Development of high-quality hexahedral human brain meshes using feature-based multi-block approach

Area of Science:

• Biomedical Engineering
• Computational Mechanics
• Neuroscience

Background:

• Brain injury is a significant health concern requiring advanced predictive tools.
• Patient-specific finite element (FE) models offer potential for personalized surgical planning and injury prevention.
• Creating high-quality hexahedral meshes for complex brain anatomy is a major challenge.

Purpose of the Study:

• To present an efficient multi-block meshing technique for developing all-hexahedral finite element (FE) brain models.
• To demonstrate the adaptability of this technique for both adult and pediatric brain models.
• To highlight the benefits of this approach for mesh density control and computational efficiency.

Main Methods:

• Utilized multi-block techniques for hexahedral mesh generation of adult and pediatric (3-year-old) brain geometries.
• Focused on efficiently handling complex intracranial surfaces and internal anatomical variations.
• Emphasized the ease of adjusting mesh density through the block-based approach.

Main Results:

• Successfully developed all-hexahedral finite element (FE) brain models for adult and pediatric subjects.
• Demonstrated an efficient meshing process overcoming the complexities of brain anatomy.
• Showcased the ability to easily adjust mesh density for convergence studies and computational trade-offs.

Conclusions:

• The multi-block meshing approach provides an efficient method for generating high-quality, all-hexahedral 3D brain models.
• This technique facilitates the development of subject-specific finite element (FE) models, enhancing their application in research and clinical settings.
• Recommended for the biomedical community to improve the adoption and utility of numerical simulations in brain injury studies.