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

1H MRI maps brain anatomy and pathology non-invasively through contrasts generated by exploiting inhomogeneities in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to the absence of direct links between MRI signals and specific tissue compartments. Here, we show that convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can generate virtual histology from MRI results. Our networks provide maps that mirror histological stains for axons and myelin with enhanced specificity compared to existing MRI markers. Furthermore, by introducing random perturbations to the inputs, the relative contribution of each MRI contrast within the networks can be estimated and guide the optimization of 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 developing novel MRI contrasts.