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

ABSTRACT: ¹H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from magnetic resonance imaging (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 mimic target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relati

SUBMITTER: Liang Z 

PROVIDER: S-EPMC8837198 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature