Rapid 3D T 1 mapping using deep learning-assisted Look-Locker inversion recovery MRI

Conventional 3D Look-Locker inversion recovery (LLIR) T mapping requires multi-repetition data acquisition to reconstruct images at different inversion times for T fitting. To ensure B robustness, sufficient time of delay (TD) is needed between repetitions, which prolongs scan time. This work proposes a novel deep learning-assisted LLIR MRI approach for rapid 3D T mapping without TD. The proposed approach is based on the fact that , the effective T in LLIR imaging, is independent of TD and can be estimated from both LLIR imaging with and without TD, while accurate conversion of to T requires TD. Therefore, deep learning can be used to learn the conversion of to T , which eliminates the need for TD. This idea was implemented for inversion-recovery-prepared Golden-angel RAdial Sparse Parallel T mapping (GraspT ). 39 GraspT datasets with a TD of 6 s (GraspT -TD6) were used for training, which also incorporates additional anatomical images. The trained network was applied for T estimation in 14 GraspT datasets without TD (GraspT -TD0). The robustness of the trained network was also tested. Deep learning-based T estimation from GraspT -TD0 is accurate compared to the reference. Incorporation of additional anatomical images improves the accuracy of T estimation. The technique is also robust against slight variation in spatial resolution, imaging orientation and scanner platform. Our approach eliminates the need for TD in 3D LLIR imaging without affecting the T estimation accuracy. It represents a novel use of deep learning towards more efficient and robust 3D LLIR T mapping.