Abstract
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.
Highlights
PCASL is a readily available non-invasive technique for perfusion imaging that can be used in a clinical setting (Alsop et al, 2015), for clinical research, as well as for neuroscience applications, such as identification of neuronal networks (Dai et al, 2016)
Whereas current clinical practice relies on general atlases of typical flow territories, information on the layout of the actual flow territories in an individual patient can be highly valuable in several clinical settings, such as for risk assessment and guidance of revascularization therapy in patients suffering from stroke, as well as for explaining differences in patient outcome (Hartkamp et al, 2016; Hendrikse et al, 2009; van Laar et al, 2008)
The pseudo-continuous ASL (pCASL) scans acquired without a vessel-encoding gradient were analyzed only using the independent component analysis (ICA) and t-Stochastic neighbor embedding (t-SNE) methods, since the general linear model (GLM)-approach relies on prior knowledge on the timing of the fluctuations in labeling efficiency, which is not available when there is no prior knowledge on the pattern of signal fluctuations
Summary
PCASL is a readily available non-invasive technique for perfusion imaging that can be used in a clinical setting (Alsop et al, 2015), for clinical research, as well as for neuroscience applications, such as identification of neuronal networks (Dai et al, 2016). Small changes in ASL signal over time allow for identification of resting state networks (Dai et al, 2016). The addition of the 10 mm blurred version was based on the hypothesis that the relatively low SNR of single average ASL images would hamper identification of flow territory specific fluctuations. Since such fluctuations would per definition be spatially coherent, some blurring could help in identifying flow territories, albeit at a loss of effective spatial resolution
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