Development of high-quality hexahedral human brain meshes using feature-based multi-block approach
The finite element (FE) method is a powerful tool to study brain injury that remains to be a critical health concern. Subject/patient-specific FE brain models have the potential to accurately predict a specific subject/patient's brain responses during computer-assisted surgery or to design subj...
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          | Published in | Computer methods in biomechanics and biomedical engineering Vol. 16; no. 3; pp. 271 - 279 | 
|---|---|
| Main Authors | , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        England
          Taylor & Francis Group
    
        01.03.2013
     | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1025-5842 1476-8259 1476-8259  | 
| DOI | 10.1080/10255842.2011.617005 | 
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| Abstract | The finite element (FE) method is a powerful tool to study brain injury that remains to be a critical health concern. Subject/patient-specific FE brain models have the potential to accurately predict a specific subject/patient's brain responses during computer-assisted surgery or to design subject-specific helmets to prevent brain injury. Unfortunately, efforts required in the development of high-quality hexahedral FE meshes for brain, which consists of complex intracranial surfaces and varying internal structures, are daunting. Using multi-block techniques, an efficient meshing process to develop all-hexahedral FE brain models for an adult and a paediatric brain (3-year old) was demonstrated in this study. Furthermore, the mesh densities could be adjusted at ease using block techniques. Such an advantage can facilitate a mesh convergence study and allows more freedom for choosing an appropriate brain mesh density by balancing available computation power and prediction accuracy. The multi-block meshing approach is recommended to efficiently develop 3D all-hexahedral high-quality models in biomedical community to enhance the acceptance and application of numerical simulations. | 
    
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| AbstractList | The finite element (FE) method is a powerful tool to study brain injury that remains to be a critical health concern. Subject/patient-specific FE brain models have the potential to accurately predict a specific subject/patient's brain responses during computer-assisted surgery or to design subject-specific helmets to prevent brain injury. Unfortunately, efforts required in the development of high-quality hexahedral FE meshes for brain, which consists of complex intracranial surfaces and varying internal structures, are daunting. Using multi-block techniques, an efficient meshing process to develop all-hexahedral FE brain models for an adult and a paediatric brain (3-year old) was demonstrated in this study. Furthermore, the mesh densities could be adjusted at ease using block techniques. Such an advantage can facilitate a mesh convergence study and allows more freedom for choosing an appropriate brain mesh density by balancing available computation power and prediction accuracy. The multi-block meshing approach is recommended to efficiently develop 3D all-hexahedral high-quality models in biomedical community to enhance the acceptance and application of numerical simulations. The finite element (FE) method is a powerful tool to study brain injury that remains to be a critical health concern. Subject/patient-specific FE brain models have the potential to accurately predict a specific subject/patient's brain responses during computer-assisted surgery or to design subject-specific helmets to prevent brain injury. Unfortunately, efforts required in the development of high-quality hexahedral FE meshes for brain, which consists of complex intracranial surfaces and varying internal structures, are daunting. Using multi-block techniques, an efficient meshing process to develop all-hexahedral FE brain models for an adult and a paediatric brain (3-year old) was demonstrated in this study. Furthermore, the mesh densities could be adjusted at ease using block techniques. Such an advantage can facilitate a mesh convergence study and allows more freedom for choosing an appropriate brain mesh density by balancing available computation power and prediction accuracy. The multi-block meshing approach is recommended to efficiently develop 3D all-hexahedral high-quality models in biomedical community to enhance the acceptance and application of numerical simulations.The finite element (FE) method is a powerful tool to study brain injury that remains to be a critical health concern. Subject/patient-specific FE brain models have the potential to accurately predict a specific subject/patient's brain responses during computer-assisted surgery or to design subject-specific helmets to prevent brain injury. Unfortunately, efforts required in the development of high-quality hexahedral FE meshes for brain, which consists of complex intracranial surfaces and varying internal structures, are daunting. Using multi-block techniques, an efficient meshing process to develop all-hexahedral FE brain models for an adult and a paediatric brain (3-year old) was demonstrated in this study. Furthermore, the mesh densities could be adjusted at ease using block techniques. Such an advantage can facilitate a mesh convergence study and allows more freedom for choosing an appropriate brain mesh density by balancing available computation power and prediction accuracy. The multi-block meshing approach is recommended to efficiently develop 3D all-hexahedral high-quality models in biomedical community to enhance the acceptance and application of numerical simulations.  | 
    
| Author | Genthikatti, Vinay Veeranna Mao, Haojie Cao, Libo Gao, Haitao Yang, King H.  | 
    
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22149289$$D View this record in MEDLINE/PubMed | 
    
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| SubjectTerms | Adult Brain - pathology Brain Injuries - pathology brain injury Child, Preschool Finite Element Analysis finite element method hexahedral mesh Humans Magnetic Resonance Imaging multi-block technique patient-specific model subject-specific model  | 
    
| Title | Development of high-quality hexahedral human brain meshes using feature-based multi-block approach | 
    
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