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Official Website: https://dsi-studio.labsolver.org
User Forum: https://groups.google.com/g/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
- 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)
- None. DSI Studio is distributed as a standalone executable (no installation or compilation needed)
- GPU version requires installation of CUDA toolkit.
- CPU with ≥4 cores
- ≥8 GB RAM
- NVIDIA GPU recommended for GPU version
- Visit the official download page
- Select the appropriate binary for your platform:
dsi_studio_64.exe(Windows)dsi_studio_mac.dmg(macOS)dsi_studio_ubuntu.zip(Linux)
- Extract the ZIP file (if needed)
- 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
- Launch
dsi_studio - Find one
.fzfile at THEFiber Data Tabtab and click on theOpen XXX.fzbutton to bring up tracking window
Alternatively, the data can be directly downloaded from Fiber Data Hub web portal
- Click on the
Fiber Trackingbutton to initiate fiber tracking
- Visualize tractography and export results using functions at the top menu
- Tract files (`.tt.gz)
- Anisotropy maps (
.nii.gz) - Connectivity matrices
⏱️ Runtime: ~1–3 minutes on a standard desktop
- Use File → Open → DICOM/NIfTI to import your raw diffusion MRI data
- Create
.szusing the “Step T1” conversion - Reconstruct using preferred method (e.g., GQI, DTI) to create
.fzfiles - Run tractography using custom or template ROIs
- Export tracts, metrics, and connectome data
Detailed documentation available at https://dsi-studio.labsolver.org/
DSI Studio supports full CLI scripting for batch processing.
Docs: https://dsi-studio.labsolver.org/doc/cli_t1.html
- Parameters are saved with output files
- All reconstructions and tracking are reproducible via GUI or CLI
- 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
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!