MR Fingerprinting (MRF)-based Arterial-Spin-Labeling (ASL) has been recently proposed as a new approach to measure multiple hemodynamic parameters in a single scan. However, the previous implementation of MRF-ASL lacks the comparison with clinical standard techniques such as dynamic-susceptibility-contrast (DSC). Therefore, in this work, we validated MRF-ASL by comparing with DSC MRI. The results showed that these two methods provided visually comparable and quantitatively correlated perfusion estimations. Furthermore, we sought to directly estimate DSC-equivalent parameters from the MRF-ASL raw data using a deep-learning (DL) approach. DL-derived maps show better quality and are more consistent with DSC maps, compared to dictionary-matching results.