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Dmipy, a Diffusion Microstructure Imaging toolbox in Python to improve research reproducibility

Abstract : Non-invasive estimation of brain white matter microstructure features using diffusion MRI - otherwise known as Microstructure Imaging - has become an increasingly diverse and complicated field over the last decade. Multi-compartment-based models have been a popular approach to estimate these features. In this work, we present Diffusion Microstructure Imaging in Python (Dmipy), a diffusion MRI toolbox which allows accessing any multi-compartment-based model and robustly estimates these important features from single-shell, multi-shell, and multi-diffusion time, and multi-TE data. Dmipy follows a building block-based philosophy to microstructure imaging, meaning a multi-compartment model can be constructed and fitted to dMRI data using any combination of underlying tissue models, axon dispersion-or diameter distributions, and optimization algorithms using less than 10 lines of code, thus helps improve research reproducibility. In describing the toolbox, we show how Dmipy enables to easily design microstructure models and offers to the users the freedom to choose among different optimization strategies.We finally present three advanced examples of highly complex modeling approaches which are made easy using Dmipy.
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Submitted on : Thursday, September 13, 2018 - 10:58:54 AM
Last modification on : Monday, December 13, 2021 - 9:16:08 AM
Long-term archiving on: : Friday, December 14, 2018 - 2:04:50 PM


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  • HAL Id : hal-01873353, version 1


Abib Alimi, Rutger Fick, Demian Wassermann, Rachid Deriche. Dmipy, a Diffusion Microstructure Imaging toolbox in Python to improve research reproducibility. MICCAI 2018 - Workshop on Computational Diffusion MRI, Sep 2018, Granada, Spain. ⟨hal-01873353⟩



Les métriques sont temporairement indisponibles