Tractography.jl

Tractography.jl is a high-performance Julia package for brain tractography that leverages parallel computing and specialized hardware (e.g., GPUs) to reconstruct white matter fiber bundles from diffusion-weighted MRI data. This enables researchers to study the structural connectivity of the brain at unprecedented scales.

Key Features

• GPU acceleration: Massive parallelization for processing billions of streamlines
• Multiple tracking algorithms: Including stochastic methods
• Flexible seeding: Generate seeds from FOD data or anatomical masks
• Visualization: Built-in plotting recipes for Makie.jl
• Export capabilities: Save tractograms to standard TCK format
• High performance: Successfully used to sample 500 billion streamlines on GPU

📦 Installation

Assuming you have Julia installed, add Tractography.jl using the package manager:

import Pkg
Pkg.add("Tractography")

📚 Citing this work

To come...

🧑‍💻 Related Software

Similar algorithms are implemented in the Python package:

• Cronos Tractography

Several excellent tractography software packages are available and listed on the IST website:

• DIPY - Comprehensive diffusion MRI analysis in Python
• DSI Studio - Diffusion MRI analysis tool
• Entrack - Deep learning-based tractography
• ExploreDTI - DTI and HARDI analysis toolbox
• MRtrix3 - Leading software for diffusion MRI analysis (lacks GPU support)
• Scilpy - Python tools for diffusion MRI processing
• Trekker - Fast parallel tractography

Julia Ecosystem

Tractography.jl is currently the only Julia package focused specifically on tractography. Related Julia packages include:

• Fibers.jl - Tools for diffusion MRI data
• Microstructure.jl - Microstructure modeling
• NeuroFormats.jl - Neuroimaging file format support
• JuliaNeuroscience - Neuroscience tools organization

Performance

The examples in this documentation prioritize clarity and simplicity over maximum performance. However, Tractography.jl is capable of extreme-scale tractography.

Proven at Scale

This package was used to sample 500 billion streamlines on GPU for a recent publication:

• Yanis Aeschlimann, Samuel Deslauriers-Gauthier, Romain Veltz. GPU tractography: What can we learn from half a trillion streamlines? International Society for Tractography Conference - IST 2025, Oct 2025, Bordeaux, France. ⟨hal-05272265⟩

This demonstrates the package's capability to handle production-scale neuroimaging research with GPU acceleration.