Automated generation of cerebral blood flow and arterial transit time maps from multiple delay arterial spin‐labeled <scp>MRI</scp>

<jats:sec><jats:title>Purpose</jats:title><jats:p>Develop and evaluate a deep learning approach to estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multiple post‐labeling delay (PLD) ASL MRI.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>ASL MRI were acquired with 6 PLDs on a 1.5T or 3T GE system in adults with and without cognitive impairment (<jats:italic>N</jats:italic> = 99). Voxel‐level CBF and ATT maps were quantified by training models with distinct convolutional neural network architectures: (1) convolutional neural network (CNN) and (2) U‐Net. Models were trained and compared via 5‐fold cross validation. Performance was evaluated using mean absolute error (MAE). Model outputs were trained on and compared against a reference ASL model fitting after data cleaning. Minimally processed ASL data served as another benchmark. Model output uncertainty was estimated using Monte Carlo dropout. The better‐performing neural network was subsequently re‐trained on inputs with missing PLDs to investigate generalizability to different PLD schedules.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Relative to the CNN, the U‐Net yielded lower MAE on training data. On test data, the U‐Net MAE was 8.4 ± 1.4 mL/100 g/min for CBF and 0.22 ± 0.09 s for ATT. A significant association was observed between MAE and Monte Carlo dropout‐based uncertainty estimates. Neural network performance remained stable despite systematically reducing the number of input images (i.e., up to 3 missing PLD images). Mean processing time was 10.77 s for the U‐Net neural network compared to 10 min 41 s for the reference pipeline.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>It is feasible to generate CBF and ATT maps from 1.5T and 3T multi‐PLD ASL MRI with a fast deep learning image‐generation approach.</jats:p></jats:sec>