Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications.

Area of Science:

• Neuroscience
• Biomechanics
• Medical Imaging

Background:

• Finite element (FE) head models are vital for traumatic brain injury (TBI) research.
• Generating subject-specific models, especially for diverse age groups and pathological conditions, is challenging and limits personalized simulations.
• Existing methods struggle with significant anatomical variations.

Purpose of the Study:

• To present a novel personalization framework for generating subject-specific FE head models.
• To enable accurate modeling across the lifespan and for brains with pathological changes.
• To overcome the bottleneck in personalized TBI simulations.

Main Methods:

• A framework utilizing hierarchical multiple feature and multimodality imaging registrations.
• Mesh morphing and mesh grouping techniques were employed.
• The framework was tested on a heterogeneous dataset including a newborn, children, adults, an elderly individual, and a hydrocephalus patient.

Main Results:

• The framework efficiently generated personalized FE head models for subjects with significant anatomical differences.
• Competitive personalization accuracy was achieved across the diverse age range and pathological case.
• The generated models demonstrated the framework's capacity for handling heterogeneous anatomical data.

Conclusions:

• The developed framework facilitates the efficient generation of subject-specific FE head models.
• This advancement supports personalized simulations in neuroscience, particularly for studying age-dependent and groupwise brain injury mechanisms.
• The framework addresses the need for reliable head models in TBI research across diverse populations.