DSI-Studio

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📘 Documentation: https://dsi-studio.labsolver.org/manual
💬 User Forum: https://groups.google.com/g/dsi-studio

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🧠 What is DSI Studio?

DSI Studio is a lightweight and user-friendly software for diffusion MRI analysis, tractography, and connectome mapping. It enables researchers and clinicians to:

• Perform deterministic fiber tracking and automated bundle mapping
• Reconstruct diffusion models
• Visualize and interactively edit brain tracts
• Export a wide variety of metrics and outputs

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💻 System Requirements

Supported Platforms

• Windows: 64-bit (Windows 10 or newer)
• macOS: Intel or Apple Silicon (macOS 13+)
• Linux: Ubuntu 18.04 or newer (tested on 20.04, 22.04)

Software Dependencies

• None. DSI Studio is distributed as a standalone executable (no installation or compilation needed)
• GPU version requires installation of CUDA toolkit.

Hardware Recommendations

• CPU with ≥4 cores
• ≥8 GB RAM
• NVIDIA GPU recommended for GPU version

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🚀 Installation Instructions

1. Visit the official download page
2. Select the appropriate binary for your platform:
   • dsi_studio_64.exe (Windows)
   • dsi_studio_mac.dmg (macOS)
   • dsi_studio_ubuntu.zip (Linux)
3. Extract the ZIP file (if needed)
4. Run the executable
   • Windows: no installation required
   • MacOS: check out the instructions at the download page for software execution permission
   • Ubuntu: no installation required

🕒 Install Time: Less than 1 minute

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🧪 Demo: Try it Now

Demo Dataset and How to Run

1. Launch dsi_studio
2. Find one .fz file at THE Fiber Data Tab tab and click on the Open XXX.fz button to bring up tracking window

Alternatively, the data can be directly downloaded from Fiber Data Hub web portal

3. Click on the Fiber Tracking button to initiate fiber tracking

4. Visualize tractography and export results using functions at the top menu

Output

• Tract files (`.tt.gz)
• Anisotropy maps (.nii.gz)
• Connectivity matrices

⏱️ Runtime: ~1–3 minutes on a standard desktop

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▶️ How to Use with Your Data

1. Use File → Open → DICOM/NIfTI to import your raw diffusion MRI data
2. Create .sz using the “Step T1” conversion
3. Reconstruct using preferred method (e.g., GQI, DTI) to create .fz files
4. Run tractography using custom or template ROIs
5. Export tracts, metrics, and connectome data

Detailed documentation available at https://dsi-studio.labsolver.org/

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⚙️ Command-Line Interface

DSI Studio supports full CLI scripting for batch processing.
Docs: https://dsi-studio.labsolver.org/doc/cli_t1.html

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🔁 Reproducibility

• Parameters are saved with output files
• All reconstructions and tracking are reproducible via GUI or CLI

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📬 Support and Community

• Tutorial videos: https://practicum.labsolver.org
• Forum (bug report, suggestions, troubleshooting): https://groups.google.com/g/dsi-studio
• Documentation: https://dsi-studio.labsolver.org/manual
• Issue tracker: https://github.com/frankyeh/DSI-Studio/issues

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📄 Citations

Please cite the methods you used (select only those applied to your study):

Population-based atlas and tracto-to-region connectome (2022): This study constructs a population-based probablistic tractography atlas and its associated tract-to-region connectome.

Yeh FC. Population-based tract-to-region connectome of the human brain and its hierarchical topology. Nature communications. 2022 Aug 22;13(1):1-3.

Shape Analysis (2020): Shape analysis is a morphology based quantification of tractography.

Yeh, Fang-cheng. "Shape Analysis of the Human Association Pathways." Neuroimage (2020).

Augmented fiber tracking (2020): The “augmented fiber tracking” are three strategies used to boost reproducibility of deterministic fiber tracking.

Yeh, Fang-cheng. "Shape Analysis of the Human Association Pathways." Neuroimage (2020).

SRC file quality control (2019): The “neighboring DWI correlation” is introduced in this study as a QC metrics for DWI.

Yeh, Fang-Cheng, et al. "Differential tractography as a track-based biomarker for neuronal injury." NeuroImage 202 (2019): 116131.

Topology informed pruning (TIP, 2019): A topology-based approach to remove false fiber trajectories.

Yeh, F. C., Panesar, S., Barrios, J., Fernandes, D., Abhinav, K., Meola, A., & Fernandez-Miranda, J. C. (2019). Automatic Removal of False Connections in Diffusion MRI Tractography Using Topology-Informed Pruning (TIP). Neurotherapeutics, 1-7.

connectometry (2016): connectometry is a statistical framework for testing the significance of correlational tractography.

Yeh, Fang-Cheng, David Badre, and Timothy Verstynen. "Connectometry: A statistical approach harnessing the analytical potential of the local connectome." NeuroImage 125 (2016): 162-171.

Restricted diffusion imaging (RDI, 2016): RDI is a model-free method that calculates the density of diffusing spins restricted within a given displacement distance.

Yeh, Fang-Cheng, Li Liu, T. Kevin Hitchens, and Yijen L. Wu, "Mapping Immune Cell Infiltration Using Restricted Diffusion MRI", Magn Reson Med. accepted, (2016)

Local connectome fingerprint (LCF, 2016): Local conectome fingerprint provides a subject-specific measurement for characterizing the white matter architectures and quantifying differences/similarity.

Yeh, F. C., Vettel, J. M., Singh, A., Poczos, B., Grafton, S. T., Erickson, K. I., ... & Verstynen, T. D. (2016). Quantifying differences and similarities in whole-brain white matter architecture using local connectome fingerprints. PLoS computational biology, 12(11), e1005203.

Individual connectometry (2013): Individual connectometry is atlas-based analysis method that tracks the deviant pathways of one individual (e.g. a patient) by comparing subject’s data with a normal population.

Yeh, Fang-Cheng, Pei-Fang Tang, and Wen-Yih Isaac Tseng. "Diffusion MRI connectometry automatically reveals affected fiber pathways in individuals with chronic stroke." NeuroImage: Clinical 2 (2013): 912-921.

Generalized deterministic tracking algorithm (2013): The fiber tracking algorithm implemented in DSI Studio is a generalized version of the deterministic tracking algorithm that uses quantitative anisotropy as the termination index.

Yeh, Fang-Cheng, et al. "Deterministic diffusion fiber tracking improved by quantitative anisotropy." (2013): e80713. PLoS ONE 8(11): e80713. doi:10.1371/journal.pone.0080713

Q-space diffeormophic reconstruction (QSDR, 2011): QSDR is a model-free method that calculates the orientational distribution of the density of diffusing water in a standard space.

Yeh, Fang-Cheng, and Wen-Yih Isaac Tseng, "NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction." Neuroimage 58.1 (2011): 91-99.

Generalized q-sampling imaging (GQI, 2010): GQI is a model-free method that calculates the orientational distribution of the density of diffusing water.

Yeh, Fang-Cheng, Van Jay Wedeen, and Wen-Yih Isaac Tseng, "Generalized q-sampling imaging" Medical Imaging, IEEE Transactions on 29.9 (2010): 1626-1635.

Let me know if you'd like to add example commands, a reproducibility checklist, or a BibTeX citation block!