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

<p><span>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. </span><span> </span>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 <a href="https://github.com/liangzifei/MRH-Net">https://github.com/liangzifei/MRH-Net</a>.</p>