Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology

H 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 provided deep convolutional neural
networks, called MRH-Nets, 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. The dataset
contains multi-contrast MRI and histology used for the
training and testing and the acquisition parameters. The datasets have
been carefully registered to mouse brain images from the Allen Mouse Brain
Atlas (https://mouse.brain-map.org). The source codes for
MRH-Nets can be found
at https://github.com/liangzifei/MRH-Net.