Tractography is a 3D modeling technique used to visually represent brain tracts of data collected by a special MRI technique called diffusion MRI (dMRI). This data allows researchers, doctors and clinicians to perform structural connectivity analysis of the human brain. Usually, dMRI studies aim to compare connectivity patterns across groups of subjects/patients and certain disease populations to understand abnormalities in the brain’s white matter connectivity and the distributions of biologically sensitive dMRI derived metrics. FiberStars is a visual analysis tool for analyzing dMRI data that allows an interactive visualization of brain tracts, so called 'fiber-clusters' by combining the existing 3D brain anatomy with 2D visualizations. With FiberStars, researchers can analyze and compare multiple subjects/patients in large collections of brain fibers using different types of views.
git clone https://github.com/lorifranke/FiberStars.git
To use the tool with your own data, navigate to the cloned repository on your local computer. Then, put your own data (this includes a .csv file containing the meta data and the files for the fiber cluters as .vtp or .tko) in the folder public/data.
Optionally, you can convert your data as well to the 28x compressed .tko format with this tool: https://github.com/bostongfx/TRAKO to display it with FiberStars.
To run this tool in your browser, you will need npm, a package manager for the Node JavaScript platform. More information and the documentation can be found here: https://docs.npmjs.com/cli/v9/commands/npm
npm install # first time only
npm start
- Add
"homepage": "https://lorifranke.github.io/FiberStars/"topackage.jsonwhere the url should point to your own github repository page where you want to deploy the tool. npm run deploy
Copyright (c) 2022 The Fiberstars Developers. Fiberstars is licensed under the MIT License: http://www.opensource.org/licenses/mit-license.php
@inproceedings{franke2021fiberstars,
title={FiberStars: Visual Comparison of Diffusion Tractography Data between Multiple Subjects},
author={Franke, Loraine and Weidele, Daniel Karl I and Zhang, Fan and Cetin-Karayumak, Suheyla and Pieper, Steve and O’Donnell, Lauren J and Rathi, Yogesh and Haehn, Daniel},
booktitle={2021 IEEE 14th Pacific Visualization Symposium (PacificVis)},
pages={116--125},
year={2021},
organization={IEEE}
}