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Automated Mapping of Residual Distortion Severity in Diffusion MRI

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Computational Diffusion MRI (CDMRI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14328))

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Abstract

Susceptibility-induced distortion is a common artifact in diffusion MRI (dMRI), which deforms the dMRI locally and poses significant challenges in connectivity analysis. While various methods were proposed to correct the distortion, residual distortions often persist at varying degrees across brain regions and subjects. Generating a voxel-level residual distortion severity map can thus be a valuable tool to better inform downstream connectivity analysis. To fill this current gap in dMRI analysis, we propose a supervised deep-learning network to predict a severity map of residual distortion. The training process is supervised using the structural similarity index measure (SSIM) of the fiber orientation distribution (FOD) in two opposite phase encoding (PE) directions. Only b0 images and related outputs from the distortion correction methods are needed as inputs in the testing process. The proposed method is applicable in large-scale datasets such as the UK Biobank, Adolescent Brain Cognitive Development (ABCD), and other emerging studies that only have complete dMRI data in one PE direction but acquires b0 images in both PEs. In our experiments, we trained the proposed model using the Lifespan Human Connectome Project Aging (HCP-Aging) dataset (\(n=662\)) and apply the trained model to data (\(n=1330\)) from UK Biobank. Our results show low training, validation, and test errors, and the severity map correlates excellently with an FOD integrity measure in both HCP-Aging and UK Biobank data. The proposed method is also highly efficient and can generate the severity map in around 1 s for each subject.

Y. Shi—This work is supported by the National Institute of Health (NIH) under grants R01EB022744, RF1AG077578, RF1AG056573, RF1AG064584, R21AG064776, U19AG078109, and P41EB015922.

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Acknowledgements

Authors thank Dr. Yuchuan Qiao from Fudan University for the kindly help and useful discussions. We also appreciate Mr. Sarthak Kumar Maharana from University of Southern California for the help in tractography. Shuo Huang wants to thank Miss Yi Liu from University of Southern California for the help in editing the grammar of this work.

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Authors and Affiliations

  1. Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, 90033, USA

    Shuo Huang, Lujia Zhong & Yonggang Shi

  2. Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA

    Shuo Huang & Yonggang Shi

  3. Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA

    Lujia Zhong & Yonggang Shi

Authors
  1. Shuo Huang
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  2. Lujia Zhong
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  3. Yonggang Shi
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Corresponding author

Correspondence to Yonggang Shi .

Editor information

Editors and Affiliations

  1. University of Illinois at Chicago, Chicago, IL, USA

    Muge Karaman

  2. Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia

    Remika Mito

  3. University College London, London, UK

    Elizabeth Powell

  4. Université de Sherbrooke, Sherbrooke, QC, Canada

    Francois Rheault

  5. Microsoft Research Cambridge, Milton, UK

    Stefan Winzeck

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Huang, S., Zhong, L., Shi, Y. (2023). Automated Mapping of Residual Distortion Severity in Diffusion MRI. In: Karaman, M., Mito, R., Powell, E., Rheault, F., Winzeck, S. (eds) Computational Diffusion MRI. CDMRI 2023. Lecture Notes in Computer Science, vol 14328. Springer, Cham. https://doi.org/10.1007/978-3-031-47292-3_6

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  • DOI: https://doi.org/10.1007/978-3-031-47292-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47291-6

  • Online ISBN: 978-3-031-47292-3

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