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Data-Driven Multi-contrast Spectral Microstructure Imaging with InSpect

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12266))

Abstract

We introduce and demonstrate an unsupervised machine learning method for spectroscopic analysis of quantitative MRI (qMRI) experiments. qMRI data can support estimation of multidimensional correlation (or single-dimensional) spectra, which allow model-free investigation of tissue properties, but this requires an ill-posed calculation. Moreover, in the vast majority of applications ground truth knowledge is unobtainable, preventing the application of supervised machine learning. Here we present a new method that addresses these limitations in a data-driven way. The algorithm simultaneously estimates a canonical basis of spectral components and voxelwise maps of their weightings, thereby pooling information across whole images to regularise the ill-posed problem. We show that our algorithm substantially outperforms current voxelwise spectral approaches. We demonstrate the method on combined diffusion-relaxometry placental MRI scans, revealing anatomically-relevant substructures, and identifying dysfunctional placentas. Our algorithm vastly reduces the data required to reliably estimate multidimensional correlation (or single-dimensional) spectra, opening up the possibility of spectroscopic imaging in a wide range of new applications.

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Author information

Authors and Affiliations

  1. Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK

    Paddy J. Slator, Razvan V. Marinescu, Marco Palombo & Daniel C. Alexander

  2. Centre for the Developing Brain, King’s College London, London, UK

    Jana Hutter, Laurence H. Jackson, Mary Rutherford & Joseph V. Hajnal

  3. Biomedical Engineering Department, King’s College London, London, UK

    Jana Hutter, Laurence H. Jackson & Joseph V. Hajnal

  4. Women’s Health Department, King’s College London, London, UK

    Alison Ho & Lucy C. Chappell

Authors
  1. Paddy J. Slator
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  2. Jana Hutter
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  3. Razvan V. Marinescu
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  4. Marco Palombo
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  5. Laurence H. Jackson
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  6. Alison Ho
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  7. Lucy C. Chappell
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  8. Mary Rutherford
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  9. Joseph V. Hajnal
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  10. Daniel C. Alexander
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Corresponding author

Correspondence to Paddy J. Slator .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Anne L. Martel

  2. The University of British Columbia, Vancouver, BC, Canada

    Purang Abolmaesumi

  3. University College London, London, UK

    Danail Stoyanov

  4. École Centrale de Nantes, Nantes, France

    Diana Mateus

  5. EURECOM, Biot, France

    Maria A. Zuluaga

  6. Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  7. Sorbonne University, Paris, France

    Daniel Racoceanu

  8. The Hebrew University of Jerusalem, Jerusalem, Israel

    Leo Joskowicz

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Slator, P.J. et al. (2020). Data-Driven Multi-contrast Spectral Microstructure Imaging with InSpect. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_36

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  • DOI: https://doi.org/10.1007/978-3-030-59725-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59724-5

  • Online ISBN: 978-3-030-59725-2

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