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Current Applications and Future Promises of Machine Learning in Diffusion MRI

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Computational Diffusion MRI (MICCAI 2019)

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

Abstract

Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) explores the random motion of diffusing water molecules in biological tissue and can provide information on the tissue structure at a microscopic scale. DW-MRI in used in many applications both in the brain and other parts of the body such as the breast and prostate, and novelcomputational methods are at the core of advancements in DW-MRI, both in terms of research and its clinical translation. This article reviews the ways in whichmachine learning anddeep learning is currently applied in DW-MRI. We will also discuss the more traditional methods used for processing diffusion MRI and the potential of deep learning in augmenting these existing methods in the future.

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Acknowledgements

DR and DCA are supported by a project that has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement number 666992. EPSRC grants EP/M020533/1 and EP/N018702/1 support AI and DCAs work on this topic. UCL EPSRC Centre for Doctor Training in Medical Imaging (EP/L016478/1) funds NG.

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Authors and Affiliations

  1. University College London, London, UK

    Daniele Ravi, Nooshin Ghavami, Daniel C. Alexander & Andrada Ianus

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  1. Daniele Ravi
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  2. Nooshin Ghavami
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  3. Daniel C. Alexander
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  4. Andrada Ianus
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Correspondence to Daniele Ravi .

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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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Ravi, D., Ghavami, N., Alexander, D.C., Ianus, A. (2019). Current Applications and Future Promises of Machine Learning in Diffusion MRI. 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_9

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