Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning

Liang Z, Lee CH, Arefin TM, Dong Z, Walczak P, Shi SH, Knoll F, Ge Y, Ying L, Zhang J (2022)

Publication Type: Journal article

Publication year: 2022

Journal

eLife eLife Sciences Publications

Book Volume: 11

Article Number: e72331

DOI: 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.

Authors with CRIS profile

Florian Knoll

Involved external institutions

University of Maryland US United States (USA) (US) Memorial Sloan Kettering Cancer Center US United States (USA) (US) University at Buffalo. State University of New York US United States (USA) (US) New York University (NYU) US United States (USA) (US)

How to cite

APA:

Liang, Z., Lee, C.H., Arefin, T.M., Dong, Z., Walczak, P., Shi, S.-H.,... Zhang, J. (2022). Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning. eLife, 11. https://doi.org/10.7554/eLife.72331

MLA:

Liang, Zifei, et al. "Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning." eLife 11 (2022).

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