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Diffusion MRI Anisotropy: Modeling, Analysis and Interpretation

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Modeling, Analysis, and Visualization of Anisotropy

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

Abstract

The micro-architecture of brain tissue obstructs the movement of diffusing water molecules, causing tissue-dependent, often anisotropic diffusion profiles. In diffusion MRI (dMRI), the relation between brain tissue structure and diffusion anisotropy is studied using oriented diffusion gradients, resulting in tissue- and orientation-dependent diffusion-weighted images (DWIs). Over time, various methods have been proposed that summarize these DWIs, that can be measured at different orientations, gradient strengths and diffusion times into one “diffusion anisotropy” measure. This book chapter is dedicated to understanding the similarities and differences between the diffusion anisotropy metrics that different methods estimate. We first discuss the physical interpretation of diffusion anisotropy in terms of the diffusion properties around nervous tissue. We then explain how DWIs are influenced by diffusion anisotropy and the parameters of the dMRI acquisition itself. We then go through the state-of-the-art of signal-based and multi-compartment-based dMRI methods that estimate diffusion anisotropy-related methods, focusing on their limitations and applications. We finally discuss confounding factors in the estimation of diffusion anisotropy and current challenges.

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Notes

  1. 1.

    http://www.nitrc.org/projects/noddi_toolbox

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Acknowledgements

Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. This work was partly supported by ANR “MOSIFAH” under ANR-13-MONU-0009-01 and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665: CoBCoM). The author Marco Pizzolato expresses his thanks to Olea Medical and the Provence-Alpes-Côte d’Azur (PACA) Regional Council for providing grant and support. We thank Mauro Zucchelli for useful discussions.

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Authors and Affiliations

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

    Rutger H. J. Fick, Marco Pizzolato, Demian Wassermann & Rachid Deriche

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  1. Rutger H. J. Fick
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  2. Marco Pizzolato
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Correspondence to Rutger H. J. Fick .

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Editors and Affiliations

  1. Institute of Computer Science, University of Bonn, Bonn, Germany

    Thomas Schultz

  2. Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden

    Evren Özarslan

  3. Department of Science & Technology, Linköping University, Norrköping, Sweden

    Ingrid Hotz

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Fick, R.H.J., Pizzolato, M., Wassermann, D., Deriche, R. (2017). Diffusion MRI Anisotropy: Modeling, Analysis and Interpretation. In: Schultz, T., Özarslan, E., Hotz, I. (eds) Modeling, Analysis, and Visualization of Anisotropy. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-61358-1_9

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