Rapid 3D T1 mapping using deep learning-assisted Look-Locker inversion recovery MRI
Haoyang Pei, Ding Xia, Xiang Xu, Yang Yang, Yao Wang, Fang Liu, Li Feng
Abstract
Purpose
Conventional 3D Look-Locker inversion recovery (LLIR) T1 mapping requires multi-repetition data acquisition to reconstruct images at different inversion times for T1 fitting. To ensure B1 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 T1 mapping without TD.
Theory and Methods
The proposed approach is based on the fact that T1*, the effective T1 in LLIR imaging, is independent of TD and can be estimated from both LLIR imaging with and without TD, while accurate conversion of T1* to T1 requires TD. Therefore, deep learning can be used to learn the conversion of T1* to T1, which eliminates the need for TD. This idea was implemented for inversion-recovery-prepared Golden-angel RAdial Sparse Parallel T1 mapping (GraspT1). 39 GraspT1 datasets with a TD of 6 s (GraspT1-TD6) were used for training, which also incorporates additional anatomical images. The trained network was applied for T1 estimation in 14 GraspT1 datasets without TD (GraspT1-TD0). The robustness of the trained network was also tested.
Results
Deep learning-based T1 estimation from GraspT1-TD0 is accurate compared to the reference. Incorporation of additional anatomical images improves the accuracy of T1 estimation. The technique is also robust against slight variation in spatial resolution, imaging orientation and scanner platform.
Conclusion
Our approach eliminates the need for TD in 3D LLIR imaging without affecting the T1 estimation accuracy. It represents a novel use of deep learning towards more efficient and robust 3D LLIR T1 mapping.