DeepGraspT1: Deep Learning-Enabled GRASP T1 Mapping

Golden-angle RAdial Sparse Parallel (GRASP) MRI has recently been extended for rapid, accurate and robust T1 mapping (GraspT1) that can be performed during free breathing. However, GraspT1 implements a conventional T1 mapping framework that reconstructs an image series from undersampled dynamic k-space in the first step and then performs pixel-wise parameter fitting in the second step. This leads to a slow and cumbersome pipeline to obtain T1 maps. In this work, we developed deep learning-based GraspT1 (DeepGraspT1), which directly estimates T1 maps from undersampled k-space and enables additional acceleration that outperforms conventional iterative reconstruction.