下，每个单元胞的结构都是任意的。
在本研究中，我们扩展了之前在频域中开发 BWM 微观结构代表性体积元（RVE）的工作，以开发能够预测各向异性复合材料性能的 3D 深度学习算法。深度 3D 卷积神经网络（CNN）算法利用体素化方法从 3D RVE 中获取几何信息。体素化位置编码的结构信息被用作输入数据，同时交叉引用 RVE 的材料特性（输出数据）。我们进一步通过结合并行途径、残差神经网络和初始模量来开发方法，以提高深度学习算法的效率。
本文提出了不同的 CNN 算法来预测 BWM 的各向异性复合材料性能。通过分析个别算法的定量结果，提出了优化策略，以解释脑 MRE 和 DTI 的综合测量结果。
与基线 CNN 算法相比，所提出的多尺度 3D ResNet（M3DR）算法在预测 BWM 组织特性方面具有更高的学习能力和性能。混合 M3DR 框架还克服了单独使用有限元建模脑组织时遇到的重大限制，包括计算成本高、网格和模拟失败等问题。所提出的框架还为实施复杂边界条件、建模固有材料特性和赋予界面结构信息提供了高效、精简的平台。
