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From MRI to Mesh: Finite Element Brain Model Creation

This exemplar provides a reproducible workflow for structural magnetic resonance (MR) images to be transformed into a finite element brain mesh in LS-DYNA keyword (.k) format. It combines image preprocessing, segmentation and meshing in one clear MRI-to-mesh workflow. It also contains optional post-processing to enable a downstream FE simulations.

Brain strain demo

This brain mesh creation pipeline was scientifically developed by Dr Mazdak Ghajari, Dr Harry Duckworth, Mr Vahid Darvish, and Ms Emily Chan, all in the HEAD Lab at Imperial College London.

This exemplar was developed at Imperial College London by Ms Emily Chan in collaboration with Dr Miruna Serina from Research Software Engineering and Dr Jianliang Gao from Research Computing & Data Science at the Early Career Researcher Institute.

With suitable MRI-derived inputs, this pipeline allows users to turn an individual's brain anatomy into a subject-specific finite element mesh.

Disciplinary Background 🔬

In brain biomechanics research, subject-specific anatomical models are often generated from structural MRI data and converted into finite element meshes that can be used to simulate tissue deformation under mechanical loading. These models are widely used traumatic brain injury (TBI) research, where understanding how anatomy influences brain strain can help interpret injury mechanisms and improve predictive modelling. At the HEAD Lab, researchers have used the mesh generation pipeline to generate hundreds of brain FE models, enabling large-scale FE simulations in TBI research, particularly how anatomical differences of the brain affects injury.

Learning Outcomes 🎓

After completing this exemplar, you will be able to:

  • Process and visualise neuroimaging data using FSL, a useful open-source neuroimaging software library.
  • Interpret the structure of an LS-DYNA mesh file, understand how nodes, elements, parts, and model definitions.
  • Generate a subject-specific volumetric finite element brain mesh from structural MRI-derived inputs.
  • (Optional) Understand the basic requirements for setting up an LS-DYNA simulation, including the role of supporting files needed alongside the generated mesh.

Prerequisites ✅

Academic 📚

  • Basic familiarity with Python, including using libaries and calling functions.
  • Basic familiarity with simple Bash commands in Jupyter notebook environment, such as navigating directories, writing shell commands, and handling files (e.g. cd, cp, echo).
  • A general awareness of finite element modelling is helpful, but not required.

System 💻

  • Python 3.9 and above
  • Jupyter Notebook
  • FSL v6.0.7 and above, installed (requires ~20GB disk space) and configured properly
  • Sufficient disk space (estimate 2GB) for intermediate image files, and generated meshes
  • Ls-PrePost for visualising output brain mesh

Software Tools 🛠️

  • Programming language: Python, with some Bash commands used within Jupyter notebooks.
  • LS-Prepost: a GUI tool for pre- and post-processing of finite element models.
  • FSL - FMRIB Software Library: a library for brain imaging data analysis.

FSL must be correctly configured before running the notebooks. In particular, the FSLDIR environment variable must be set, and the FSL command-line tools must be available from your terminal.

After installing FSL, follow the official FSL configuration instructions. For bash or zsh, this usually means adding the following lines to your shell configuration file, such as ~/.bashrc, ~/.bash_profile, or ~/.zshrc:

# FSL Setup
FSLDIR=~/fsl
PATH=${FSLDIR}/share/fsl/bin:${PATH}
export FSLDIR PATH
. ${FSLDIR}/etc/fslconf/fsl.sh

Getting Started 🚀

Step guide

The main step-by-step materials are stored together in docs/. This folder contains both the explanatory Markdown pages and the executable notebooks used in the MRI-to-mesh pipeline.

  1. Fulfil the Prerequisites.
  2. Follow 01_Introduction.md for background information, required inputs, and the expected folder structure.
  3. Run 02_ImageProcess.ipynb to complete image processing and image segmentation.
  4. Follow 03_FSLeyes.md to inspect selected image-processing outputs in FSLeyes and check whether the segmentation worked as expected.
  5. Run 04_MeshCreation.ipynb to generate, smooth, and refine the finite element brain mesh.
  6. Follow 05_VisualiseMesh.md to inspect the generated brain mesh in LS-PrePost by rotating, slicing, and checking the model geometry.
  7. Optional: follow 06_Simulation.md to prepare supporting files for downstream finite element simulation.

Apart from running the notebooks in your local environment, they can also be run directly in GitHub Codespaces using the instructions in Try code on GitHub with Codespaces.

Try the notebooks on GitHub Codespaces

You can run the notebooks directly in GitHub Codespaces without setting up the environment locally. The steps are shown in the image below.

  1. In the repository, click the green Code button, open the Codespaces tab, and click the three-dot "..."* menu.
  2. Select New with options... from the menu.
  3. Under Machine type, choose 8-core, 32 GB RAM, 64 GB storage or higher (this requires signing up to GitHub Student Developer Pack if you have not already done so).
  4. Review the selected settings, then click Create codespace.
  5. Wait for GitHub Codespaces to launch VS Code in the browser and finish configuring the environment.
  6. Open the notebook you want to run from the notebooks/. Click Select Kernel, then choose Python Environments....
  7. Select the Recommended Python environment, for example the base environment, to run the notebook.

Step-by-step guide for setting up a GitHub Codespace

View neuroimages in Codespaces

There are two options available to view neuroimages.

  • Option1 (maybe slow) : Use Trame to view images
  • Find trame_nii_viewer.py and modify the line (16) /workspaces/ReCoDE-brain-mesh-creation/data/subjects/avg-male/tmp to the imaging files directory you want to view. Then save the updated trame_nii_viewer.py.

  • in a Codespace terminal, run the following command: 

    fslpython trame_nii_viewer.py
    A new tab will be opened on your web browser to view images. (be aware: if it is slow to load images, please refresh the Trame viewer tab)

  • Option2 (fast): Use fsleyes render command to view images

  • In Codespace terminal, run the following command: 

    fsleyes render --outfile output_T1.png data/subjects/sub0045/img/fs_seg/T1.nii.gz -cm brain_colours_nih

    • Modify the output filename output_T1.png as needed. This will be the exported PNG image.
    • Modify the input file path data/subjects/sub0045/img/fs_seg/T1.nii.gzi f you want to render another NIfTI file.
    • Modify -cm brain_colours_nih if you want to use a different colourmap. More colourmap codes can be found at fsleyes command

Project Structure 🗂️

.
├── data/                              # Input data, intermediate files, and generated outputs
│   └── subjects/
│       ├── sub0045/                   # Clean example subject for users to run through the workflow
│       │   └── img/
│       │       └── fs_seg/            # FreeSurfer-derived segmentation inputs
│       │           ├── T1.nii.gz      # T1-weighted structural MRI, used as input
│       │           ├── aseg.nii.gz    # Automated segmentation label map derived from the T1 image
│       │           └── brain.nii.gz   # Skull-stripped T1 volume containing brain tissue only
│       │
│       └── sub0045_example/           # Completed example subject with intermediate and final outputs
│           ├── img/
│           │   └── fs_seg/            # FreeSurfer-derived segmentation inputs
│           │       ├── T1.nii.gz
│           │       ├── aseg.nii.gz
│           │       └── brain.nii.gz
│           ├── bet/                   # Example BET outputs
│           ├── fast/                  # Example FAST outputs
│           └── output/                # Example generated brain mesh and supporting files
│ 
├── docs/                              # Markdown documentation and visual guides
│   ├── 01_Introduction.md             # Background, inputs, and preprocessing overview
│   ├── 02_ImageProcess.ipynb          # Code for MRI preprocessing and image segmentation workflow
│   ├── 03_FSLeyes.md                  # Guide for inspecting image-processing outputs
│   ├── 04_MeshCreation.ipynb          # Code for Mesh generation, smoothing, and refinement workflow
│   ├── 05_VisualiseMesh.md            # Guide for inspecting generated mesh outputs
│   ├── 06_Simulation.md               # Notes on preparing meshes for FE simulation
│   └── assets/                        # Images and media used in the documentation
│
├── src/                               # Source code and supporting tools used by the workflow
│   ├── brain_mesh_creation/           # Python package for mesh-generation utilities
│   │   ├── __init__.py
│   │   ├── mesh_utils.py              # Python utilities for mesh processing and refinement
│   │   └── bmctk.py                   # Script/module for brain mesh creation
│   └── dependencies/                  # Non-Python supporting files required by the workflow
│       └── rs/
│           ├── a.out                  # Executable for mesh smoothing
│           ├── look_up_table.txt      # Lookup table used during mesh creation
│           └── material_properties.k  # FE material property file for the brain model
│ 
├── trame_nii_viewer.py                # Interactive web viewer script for inspecting NIfTIs
├── mkdocs.yml                         # Configuration file for MkDocs
├── pyproject.toml                     # Project metadata and dependency configuration
├── requirements.txt                   # Optional pinned dependencies for local setup
├── LICENSE.md                         # Project license
└── README.md                          # This file; project overview and usage instructions
The repository includes two versions of the subject. The workflow notebooks are set up to run on sub0045 by default. The sub0045_example folder is provided for reference and comparison. You should use the sub0045 folder when following the notebooks.

Roadmap 🗺️

Core 🧩

  • Install prerequisites and setup environment
    • FSL installation
    • LS-Prepost installation
  • Image processing and segmentation
    • Does the FAST segmentation produce reasonable result?
    • Does the BET segmentation produce reasonable result?
    • Is the generated geometry file pre_model.nii.gz look reasonable in FSLeyes?
  • Mesh creation and smoothing
    • Does the generated mesh look reasonable in LS-PrePost?

Extensions 🔌

(Requires deeper understanding of finite element analysis)

  • Understand the supporting files for FE simulations using the generated mesh
    • Material property of the brain
    • Centre of gravity of the brain
    • Parts and sets of the brain model
    • Acceleration, the mechanical loading apply to the brain model
    • Run file, the configuration of FE simulation

Best Practice Notes 📝

  • During image processing and segmentation steps, regularly check the intermediate outputs using FSLeyes if you are running the project locally, or follow this if you are using with Codespaces.

Estimated Time ⏳

Task Estimated time
Introduction 10 min
Environment setup 30 min
Image processing 1 hour
Image visualisation 1 hour
Mesh creation 1 hour
Mesh visualisation 1 hour
Extension 30 min
Total Estimated Time 5 hours

Licence 📄

This project is licensed under the BSD-3-Clause license.