Since the labeling efficiency in pCASL is dependent on velocity and off-resonance effects, one could imagine that small natural occurring fluctuations at the labeling plane would lead to similar fluctuations in the ASL-signal. Such fluctuations at the brain level would be similar for voxels belonging to the same flow territory and might enable detection of flow territories, e.g. by an ICA-type of resting-state ASL analysis that is normally used for identification of neuronal networks. By deliberately manipulating the labeling efficiency, we can report that with small fluctuations in labeling efficiency flow territories can be determined, which diminish in robustness for smaller fluctuations.
Healthy subjects (n=5, mean age 30.4±7.6, male/female=4/1) were scanned on a 3-Tesla MRI scanner (Philips, the Netherlands) using a protocol containing a 3DT1, standard pCASL, a time-of-flight angiogram of the labeling plane, a planning-free flow-territory mapping scan which provides the gold standard territories, and a range of pCASL-scans with an vessel-selective gradient in the left-right direction with varying gradient strengths according to a predefined scheme (see Figure 1). The phase accrual over the labeling train was adapted to result in optimal labeling conditions at the right ICA for the first label-control pair, after which the phase accrual was adjusted to yield optimal labeling in the left ICA. These 4 conditions were subsequently repeated 20 times (6 minutes total scan-time). Subsequently, the gradient strength was adjusted for the next scan. The scans were analyzed with FMRIB Software Library v5.0 [5] and in-house developed Matlab R2016a algorithms (The MathWorks, Massachusetts).
The effect of the vessel-encoding gradient was expressed for each flow-territory as:
relative efficiency fluctuations = (SIperfect_label_conditon – SIsub_optimal_labeling) / SIpCASL
(i.e. for the right ICA territory of Figure 1: third column minus second column divided by non-selective pCASL).
Flow territories were determined in two different ways. The first exploits the knowledge when the labeling efficiency was varied by designing a general linear model (GLM) without blurring [6], whereas the second is the blind “Multivariate exploratory linear optimized decomposition into independent components (MELODIC)” method (that could be used when exploiting naturally occurring fluctuations). Finally, MELODIC was repeated after blurring with a 10mm Gaussian kernel. The ICAs were visually checked for components that represented flow territories.
The robustness of the method was assessed with receiver operating characteristic (ROC) curves, while only including intracranial voxels that were not part of the posterior circulation territory as taken from the gold-standard flow-territory mapping.
We thank Sophie Schmid, Wouter Teeuwisse and Annemarieke van Opstal for their help with acquiring scan data.
This research has been made possible by the Dutch Heart Foundation and the Netherlands Organisation for Scientific Research (NWO), as part of their joint strategic research programme: "Earlier recognition of cardiovascular diseases”. This project is partially financed by the PPP Allowance made available by Top Sector Life Sciences & Health to the Dutch Heart foundation to stimulate public-private partnerships. This research was also supported by the EU under the Horizon2020 program (project: CDS-QUAMRI), and the CAVIA project (nr. 733050202), which has been made possible by ZonMW
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