On the ability to exploit signal fluctuations in pseudocontinuous arterial spin labeling for inferring the major flow territories from a traditional perfusion scan.

In arterial spin labeling (ASL) a magnetic label is applied to the flowing blood in feeding arteries allowing depiction of cerebral perfusion maps. The labeling efficiency depends, however, on blood velocity and local field inhomogeneities and is, therefore, not constant over time. In this work, we investigate the ability of statistical methods used in functional connectivity research to infer flow territory information from traditional pseudo-continuous ASL (pCASL) scans by exploiting artery-specific signal fluctuations. By applying an additional gradient during labeling the minimum amount of signal fluctuation that allows discrimination of the main flow territories is determined. The following three approaches were tested for their performance on inferring the large vessel flow territories of the brain: a general linear model (GLM), an independent component analysis (ICA) and t-stochastic neighbor embedding. Furthermore, to investigate the effect of large vessel pathology, standard ASL scans of three patients with a unilateral stenosis (>70%) of one of the internal carotid arteries were retrospectively analyzed using ICA and t-SNE. Our results suggest that the amount of natural-occurring variation in labeling efficiency is insufficient to determine large vessel flow territories. When applying additional vessel-encoded gradients these methods are able to distinguish flow territories from one another, but this would result in approximately 8.5% lower perfusion signal and thus also a reduction in SNR of the same magnitude.