Virtual mouse brain histology from multi-contrast MRI via deep learning.
Author
Liang, ZifeiLee, Choong H
Arefin, Tanzil M
Dong, Zijun
Walczak, Piotr
Hai Shi, Song
Knoll, Florian
Ge, Yulin
Ying, Leslie
Zhang, Jiangyang
Date
2022-01-28Journal
eLifePublisher
eLife Sciences PublicationsType
Article
Metadata
Show full item recordAbstract
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.Identifier to cite or link to this item
http://hdl.handle.net/10713/17883ae974a485f413a2113503eed53cd6c53
10.7554/eLife.72331
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