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

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    Author
    Liang, Zifei
    Lee, Choong H
    Arefin, Tanzil M
    Dong, Zijun
    Walczak, Piotr
    Hai Shi, Song
    Knoll, Florian
    Ge, Yulin
    Ying, Leslie
    Zhang, Jiangyang
    Date
    2022-01-28
    Journal
    eLife
    Publisher
    eLife Sciences Publications
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.7554/eLife.72331
    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.
    Rights/Terms
    © 2022, Liang et al.
    Keyword
    medicine
    mouse
    neuroscience
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/17883
    ae974a485f413a2113503eed53cd6c53
    10.7554/eLife.72331
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