  Purpose: Arterial Spin Labeling (ASL) is a quantitative, non-invasive
alternative to perfusion imaging with contrast agents. Fixing values of certain
model parameters in traditional ASL, which actually vary from region to region,
may introduce bias in perfusion estimates. Adopting Magnetic Resonance
Fingerprinting (MRF) for ASL is an alternative where these parameters are
estimated alongside perfusion, but multiparametric estimation can degrade
precision. We aim to improve the sensitivity of ASL-MRF signals to underlying
parameters to counter this problem, and provide precise estimates. We also
propose a regression based estimation framework for MRF-ASL.
  Methods: To improve the sensitivity of MRF-ASL signals to underlying
parameters, we optimize ASL labeling durations using the Cramer-Rao Lower Bound
(CRLB). This paper also proposes a neural network regression based estimation
framework trained using noisy synthetic signals generated from our ASL signal
model.
  Results: We test our methods in silico and in vivo, and compare with multiple
post labeling delay (multi-PLD) ASL and unoptimized MRF-ASL. We present
comparisons of estimated maps for six parameters accounted for in our signal
model.
  Conclusions: The scan design process facilitates precise estimates of
multiple hemodynamic parameters and tissue properties from a single scan, in
regions of gray and white matter, as well as regions with anomalous perfusion
activity in the brain. The regression based estimation approach provides
perfusion estimates rapidly, and bypasses problems with quantization error.
  Keywords: Arterial Spin Labeling, Magnetic Resonance Fingerprinting,
Optimization, Cramer-Rao Bound, Scan Design, Regression, Neural Networks, Deep
Learning, Precision, Estimation, Brain Hemodynamics.
