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Background and Purpose: The circle of Willis (COW) is a crucial mechanism for cerebral collateral circulation. This proof-of-concept study aims to develop and assess an analysis method to characterize the hemodynamics of the arterial segments in the COW by using arterial spin-labeling (ASL) based non-contrast-enhanced dynamic MR angiography (dMRA)., Materials and Methods: The developed analysis method uses a graph model, bootstrap strategy, and ensemble learning methodologies to determine the time curve shift from ASL dMRA to estimate the flow direction within the COW. The performance of the method was assessed on 52 subjects, by using the flow direction, either antegrade or retrograde, derived from 3D phase-contrast MR imaging as the reference., Results: A total of 340 arterial segments in the COW were evaluated, among which 30 (8.8%) had retrograde flow according to 3D phase-contrast MRI. The ASL dMRA-based flow direction estimation has an accuracy, sensitivity, and specificity of 95.47%, 80%, and 96.34%, respectively., Conclusions: Using ASL dMRA and the developed image analysis method to estimate the flow direction in COW is feasible. This study provides a new method to assess the hemodynamics of the COW, which could be useful for the diagnosis and study of cerebrovascular diseases., (© 2024 by American Journal of Neuroradiology.)

Segmentation of cerebral vasculature on MR vascular images is of great significance for clinical application and research. However, the existing cerebrovascular segmentation approaches are limited due to insufficient image contrast and complicated algorithms. This study aims to explore the potential of the emerging four-dimensional arterial spin labeling magnetic resonance angiography (4D ASL-MRA) technique for fast and accurate cerebrovascular segmentation with a simple machine-learning approach. Nine temporal features were extracted from the intensity-time signal of each voxel, and eight spatial features from the neighboring voxels. Then, the unsupervised outlier detection algorithm, i.e. Isolation Forest, is used for segmentation of the vascular voxels based on the extracted features. The total length of the centerlines of the intracranial arterial vasculature, the dice similarity coefficient (DSC), and the average Hausdorff Distance (AVGHD) on the cross-sections of small- to large-sized vessels were calculated to evaluate the performance of the segmentation approach on 4D ASL-MRA of 18 subjects. Experiments show that the temporal information on 4D ASL-MRA can largely improve the segmentation performance. In addition, the proposed segmentation approach outperforms the traditional methods that were performed on the 3D image (i.e. the temporal average intensity projection of 4D ASL-MRA) and the previously proposed frame-wise approach. In conclusion, this study demonstrates that accurate and robust segmentation of cerebral vasculature is achievable on 4D ASL-MRA by using a simple machine-learning approach with appropriate features., Competing Interests: Declaration of competing interest None., (Copyright © 2023. Published by Elsevier Inc.)