  Multi-Delay single-shot arterial spin labeling (ASL) imaging provides
accurate cerebral blood flow (CBF) and, in addition, arterial transit time
(ATT) maps but the inherent low SNR can be challenging. Especially standard
fitting using non-linear least squares often fails in regions with poor SNR,
resulting in noisy estimates of the quantitative maps. State-of-the-art fitting
techniques improve the SNR by incorporating prior knowledge in the estimation
process which typically leads to spatial blurring. To this end, we propose a
new estimation method with a joint spatial total generalized variation
regularization on CBF and ATT. This joint regularization approach utilizes
shared spatial features across maps to enhance sharpness and simultaneously
improves noise suppression in the final estimates. The proposed method is
evaluated at three levels, first on synthetic phantom data including
pathologies, followed by in vivo acquisitions of healthy volunteers, and
finally on patient data following an ischemic stroke. The quantitative
estimates are compared to two reference methods, non-linear least squares
fitting and a state-of-the-art ASL quantification algorithm based on Bayesian
inference. The proposed joint regularization approach outperforms the reference
implementations, substantially increasing the SNR in CBF and ATT while
maintaining sharpness and quantitative accuracy in the estimates.
