Package: dmipy
Dmipy: Diffusion Microstructure Imaging in PythonThe Dmipy software package facilitates thereproducible 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 OptimizationAny 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).Fit the spherical mean of any multi-compartment model to the spherical mean of the data.Generalized multi-compartment constrained spherical deconvolution.Human Connectome Project Data InterfaceDmipy enables you to directly download any HCP subject data using your own credentials.Numba-Accelerated, Multi-Core processingDmipy takes heavy advantage of Python'spathosmulti-core processing andnumbafunction compilation.Documentation on Tissue and Microstructure ModelsWe 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 CompatibilityDmipy 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 athttp://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.InstallationYou can install dmipy using pypi by typing in your terminalpython3 -m pip install dmipyor you can manuallyclone repositorypython setup.py installSee solutions tocommon issuesDependenciesRecommended to use Anaconda Python distribution.numpy >= 1.13scipydipycvxpyboto (optional for HCP-AWS interface)pathos(optional for multi-core processing)numba(optional for faster functions)Getting StartedTo get a feeling for how to use Dmipy, we provide a few tutorial notebooks:Setting up an acquisition schemeSimulating and fitting data using a simple Stick modelCombining biophysical models into a Microstructure modelCreating a dispersed and/or distributed axon bundle representationImposing parameter links and constraintsParameter Cascading: Using a simple model to initialize a complex oneGeneralized Multi-Tissue ModelingExplanations and Illustrations of Dmipy ContentsBiophysical Models and DistributionsCylinder 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])Tissue Response Function Models and Estimation (WM/GM/CSF, e.g. [Jeurissen et al. 2014])Spherical Distribution Models (Axon Dispersion, e.g. [Kaden et al. 2007])Spherical Mean of any Compartment ModelGlobal Multi-Compartment OptimizersBrute Force to Gradient Descent (Brute2Fine)Stochastic (MIX) [Farooq et al. 2016]Constrained Spherical Deconvolution OptimizersGeneralized Multi-Shell Multi-Compartment CSD [Tournier et al. 2007, Jeurissen et al. 2014]Dmipy implementations of Microstructure Models in LiteratureDmipy 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 theHCP tutorial.Single Bundle ModelsBall 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]Multi-Tissue NODDI [Frigo et al. 2020]SMT-NODDI [Cabeen et al. 2019]AxCaliber [Assaf et al. 2008]AxCaliber with Extra-Axonal Diffusion Time-Dependence [De Santis et al. 2016]Crossing Bundle ModelsBall and Sticks [Behrens et al. 2003]NODDI in Crossings (NODDIx) [Farooq et al. 2016]Tumor ModelsIVIM [Le Bihan et al. 1988, Gurney-Champion et al. 2018]VERDICT [Panagiotaki et al. 2014]Spherical Mean-Based ModelsAny Spherical Mean model can also estimate parametric FODs.Spherical Mean Technique [Kaden et al. 2015]Multi-Compartment Microscopic Diffusion Imaging [Kaden et al. 2016]Spherical Deconvolution-Based ModelsConstrained 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.Multi-Shell Multi-Compartment CSD [model-based version of Jeurissen et al. 2014]Multi-Shell Multi-Tissue CSD with Unsupervised 3-Tissue Response Function Estimation [Jeurissen 2014, Dhollander 2016a]Single-Shell (1 shell + b0) Multi-Tissue CSD [Dhollander et al. 2016b]How to contribute to DmipyDmipy's design is completely modular and can easily be extended with new models, distributions or optimizers. To contribute, view ourcontribution guidelines.How to cite DmipyPrimary Reference: Rutger Fick, Demian Wassermann and Rachid Deriche, "The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy",Frontiers in Neuroinformatics13 (2019): 64.Github Repository: Rutger Fick, Rachid Deriche, & Demian Wassermann. (2019, October 15).The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy (Version 1.0). Zenodo.http://doi.org/10.5281/zenodo.3490325Package Acknowledgements