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 2 ), 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 2 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 2 /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.