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Diffusion MRI Fibre Orientation Distribution Inpainting

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Computational Diffusion MRI (CDMRI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13722))

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Abstract

The analysis of diffusion weighted brain magnetic resonance images, including the estimation of fibre orientation distribution (FOD), tractography, and connectomics, is a powerful tool for neuroscience research and clinical applications. However, focal brain pathology and imaging acquisition artifacts affecting white matter tracts may disrupt or corrupt FOD values respectively, invalidating tractography and connectome reconstructions. In this work, we propose a 3D FOD inpainting framework, named order-wise coefficient estimation network (OCE-Net), to dynamically reconstruct the affected regions. Our feature encoding stage, based on gated convolutions, extracts features from all the input FOD coefficients and re-weights them using channel attention and independent order-wise decoders, to independently predict the coefficients for each spherical harmonic order. We evaluated our model on a subset of scans from the HCP dataset, and conducted tractography and connectomics to further analyse the impact of inpainting. Our experimental results, including a statistical analysis of the reconstructed connectomes, show that our OCE-Net approach can successfully reconstruct the original FODs in the focally disrupted regions.

Z. Tang and X. Wang—Equal contributions.

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Notes

  1. 1.

    The project page of this work is available at: https://mri-synthesis.github.io.

  2. 2.

    https://www.humanconnectome.org/.

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Acknowledgments

The authors acknowledge the support of an Australian Government Research Training Program (RTP) Scholarship. The authors acknowledge the funding support by the Australia Medical Research Future Fund under Grant (MRFFAI000085).

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Authors and Affiliations

  1. School of Computer Science, University of Sydney, Sydney, NSW, 2008, Australia

    Zihao Tang, Xinyi Wang, Dongnan Liu & Weidong Cai

  2. Brain and Mind Centre, University of Sydney, Sydney, NSW, 2050, Australia

    Zihao Tang, Xinyi Wang, Mariano Cabezas, Arkiev D’Souza, Fernando Calamante, Dongnan Liu, Michael Barnett & Chenyu Wang

  3. Sydney Neuroimaging Analysis Centre, Sydney, NSW, 2050, Australia

    Michael Barnett & Chenyu Wang

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  1. Zihao Tang
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  2. Xinyi Wang
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  4. Arkiev D’Souza
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  9. Weidong Cai
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Corresponding author

Correspondence to Zihao Tang .

Editor information

Editors and Affiliations

  1. Harvard Medical School, Boston, MA, USA

    Suheyla Cetin-Karayumak

  2. KU Leuven, Leuven, Belgium

    Daan Christiaens

  3. University College London, London, UK

    Matteo Figini

  4. Universidad de ConcepciĂłn, ConcepciĂłn, Chile

    Pamela Guevara

  5. Universidad de Valladolid, Valladolid, Spain

    Tomasz Pieciak

  6. University College London, London, UK

    Elizabeth Powell

  7. Université de Sherbrooke, Sherbrooke, QC, Canada

    Francois Rheault

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Data used in this work were obtained from the MGH-USC Human Connectome Project (HCP) database.

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Tang, Z. et al. (2022). Diffusion MRI Fibre Orientation Distribution Inpainting. In: Cetin-Karayumak, S., et al. Computational Diffusion MRI. CDMRI 2022. Lecture Notes in Computer Science, vol 13722. Springer, Cham. https://doi.org/10.1007/978-3-031-21206-2_6

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  • DOI: https://doi.org/10.1007/978-3-031-21206-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21205-5

  • Online ISBN: 978-3-031-21206-2

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