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Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data

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Information Processing in Medical Imaging (IPMI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11492))

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Abstract

Diffusion MRI (dMRI), while powerful for characterization of tissue microstructure, suffers from long acquisition time. In this paper, we present a method for effective diffusion MRI reconstruction from slice-undersampled data. Instead of full diffusion-weighted (DW) image volumes, only a subsample of equally-spaced slices need to be acquired. We show that complementary information from DW volumes corresponding to different diffusion wavevectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. The experimental results indicate a high acceleration factor of up to 5 can be achieved with minimal information loss.

This work was supported in part by NIH grants (NS093842, EB022880, and EB006733).

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Authors and Affiliations

  1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Yoonmi Hong, Geng Chen, Pew-Thian Yap & Dinggang Shen

Authors
  1. Yoonmi Hong
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  2. Geng Chen
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  3. Pew-Thian Yap
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  4. Dinggang Shen
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Corresponding authors

Correspondence to Pew-Thian Yap or Dinggang Shen .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Albert C. S. Chung

  2. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    James C. Gee

  3. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    Paul A. Yushkevich

  4. Department of Natural Language Processing, Baidu Inc., Shenzhen, China

    Siqi Bao

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Hong, Y., Chen, G., Yap, PT., Shen, D. (2019). Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham. https://doi.org/10.1007/978-3-030-20351-1_41

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  • DOI: https://doi.org/10.1007/978-3-030-20351-1_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20350-4

  • Online ISBN: 978-3-030-20351-1

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