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Purpose: Arterial spin labeling (ASL) perfusion imaging indicates direct andabsolute measurement of cerebral blood flow (CBF). Arterial transit time (ATT)is a related physiological parameter reflecting the duration for the labeledspins to reach the brain region of interest. Multiple post-labeling delay(PLDs) can provide robust measures of both CBF and ATT, allowing foroptimization of regional CBF modeling based on ATT. The prolonged acquisitiontime can potentially reduce the quality and accuracy of the CBF and ATTestimation. We proposed a novel network to significantly reduce the number ofPLDs with higher signal-to-noise ratio (SNR). Method: CBF and ATT estimationswere performed for one PLD and two PLDs sepa-rately. Each model was trainedindependently to learn the nonlinear transformation from perfusion weightedimage (PWI) to CBF and ATT images. Results: Both one-PLD and two-PLD modelsoutperformed the conventional method visually on CBF and two-PLD model showedmore accurate structure on ATT estima-tion. The proposed method significantlyreduces the number of PLDs from 6 to 2 on ATT and even to single PLD on CBFwithout sacrificing the SNR. Conclusion: It is feasible to generate CBF and ATTmaps with reduced PLDs using deep learning with high quality.

Acceleration of cerebral blood flow and arterial transit time maps  estimation from multiple post-labeling delay arterial spin-labeled MRI via  deep learning