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The MR Physics of Advanced Diffusion Imaging

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Computational Diffusion MRI (MICCAI 2016)

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

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Abstract

Over the last decade, the number of models used to analyse and interpret diffusion MRI data has increased dramatically. Exponentials and biexponentials have been joined by stretched exponentials, HARDI methods, compartment-based microstructure models and effective medium theories. At the same time, the field has experienced a cultural shift away from MR physics and towards computer science, emphasising Bayesian statistics and Machine Learning. This has meant that understanding imaging methodology whilst still keeping in mind the underlying physical assumptions can be challenging. This chapter reviews the Diffusion MR modelling literature from the point of view of the underlying physics. We show how the Bloch-Torrey equation can be derived, and then how different physical assumptions and formulations lead to different models. The intention is to show the different assumptions made in different models, to aid understanding and model selection.

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Author information

Authors and Affiliations

  1. Developmental Imaging and Biophysics, UCL GOS Institute of Child Health, University College London, London, UK

    Matt G. Hall Ph.D

Authors
  1. Matt G. Hall Ph.D
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Correspondence to Matt G. Hall Ph.D .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Andrea Fuster

  2. Centre for Medical Image Computing, University College London, London, United Kingdom

    Aurobrata Ghosh

  3. Centre for Medical Image Computing, University College London, London, United Kingdom

    Enrico Kaden

  4. Department of Psychiatry, Brigham & Womens Hospital, Boston, Massachusetts, USA

    Yogesh Rathi

  5. Medical Physics, University Medical Center, Freiburg, Germany

    Marco Reisert

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Hall, M.G. (2017). The MR Physics of Advanced Diffusion Imaging. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds) Computational Diffusion MRI. MICCAI 2016. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-54130-3_1

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