  Purpose: Arterial spin labeling (ASL) perfusion imaging indicates direct and
absolute measurement of cerebral blood flow (CBF). Arterial transit time (ATT)
is a related physiological parameter reflecting the duration for the labeled
spins to reach the brain region of interest. Multiple post-labeling delay
(PLDs) can provide robust measures of both CBF and ATT, allowing for
optimization of regional CBF modeling based on ATT. The prolonged acquisition
time can potentially reduce the quality and accuracy of the CBF and ATT
estimation. We proposed a novel network to significantly reduce the number of
PLDs with higher signal-to-noise ratio (SNR). Method: CBF and ATT estimations
were performed for one PLD and two PLDs sepa-rately. Each model was trained
independently to learn the nonlinear transformation from perfusion weighted
image (PWI) to CBF and ATT images. Results: Both one-PLD and two-PLD models
outperformed the conventional method visually on CBF and two-PLD model showed
more accurate structure on ATT estima-tion. The proposed method significantly
reduces the number of PLDs from 6 to 2 on ATT and even to single PLD on CBF
without sacrificing the SNR. Conclusion: It is feasible to generate CBF and ATT
maps with reduced PLDs using deep learning with high quality.
