dipy
Diffusion MRI Imaging in Python
Decision gist · record as of 2026-08-14
Yes, if you work with diffusion MRI data in a research context. DIPY is actively maintained, well-established (since 2011), and has no known vulnerabilities. Medium install friction is typical for scientific packages with compiled dependencies. Not suitable for clinical deployment without explicit approval from maintainers. Install via pip or conda; prebuilt wheels available for modern Python versions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires compiled dependencies (numpy, scipy, nibabel, h5py); installation may take several minutes on first setup.
- Medium install friction due to compiled dependencies (numpy, scipy, nibabel, h5py).
- Active maintenance with recent releases; last commit 2026-08-11.
License · maintenance · safety
permissive license (permissive) — BSD license (permissive). You may use, modify, and distribute DIPY freely in commercial or private projects provided you retain copyright notices and disclaimers. No patent indemnification or warranty.
last release 2026-04-23 (113 days) · last repo commit 2026-08-11 · 835 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 100,810 downloads/mo, #12,972 on PyPI
Alternatives
Verify before relying
pip install dipy
import dipy
from dipy.data import get_fnames
from dipy.io.image import load_nifti
hardi_fname, hardi_bval, hardi_bvec = get_fnames('stanford_hardi')
data, affine = load_nifti(hardi_fname)- Whether clinical deployment support exists despite the stated research-only disclaimer.
- Performance characteristics and scalability limits for large diffusion datasets.
- Availability of GPU acceleration or parallel processing capabilities.
What it is and what it does
DIPY is a specialized neuroimaging library for processing and analyzing diffusion magnetic resonance imaging (dMRI) data. It provides implementations of diffusion tensor imaging (DTI), tractography algorithms, fiber tracking, brain registration, segmentation, and microstructural analysis techniques commonly used in neuroscience research.
The library is built on top of numpy, scipy, and nibabel, and integrates with h5py for data I/O. It is designed for research workflows and explicitly disclaims clinical use without contacting the maintainers. The package follows Scientific Python's SPEC 0 versioning guidelines and maintains active development with broad platform support.
Use it for
- Perform fiber tractography and white matter bundle segmentation from diffusion-weighted MRI scans.
- Compute diffusion tensor metrics (FA, MD, RD, AD) for microstructural brain analysis.
- Register diffusion images to standard brain templates for group-level statistical analysis.
- Simulate diffusion signals for validation and testing of reconstruction algorithms.
- Extract connectome data and perform network analysis on tractography results.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with diffusion MRI data in a research context.
DIPY is actively maintained, well-established (since 2011), and has no known vulnerabilities. Medium install friction is typical for scientific packages with compiled dependencies. Not suitable for clinical deployment without explicit approval from maintainers. Install via pip or conda; prebuilt wheels available for modern Python versions.
Install
dipy on PyPI
Before you install
Medium install friction due to compiled dependencies (numpy, scipy, nibabel, h5py). Active maintenance with recent releases; last commit 2026-08-11. Supports Python 3.11 through 3.14 with prebuilt wheels across macOS, Linux, and Windows platforms.
Requires compiled dependencies (numpy, scipy, nibabel, h5py); installation may take several minutes on first setup.
License in practice
BSD license (permissive). You may use, modify, and distribute DIPY freely in commercial or private projects provided you retain copyright notices and disclaimers. No patent indemnification or warranty.
Quickstart
pip install dipy
import dipy
from dipy.data import get_fnames
from dipy.io.image import load_nifti
hardi_fname, hardi_bval, hardi_bvec = get_fnames('stanford_hardi')
data, affine = load_nifti(hardi_fname)Verify before relying
- Whether clinical deployment support exists despite the stated research-only disclaimer.
- Performance characteristics and scalability limits for large diffusion datasets.
- Availability of GPU acceleration or parallel processing capabilities.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 7 packagesnumpyscipynibabelh5pypackagingtqdmtrx-python |
| Maintenance | Actively maintained 113 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 100,810 / month, #12,972 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: OS IndependentOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: dipy-1.12.1-cp311-cp311-macosx_11_0_arm64.whl; dipy-1.12.1-cp311-cp311-macosx_11_0_x86_64.whl; dipy-1.12.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; dipy-1.12.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; dipy-1.12.1-cp311-cp311-win_amd64.whl; dipy-1.12.1-cp312-cp312-macosx_11_0_arm64.whl; dipy-1.12.1-cp312-cp312-macosx_11_0_x86_64.whl; dipy-1.12.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; dipy-1.12.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; dipy-1.12.1-cp312-cp312-win_amd64.whl; dipy-1.12.1-cp313-cp313-macosx_11_0_arm64.whl; dipy-1.12.1-cp313-cp313-macosx_11_0_x86_64.whl; dipy-1.12.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; dipy-1.12.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; dipy-1.12.1-cp313-cp313-win_amd64.whl; dipy-1.12.1-cp314-cp314-macosx_11_0_arm64.whl; dipy-1.12.1-cp314-cp314-macosx_11_0_x86_64.whl; dipy-1.12.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; dipy-1.12.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; dipy-1.12.1-cp314-cp314-win_amd64.whl
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