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
- Any combination of tissue models (e.g. Gaussian, Cylinder, Sphere) and axon bundle representation (e.g. orientation-dispersed/diameter-distributed) can be combined into a multi-compartment model.
- Any appropriate model can be orientation-dispersed and/or axon diameter-distributed.
- Any predefined or custom parameter constraints or relations can be imposed.
- Free choice of global optimizer to fit your model to the data (Brute-Force or Stochastic)
- Ability to also fit the spherical mean of any multi-compartment model to the spherical mean of the data.
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.
- clone repository
- python setup.py install
See solutions to common issues
Recommended to use Anaconda Python distribution.
- numpy >= 1.13
- scipy
- dipy
- boto (optional for HCP-AWS interface)
- pathos (optional for multi-core processing)
- numba (optional for faster functions)
To get a feeling for how to use Dmipy, we provide a few tutorial notebooks:
- Setting up an acquisition scheme
- Simulating and fitting data using a simple Stick model
- Combining biophysical models into a Microstructure model
- Creating a dispersed and/or distributed axon bundle representation
- Imposing parameter links and constraints
- Parameter Cascading: Using a simple model to initialize a complex one
- Cylinder Models (Axons, e.g. [Assaf et al. 2004])
- Sphere Models (Tumor cells, e.g. [Panagiotaki et al. 2014])
- Parameter Distribution Models (Axon Diameter Distribution, e.g. [Assaf et al. 2008])
- Gaussian Models (Extra-axonal, e.g. [Behrens et al. 2003])
- Spherical Distribution Models (Axon Dispersion, e.g. [Kaden et al. 2007])
- Spherical Mean of any Compartment Model
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.
- Ball and Stick [Behrens et al. 2003]
- Ball and Racket [Sotiropoulos et al. 2012]
- NODDI-Watson [Zhang et al. 2012]
- NODDI-Bingham [Tariq et al. 2016]
- AxCaliber [Assaf et al. 2008]
Any Spherical Mean model can also estimate parametric FODs.
- Spherical Mean Technique [Kaden et al. 2015]
- Multi-Compartment Microscopic Diffusion Imaging [Kaden et al. 2016]
Dmipy's design is completely modular and can easily be extended with new models, distributions or optimizers. To contribute, view our contribution guidelines.
- Temporarily Reference: Rutger Fick, Demian Wassermann and Rachid Deriche Mipy: An Open-Source Framework to improve reproducibility in Brain Microstructure Imaging, OHBM (2018) Singapore.
- Github Repository: Rutger Fick, Rachid Deriche, & Demian Wassermann. (2018, March 3). Dmipy: An Open-source Framework for Reproducible dMRI-Based Microstructure Research (Version 0.1). Zenodo. http://doi.org/10.5281/zenodo.1188268