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

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 (R²), 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.