Deep learning-based MR fingerprinting ASL ReconStruction (DeepMARS).

PURPOSE

To develop a reproducible and fast method to reconstruct MR fingerprinting arterial spin labeling (MRF-ASL) perfusion maps using deep learning.

METHOD

A fully connected neural network, denoted as DeepMARS, was trained using simulation data and added Gaussian noise. Two MRF-ASL models were used to generate the simulation data, specifically a single-compartment model with 4 unknowns parameters and a two-compartment model with 7 unknown parameters. The DeepMARS method was evaluated using MRF-ASL data from healthy subjects (N = 7) and patients with Moymoya disease (N = 3). Computation time, coefficient of determination (R ), and intraclass correlation coefficient (ICC) were compared between DeepMARS and conventional dictionary matching (DM). The relationship between DeepMARS and Look-Locker PASL was evaluated by a linear mixed model.

RESULTS

Computation time per voxel was <0.5 ms for DeepMARS and >4 seconds for DM in the single-compartment model. Compared with DM, the DeepMARS showed higher R and significantly improved ICC for single-compartment derived bolus arrival time (BAT) and two-compartment derived cerebral blood flow (CBF) and higher or similar R /ICC for other parameters. In addition, the DeepMARS was significantly correlated with Look-Locker PASL for BAT (single-compartment) and CBF (two-compartment). Moreover, for Moyamoya patients, the location of diminished CBF and prolonged BAT shown in DeepMARS was consistent with the position of occluded arteries shown in time-of-flight MR angiography.

CONCLUSION

Reconstruction of MRF-ASL with DeepMARS is faster and more reproducible than DM.