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Dictionary Based Super-Resolution for Diffusion MRI

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Computational Diffusion MRI

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

Abstract

Diffusion magnetic resonance imaging (dMRI) provides unique capabilities for non-invasive mapping of fiber tracts in the brain. It however suffers from relatively low spatial resolution, often leading to partial volume effects. In this paper, we propose to use a super-resolution approach based on dictionary learning for alleviating this problem. Unlike the majority of existing super-resolution algorithms, our proposed solution does not entail acquiring multiple scans from the same subject which renders it practical in clinical settings and applicable to legacy data. Moreover, this approach can be used in conjunction with any diffusion model. Motivated by how functional connectivity (FC) reflects the underlying structural connectivity (SC), we quantitatively validate our results by investigating the consistency between SC and FC before and after super-resolving the data. Based on this scheme, we show that our method outperforms traditional interpolation strategies and the only existing single image super-resolution method for dMRI that is not dependent on a specific diffusion model. Qualitatively, we illustrate that fiber tracts and track-density maps reconstructed from super-resolved dMRI data reveal exquisite details beyond what is achievable with the original data.

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Notes

  1. 1.

    This dataset is available online at: http://www.nitrc.org/projects/multimodal.

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Acknowledgements

The authors wish to thank Dr. Pierrick Coupé for assisting in the comparative assessment of our method with CLASR.

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Authors and Affiliations

  1. Biomedical Signal and Image Computing Laboratory, The University of British Columbia, Vancouver, BC, Canada

    Burak Yoldemir, Mohammad Bajammal & Rafeef Abugharbieh

Authors
  1. Burak Yoldemir
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  2. Mohammad Bajammal
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  3. Rafeef Abugharbieh
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Corresponding author

Correspondence to Burak Yoldemir .

Editor information

Editors and Affiliations

  1. Harvard Medical School Brigham and Women's Hospital, Boston, Massachusetts, USA

    Lauren O'Donnell

  2. Dept. of Computer Science, University College London Dept of Computer Science, London, United Kingdom

    Gemma Nedjati-Gilani

  3. Harvard Medical School Brigham and Women's Hospital, Boston, Massachusetts, USA

    Yogesh Rathi

  4. University Medical Center Freiburg Department of Radiology, Freiburg, Germany

    Marco Reisert

  5. Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Queen Square House, Queen Square, London, United Kingdom

    Torben Schneider

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Yoldemir, B., Bajammal, M., Abugharbieh, R. (2014). Dictionary Based Super-Resolution for Diffusion MRI. In: O'Donnell, L., Nedjati-Gilani, G., Rathi, Y., Reisert, M., Schneider, T. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-11182-7_18

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