Optimizing sampling strategies for perfusion quantification using PASL

Arterial Spin Labeling (ASL) techniques potentially allow the absolute, non-invasive quantification of brain perfusion. This can be achieved either by fitting a kinetic model to the data acquired at a number of inversion times (TI) or by applying the model to a single TI assuming values for all other parameters. The accuracy of the model estimation strongly depends on the distribution of the TI sampling points. Here, we propose a Bayesian framework for ASL estimation based on the maximum a posteriori criterion, whereby the optimal sampling points can be determined taking into account the parameter uncertainty.