Virtual mouse brain histology from multi-contrast MRI via deep learning

  1. Zifei Liang
  2. Choong H Lee
  3. Tanzil M Arefin
  4. Zijun Dong
  5. Piotr Walczak
  6. Song Hai Shi
  7. Florian Knoll
  8. Yulin Ge
  9. Leslie Ying
  10. Jiangyang Zhang  Is a corresponding author
  1. New York University School of Medicine, United States
  2. University of Maryland, United States
  3. Memorial Sloan Kettering Cancer Center
  4. University at Buffalo, State University of New York, United States

Abstract

1H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we show that deep convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relative contribution of each MRI contrast within the networks can be used to optimize multi-contrast MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for validating novel MRI techniques.

Data availability

All data and source codes used in this study are available at https://www.github.com/liangzifei/MRH-net/. The data can also be found at datadryad.org

The following data sets were generated
The following previously published data sets were used
    1. Lein ES et al
    (2006) Allen Mouse Brain Atlas
    The reference data at http://connectivity.brain-map.org/static/referencedata.

Article and author information

Author details

  1. Zifei Liang

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Choong H Lee

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Tanzil M Arefin

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Zijun Dong

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Piotr Walczak

    Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Song Hai Shi

    Developmental Biology Program, Memorial Sloan Kettering Cancer Center
    Competing interests
    The authors declare that no competing interests exist.
  7. Florian Knoll

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Yulin Ge

    Department of Radiology, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Leslie Ying

    Departments of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Jiangyang Zhang

    Department of Radiology, New York University School of Medicine, New York, United States
    For correspondence
    jiangyang.zhang@nyulangone.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0740-2662

Funding

Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD074593)

  • Jiangyang Zhang

National Institute of Neurological Disorders and Stroke (R01NS102904)

  • Jiangyang Zhang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Saad Jbabdi, University of Oxford, United Kingdom

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (s16-00145-133) of the New York University.

Version history

  1. Preprint posted: May 3, 2020 (view preprint)
  2. Received: July 20, 2021
  3. Accepted: January 27, 2022
  4. Accepted Manuscript published: January 28, 2022 (version 1)
  5. Version of Record published: February 11, 2022 (version 2)

Copyright

© 2022, Liang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Zifei Liang
  2. Choong H Lee
  3. Tanzil M Arefin
  4. Zijun Dong
  5. Piotr Walczak
  6. Song Hai Shi
  7. Florian Knoll
  8. Yulin Ge
  9. Leslie Ying
  10. Jiangyang Zhang
(2022)
Virtual mouse brain histology from multi-contrast MRI via deep learning
eLife 11:e72331.
https://doi.org/10.7554/eLife.72331

Share this article

https://doi.org/10.7554/eLife.72331

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