The Dmipy software package facilitates the reproducible estimation of diffusion MRI-based microstructure features. It does this by taking a completely modular approach to Microstructure Imaging. Using Dmipy you can design, fit, and recover the parameters of any multi-compartment microstructure model in usually less than 10 lines of code. Created models can be used to simulate and fit data for any PGSE-based dMRI acquisition, including single shell, multi-shell, multi-diffusion time and multi-TE acquisition schemes. Dmipy's main features include:
Complete Freedom in Model Design and Optimization
Human Connectome Project Data Interface Dmipy enables you to directly download any HCP subject data using your own credentials.
Numba-Accelerated, Multi-Core processing Dmipy takes heavy advantage of Python's pathos multi-core processing and numba function compilation.
Documentation on Tissue and Microstructure Models We include documentation and illustrations of all tissue models and parameter distributions, as well as example implementations and results on HCP data for Ball and Stick, Ball and Racket, NODDI-Watson/Bingham, AxCaliber, Spherical Mean models and more.
Dipy Compatibility Dmipy is designed to be complementary for Dipy users. Dipy gradient tables can be directly used in Dmipy models, Dipy models can be used to give initial parameter guesses for Dmipy optimization, and Dmipy models that estimate Fiber Orientation Distributions (FODs) can be visualized and used for tractography in Dipy.
Dmipy allows the user to do Microstructure Imaging research at the highest level, while the package automatically takes care of all the coding architecture that is needed to fit a designed model to a data set. The Dmipy documentation can be found at http://dmipy.readthedocs.io/. If you use Dmipy for your research publications, we kindly request you cite this package at the reference at the bottom of this page.
You can install dmipy using pypi by typing in your terminal
or you can manually
See solutions to common issues
Recommended to use Anaconda Python distribution.
To get a feeling for how to use Dmipy, we provide a few tutorial notebooks:
Dmipy uses HCP data to illustrate microstructure model examples. To reproduce these examples, dmipy provides a direct way to download HCP data (using your own AWS credentials) in the HCP tutorial.
Any Spherical Mean model can also estimate parametric FODs.
Constrained spherical deconvolution (CSD) models are primarily used for Fiber Orientation Distribution (FOD) estimation. Multi-Tissue CSD models improve FOD estimation by separating WM/GM/CSF signal contributions using multiple tissue response functions.
Dmipy's design is completely modular and can easily be extended with new models, distributions or optimizers. To contribute, view our contribution guidelines.
