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Towards Optimal Sampling in Diffusion MRI

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Computational Diffusion MRI (MICCAI 2019)

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

Abstract

The methodology outlined in this chapter is intended to provide a tool for the generation of sets of MRI diffusion encoding waveforms that are optimal for tissue micro-structure estimation. The methodology presented has five distinct components: 1. Defining the class of waveforms allowed, i.e. defining the measurement space. 2. Specifying the expected distribution of microstructure features present in the targeted tissue. 3. Learning the metric in the chosen measurement space. 4. Designing a continuous parametric functional suitable for approximation of the estimated metric. 5. Finding a distribution of a chosen number of waveforms that is optimal given the continuous metric. The tissue is modeled as a collection of simple elliptical compartments with varying size and shape. Two waveform classes are tested: The classical Stejskal-Tanner waveform and an idealized Laun long-short waveform. The estimation of the metric is based on correlations between measurements obtained at given points in the measurement space using an information theoretical approach. Optimal sets of waveforms are found using a simulated annealing inspired energy minimizing approach. The superior performance of the methodology is demonstrated for a number of different cases by means of simulations.

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Acknowledgements

The author acknowledges the following grants: The Swedish Research Council 2015-05356, the Swedish Foundation for Strategic Research AM13-0090, the Linneaus center ‘CADICS’ and the Wallenberg foundation ’Seeing Organ Function’.

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Authors and Affiliations

  1. Linköping University, Linköping, Sweden

    Hans Knutsson

Authors
  1. Hans Knutsson
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Correspondence to Hans Knutsson .

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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Knutsson, H. (2019). Towards Optimal Sampling 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_1

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