Deep 3D Convolution Neural Network Methods for Brain White Matter Hybrid Computational Simulations

2020 ◽  
Author(s):  
Xuehai Wu ◽  
Assimina A. Pelegri
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
White Matter ◽  
Finite Elements ◽  
Frequency Domain ◽  
Finite Element Methods ◽  
Material Properties ◽  
Output Data ◽  
Micro Scale ◽  
Brain White Matter

Abstract Material properties of brain white matter (BWM) show high anisotropy due to the complicated internal three-dimensional microstructure and variant interaction between heterogeneous brain-tissue (axon, myelin, and glia). From our previous study, finite element methods were used to merge micro-scale Representative Volume Elements (RVE) with orthotropic frequency domain viscoelasticity to an integral macro-scale BWM. Quantification of the micro-scale RVE with anisotropic frequency domain viscoelasticity is the core challenge in this study. The RVE behavior is expressed by a viscoelastic constitutive material model, in which the frequency-related viscoelastic properties are imparted as storage modulus and loss modulus for the composite comprised of axonal fibers and extracellular glia. Using finite elements to build RVEs with anisotropic frequency domain viscoelastic material properties is computationally very consuming and resource-draining. Additionally, it is very challenging to build every single RVE using finite elements since the architecture of each RVE is arbitrary in an infinite data set. The architecture information encoded in the voxelized location is employed as input data and is consequently incorporated into a deep 3D convolution neural network (CNN) model that cross-references the RVEs’ material properties (output data). The output data (RVEs’ material properties) is calculated in parallel using an in-house developed finite element method, which models RVE samples of axon-myelin-glia composites. This novel combination of the CNN-RVE method achieved a dramatic reduction in the computation time compared with directly using finite element methods currently present in the literature.

Brain White Matter Model of Orthotropic Viscoelastic Properties in Frequency Domain

2019 ◽  
Author(s):  
Xuehai Wu ◽  
John G. Georgiadis ◽  
Assimina A. Pelegri
Keyword(s):  
Finite Element ◽  
White Matter ◽  
Finite Element Model ◽  
Frequency Domain ◽  
Material Properties ◽  
Viscoelastic Properties ◽  
Diffusion Mri ◽  
Element Model ◽  
Brain White Matter ◽  
Macro Scale

Abstract Finite element analysis is used to study brain axonal injury and develop Brain White Matter (BWM) models while accounting for both the strain magnitude and the strain rate. These models are becoming more sophisticated and complicated due to the complex nature of the BMW composite structure with different material properties for each constituent phase. State-of-the-art studies, focus on employing techniques that combine information about the local axonal directionality in different areas of the brain with diagnostic tools such as Diffusion-Weighted Magnetic Resonance Imaging (Diffusion-MRI). The diffusion-MRI data offers localization and orientation information of axonal tracks which are analyzed in finite element models to simulate virtual loading scenarios. Here, a BMW biphasic material model comprised of axons and neuroglia is considered. The model’s architectural anisotropy represented by a multitude of axonal orientations, that depend on specific brain regions, adds to its complexity. During this effort, we develop a finite element method to merge micro-scale Representative Volume Elements (RVEs) with orthotropic frequency domain viscoelasticity to an integrated macro-scale BWM finite element model, which incorporates local axonal orientation. Previous studies of this group focused on building RVEs that combined different volume fractions of axons and neuroglia and simulating their anisotropic viscoelastic properties. Via the proposed model, we can assign material properties and local architecture on each element based on the information from the orientation of the axonal traces. Consecutively, a BWM finite element model is derived with fully defined both material properties and material orientation. The frequency-domain dynamic response of the BMW model is analyzed to simulate larger scale diagnostic modalities such as MRI and MRE.

scholarly journals Hyperelastic Material Properties of Axonal fibers in Brain White Matter

2021 ◽  
pp. 100035
Author(s):  
Poorya Chavoshnejad ◽  
Guy K. German ◽  
Mir Jalil Razavi
Keyword(s):  
White Matter ◽  
Material Properties ◽  
Hyperelastic Material ◽  
Brain White Matter

scholarly journals A Brief Review on Polygonal/Polyhedral Finite Element Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Logah Perumal
Keyword(s):  
Finite Element ◽  
Finite Elements ◽  
Finite Element Methods ◽  
Virtual Node ◽  
Advantages And Disadvantages

This paper provides brief review on polygonal/polyhedral finite elements. Various techniques, together with their advantages and disadvantages, are listed. A comparison of various techniques with the recently proposed Virtual Node Polyhedral Element (VPHE) is also provided. This review would help the readers to understand the various techniques used in formation of polygonal/polyhedral finite elements.

Microstructural Hyperelastic Characterization of Brain White Matter in Tension

2019 ◽  
Author(s):  
Mohammadreza Ramzanpour ◽  
Mohammad Hosseini-Farid ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Finite Element ◽  
White Matter ◽  
Human Brain ◽  
Volume Fraction ◽  
Fibrous Composite ◽  
Diffuse Axonal Injury ◽  
Stiffness Ratio ◽  
Fibrous Composite Material ◽  
Brain White Matter ◽  
The Brain

Abstract Axons as microstructural constituent elements of brain white matter are highly oriented in extracellular matrix (ECM) in one direction. Therefore, it is possible to model the human brain white matter as a unidirectional fibrous composite material. A micromechanical finite element model of the brain white matter is developed to indirectly measure the brain white matter constituents’ properties including axon and ECM under tensile loading. Experimental tension test on corona radiata conducted by Budday et al. 2017 [1] is used in this study and one-term Ogden hyperelastic constitutive model is applied to characterize its behavior. By the application of genetic algorithm (GA) as a black box optimization method, the Ogden hyperelastic parameters of axon and ECM minimizing the error between numerical finite element simulation and experimental results are measured. Inverse analysis is conducted on the resultant optimized parameters shows high correlation of coefficient (>99%) between the numerical and experimental data which verifies the accuracy of the optimization procedure. The volume fraction of axons in porcine brain white matter is taken to be 52.7% and the stiffness ratio of axon to ECM is perceived to be 3.0. As these values are not accurately known for human brain white matter, we study the material properties of axon and ECM for different stiffness ratio and axon volume fraction values. The results of this study helps to better understand the micromechanical structure of the brain and micro-level injuries such as diffuse axonal injury.

A Method for the Validation of Predictive Computations Using a Stochastic Approach

2004 ◽  
Vol 126 (3) ◽  
pp. 227-234 ◽  
Author(s):  
Manuel Pellissetti ◽  
Roger Ghanem
Keyword(s):  
Finite Element ◽  
Finite Element Methods ◽  
Material Properties ◽  
Stochastic Approach ◽  
Stochastic Property ◽  
Limited Data ◽  
Bernoulli Beam ◽  
Direct Differentiation Method ◽  
Stochastic Properties

Stochastic finite element methods provide predictions of the behavior of mechanical systems with randomly fluctuating material properties. Limited data is typically available for the characterization of these properties, introducing errors in their representation. In the present paper, the sensitivity of the response predictions with respect to the stochastic properties is analyzed, by means of the direct differentiation method (DDM). Explicit expressions for the dependence of certain statistics of the response on the statistics of the material property are obtained. The response sensitivities are then used to estimate the error in the response predictions, caused by the error in the representation of the stochastic property. Numerical results for a simple Bernoulli beam are presented.

scholarly journals Shape-Free Finite Element Method: Another Way between Mesh and Mesh-Free Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Song Cen ◽  
Ming-Jue Zhou ◽  
Yan Shang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Finite Elements ◽  
Finite Element Methods ◽  
High Performance ◽  
Mesh Quality ◽  
Mesh Free ◽  
Plane Element ◽  
Mesh Free Methods ◽  
Hybrid Stress

Performances of the conventional finite elements are closely related to the mesh quality. Once distorted elements are used, the accuracy of the numerical results may be very poor, or even the calculations have to stop due to various numerical problems. Recently, the author and his colleagues developed two kinds of finite element methods, named hybrid stress-function (HSF) and improved unsymmetric methods, respectively. The resulting plane element models possess excellent precision in both regular and severely distorted meshes and even perform very well under the situations in which other elements cannot work. So, they are calledshape-freefinite elements since their performances are independent to element shapes. These methods may open new ways for developing novel high-performance finite elements. Here, the thoughts, theories, and formulae of aboveshape-freefinite element methods were introduced, and the possibilities and difficulties for further developments were also discussed.

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