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Predicting the Brain Injury Effects on Physical Arrangement of White Matter Neuronal Tracts using a Finite Element Head Model based on Tractography
, Ph.D. Dissertation Sharif University of Technology ; Farahmand, Farzam (Supervisor) ; Shamloo, Amir (Co-Supervisor) ; Oghabian, Mohammad Ali (Co-Supervisor)
Abstract
Diffuse tensor imaging or tractography is a useful method for tracking the axonal tracts pathways within the brain white matter by monitoring the movements of water molecules along the axons. The higher the level of the tissue anisotropy, the more accurate the pathways can be estimated. But in the swelling regions around an edematous tumor, the excess of watery fluid disrupts the directional movement of water molecules, and consequently, the diffuse tensor imaging is unable to track the pathways. This impairment should be resolved by predicting the axontal tracts arrangement in the blind regions of the images using the numerical modeling. To this end, a finite element model of the human...
Micromechanics of brain white matter tissue: a fiber-reinforced hyperelastic model using embedded element technique
, Article Journal of the Mechanical Behavior of Biomedical Materials ; Volume 80 , April , 2018 , Pages 194-202 ; 17516161 (ISSN) ; Shamloo, A ; Farahmand, F ; Sharif University of Technology
Elsevier Ltd
2018
Abstract
A transverse-plane hyperelastic micromechanical model of brain white matter tissue was developed using the embedded element technique (EET). The model consisted of a histology-informed probabilistic distribution of axonal fibers embedded within an extracellular matrix, both described using the generalized Ogden hyperelastic material model. A correcting method, based on the strain energy density function, was formulated to resolve the stiffness redundancy problem of the EET in large deformation regime. The model was then used to predict the homogenized tissue behavior and the associated localized responses of the axonal fibers under quasi-static, transverse, large deformations. Results...
Effect of axonal fiber architecture on mechanical heterogeneity of the white matter—a statistical micromechanical model
, Article Computer Methods in Biomechanics and Biomedical Engineering ; Volume 25, Issue 1 , 2022 , Pages 27-39 ; 10255842 (ISSN) ; Farahmand, F ; Ahmadian, M. T ; Sharif University of Technology
Taylor and Francis Ltd
2022
Abstract
A diffusion tensor imaging (DTI) -based statistical micromechanical model was developed to study the effect of axonal fiber architecture on the inter- and intra-regional mechanical heterogeneity of the white matter. Three characteristic regions within the white matter, i.e., corpus callosum, brain stem, and corona radiata, were studied considering the previous observations of locations of diffuse axonal injury. The embedded element technique was used to create a fiber-reinforced model, where the fiber was characterized by a Holzapfel hyperelastic material model with variable dispersion of axonal orientations. A relationship between the fractional anisotropy and the dispersion parameter of...
A three-dimensional micromechanical model of brain white matter with histology-informed probabilistic distribution of axonal fibers
, Article Journal of the Mechanical Behavior of Biomedical Materials ; Volume 88 , 2018 , Pages 288-295 ; 17516161 (ISSN) ; Farahmand, F ; Shamloo, A ; Sharif University of Technology
Elsevier Ltd
2018
Abstract
This paper presents a three-dimensional micromechanical model of brain white matter tissue as a transversely isotropic soft composite described by the generalized Ogden hyperelastic model. The embedded element technique, with corrected stiffness redundancy in large deformations, was used for the embedment of a histology-informed probabilistic distribution of the axonal fibers in the extracellular matrix. The model was linked to a multi-objective, multi-parametric optimization algorithm, using the response surface methodology, for characterization of material properties of the axonal fibers and extracellular matrix in an inverse finite element analysis. The optimum hyperelastic...