Deep Mapping: Using deep convolutional neural networks to estimate quantitative T1 maps trained on a 7 T minimum deformation average model

Deep convolutional neural networks are increasingly being used to solve challenging medical image processing tasks. The acquisition of high resolution quantitative parameter maps in MRI, such as T1 and quantitative susceptibility maps often require long or additional acquisitions and post-processing steps. We therefore trained a convolutional neural network on a minimum deformation model of MP2RAGE data acquired at 7 T and show the feasibility of computing T1 maps from single subject data.