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Dmipy, A Diffusion Microstructure Imaging Toolbox in Python to Improve Research Reproducibility

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Computational Diffusion MRI (MICCAI 2019)

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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.

Abib Alimi and Rutger Fick share first authorship for this work.

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Notes

  1. 1.

    https://my.vanderbilt.edu/ismrmtraced2017/.

  2. 2.

    Although finding a unique minimum will be difficult given its many parameters.

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Acknowledgements

This work was partly supported by ANR “MOSIFAH” under ANR-13-MONU-0009-01, the ERC under the European Union’s Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665:CoBCoM).

Author information

Authors and Affiliations

  1. Athena, Inria, Université Côte d’Azur, Sophia Antipolis, France

    Abib Alimi & Rachid Deriche

  2. TheraPanacea, Paris, France

    Rutger Fick

  3. Parietal, Inria, CEA, Université Paris-Saclay, Paris, France

    Demian Wassermann

Authors
  1. Abib Alimi
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  2. Rutger Fick
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  3. Demian Wassermann
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  4. Rachid Deriche
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Corresponding author

Correspondence to Abib Alimi .

Editor information

Editors and Affiliations

  1. Centre for Medical Image Computing, University College London, London, UK

    Elisenda Bonet-Carne

  2. Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK

    Francesco Grussu

  3. Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Lipeng Ning

  4. Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Farshid Sepehrband

  5. Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK

    Chantal M. W. Tax

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Alimi, A., Fick, R., Wassermann, D., Deriche, R. (2019). Dmipy, A Diffusion Microstructure Imaging Toolbox in Python to Improve Research Reproducibility. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C. (eds) Computational Diffusion MRI. MICCAI 2019. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-05831-9_5

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