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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Dec 13, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Published on: June 30, 2018

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models.

J Sebastian Giudice¹, Ahmed Alshareef¹, Taotao Wu¹

¹Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, 4040 Lewis and Clark Dr., Charlottesville, VA, 229011, USA.

Annals of Biomedical Engineering

|July 30, 2020

Summary

This study introduces an automated pipeline for creating subject-specific finite element (FE) brain models. This method accurately predicts brain strain from traumatic brain injury simulations, reducing model creation time significantly.

Keywords:

Computational mechanics Magnetic resonance imaging (MRI)Personalized medicine Traumatic brain injury (TBI)

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Area of Science:

Biomechanics
Neuroimaging
Computational Modeling

Background:

Finite element (FE) models are essential for studying traumatic brain injury (TBI) mechanisms.
Manual creation of subject-specific FE brain models is labor-intensive and limits research scope.

Purpose of the Study:

To develop an automated pipeline for generating subject-specific FE brain models.
To preserve intricate neuroanatomical details during automated model generation.
To validate the accuracy of automated models against manually created ones.

Main Methods:

Utilized nonlinear image registration techniques to automatically generate FE brain models.
Compared strain distributions from automatically generated (morphed) models with manually created voxel models.
Simulated TBI using head kinematics from a football concussion case across 44 subjects.

Main Results:

Automated models accurately predicted brain strain distributions, consistent with manual models.
Differences in strain prediction between automated and manual models were less than 4%.
The automated pipeline significantly reduces model generation time to approximately 2 hours per subject.

Conclusions:

The automated FE brain model generation pipeline is efficient and accurate.
This technique facilitates interdisciplinary research in biomechanics and neuroimaging.
Automated models hold potential for clinical applications in TBI diagnosis and management.