Measuring Microscopic Anisotropy with Diffusion Magnetic Resonance: From Material Science to Biomedical Imaging

  • Andrada IanuşEmail author
  • Noam Shemesh
  • Daniel C. Alexander
  • Ivana Drobnjak
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


Diffusion magnetic resonance provides a non-invasive probe of material structure at the micro-scale in porous media including emulsions, rocks, catalysts and biological tissue. The quantification of microscopic anisotropy aims to reflect the size and shape of individual pores, separating the effect of their orientation distribution in the imaging voxel, which is of great importance in many applications.

The single diffusion encoding (SDE) sequence, which consists of a pair of diffusion gradients applied before and after the refocusing pulse in a spin-echo preparation, is the standard pulse sequence for acquiring diffusion MRI data. SDE sequences, which have one gradient orientation per measurement, have been used in various studies to estimate microscopic anisotropy, mainly assuming that the underlying substrate consists of identical pores. In order to discriminate between more complex systems, which may include pores of various sizes and shapes, more sophisticated techniques which use diffusion gradients with varying orientation within one measurement, such as double diffusion encoding, isotropic encoding or q-space trajectory imaging, have been proposed in the literature. In addition to the these techniques which aim to estimate microscopic anisotropy, a different approach to characterize pore shape directly is to take the inverse Fourier transform of the reciprocal pore shape function which can be measured with diffusion gradients that are highly asymmetric.

This work provides a review of various diffusion magnetic resonance techniques which have been proposed in the literature to measure the microscopic shape of pores, both in material science as well as in biomedical imaging.



This study was supported by EPSRC grants M507970, G007748, H046410, K020439, and M020533 and the Leverhulme trust. Funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 657366 supports NS’s work on this topic.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrada Ianuş
    • 1
    • 2
    Email author
  • Noam Shemesh
    • 2
  • Daniel C. Alexander
    • 3
  • Ivana Drobnjak
    • 3
  1. 1.Department of Computer ScienceUniversity College LondonLondonUK
  2. 2.Champalimaud Neuroscience ProgrammeChampalimaud Centre for the UnknownLisbonPortugal
  3. 3.Department of Computer ScienceUniversity College LondonLondonUK

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