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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

Yousefsani, Abdolmajid | 2018

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 51368 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Farahmand, Farzam; Shamloo, Amir; Oghabian, Mohammad Ali
  7. Abstract:
  8. 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 brain was presented based on physical modeling and inclusion of the white matter axonal tracts within the extracellular matrix using the embedded element technique. Foremost, in the framework of micromechanical modeling of white matter tissue, a theoretical approach was introduced for the first time to tackle the stiffness redundancy problem of the embedded element technique in the nonlinear large deformation regime. For the transverse-plane two-dimensional representative volume element of the white matter with histology-informed probabilistic microstructure, comparison of the simulation results with those obtained by the conventional direct meshing method indicated that the proposed theoretical approach can completely correct the stiffness redundancy. Afterwards, to find the hyperelastic characteristics of the white matter constituents, the micromechanical model was generalized to three dimensions to capture both the axial and transverse responses of the tissue. Then, in an inverse analysis approach, a theoretical method and a multi-parameter multi-objective optimization procedure were developed to find the hyperelastic properties of the axonal fibers and the extracellular matrix based on the experimental test results in both the axonal and transverse directions. Finally, the results of previous steps were used to build the tractography-informed model of brain subjected to tumor growth. At the initial stage of growth, a scaled model of tumor was built in the brain model, and the initial geometry of axonal tracts obtained from diffusion tensor imaging data of the opposite hemisphere was embedded in the brain model. Simulation of the tumor growth lasted when it reached the current size in the T1 images. At the end of simulation, the final arrangement of the axonal tracts around the tumor were in a good agreement with the corresponding tractography data. The model also predicted the fiber pathways in blind portions of the diffusion tensor images. Results showed that, unlike previous models that can only simulate the directional stiffness of the white matter tissue as a homogeneous media with no contribution to its microstructure, the present model can predict how the axonal tracts arrangement changes during progression of an injury such as the tumor growth and estimate the three-dimensional localized stress/strain fields
  9. Keywords:
  10. Large Deformation ; Micromechanical Modeling ; Brain Modeling based on Tractography ; Embedded Element Technique ; White Matter Micromechanical Modeling ; Multi-Parameter Optimization ; Stiffness Redundancy

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