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This review article provides an overview of a range of recent technical developments in advanced arterial spin labeling (ASL) methods that have been developed or adopted by the community since the publication of a previous ASL consensus paper by Alsop et al. 1 . It is part of a series of review/recommendation papers from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group. Here, we focus on advancements in readouts and trajectories, image reconstruction, noise reduction, partial volume correction, quantification of non-perfusion parameters, fMRI, fingerprinting, vessel selective ASL, angiography, deep learning, and ultra-high field ASL. We aim to provide a high level understanding of these new approaches and some guidance for their implementation, with the goal of facilitating the adoption of such advances by research groups and by MRI vendors. Topics that are outside the scope of this article, and are reviewed at length in separate articles, include velocity selective ASL, multiple-timepoint ASL, body ASL, and clinical ASL recommendations.

The development of cerebrovascular disease is tightly coupled to changes in cerebrovascular hemodynamics, with altered flow and relative pressure indicative of the onset, development, and acute manifestation of pathology. Image-based monitoring of cerebrovascular hemodynamics is, however, complicated by the narrow and tortuous vasculature, where accurate output directly depends on sufficient spatial resolution. To address this, we present a method combining dedicated deep learning and state-of-the-art 4D Flow MRI to generate super-resolution full-field images with coupled quantification of relative pressure using a physics-driven image processing approach. The method is trained and validated in a patient-specific in-silico cohort, showing good accuracy in estimating velocity (relative error: 12.0 ± 0.1%, mean absolute error (MAE): 0.07 ± 0.06 m/s at peak velocity), flow (relative error: 6.6 ± 4.7%, root mean square error (RMSE): 0.5 ± 0.1 mL/s at peak flow), and with maintained recovery of relative pressure through the circle of Willis (relative error: 11.0 ± 7.3%, RMSE: 0.3 ± 0.2 mmHg). Furthermore, the method is applied to an in-vivo volunteer cohort, effectively generating data at

Cerebral hemodynamics in the presence of cerebrovascular occlusive disease (CVOD) are influenced by the anatomy of the intracranial arteries, the degree of stenosis, the patency of collateral pathways, and the condition of the cerebral microvasculature. Accurate characterization of cerebral hemodynamics is a challenging problem. In this work, we present a strategy to quantify cerebral hemodynamics using computational fluid dynamics (CFD) in combination with arterial spin labeling MRI (ASL). First, we calibrated patient-specific CFD outflow boundary conditions using ASL-derived flow splits in the Circle of Willis. Following, we validated the calibrated CFD model by evaluating the fractional blood supply from the main neck arteries to the vascular territories using Lagrangian particle tracking and comparing the results against vessel-selective ASL (VS-ASL). Finally, the feasibility and capability of our proposed method were demonstrated in two patients with CVOD and a healthy control subject. We showed that the calibrated CFD model accurately reproduced the fractional blood supply to the vascular territories, as obtained from VS-ASL. The two patients revealed significant differences in pressure drop over the stenosis, collateral flow, and resistance of the distal vasculature, despite similar degrees of clinical stenosis severity. Our results demonstrated the advantages of a patient-specific CFD analysis for assessing the hemodynamic impact of stenosis.

Purpose This paper discusses several challenges faced by super-selective pseudo-continuous arterial spin labeling, which is used to quantify territorial perfusion in the cerebral circulation. The effects of off-resonance, pulsatility, vessel movement, and label rotation scheme are investigated, and methods to maximize labeling efficiency and overall image quality are evaluated. A strategy to calculate the territorial perfusion fractions of individual vessels is proposed. Methods The effects of off-resonance, label rotation scheme, and vessel movement on labeling efficiency were simulated. Two off-resonance compensation strategies (multiphase prescan, field map), cardiac triggering, and vessel movement were studied in vivo in a group of 10 subjects. Subsequently, a territorial perfusion fraction map was acquired in 2 subjects based on the mean vessel labeling efficiency. Results Multiphase calibration provided the highest labeling efficiency (P = .002) followed by the field map compensation (P = .037) compared with the uncompensated acquisition. Cardiac triggering resulted in a qualitative improvement of the image and an increase in signal contrast between the perfusion territory and the surrounding tissue (P = .010) but failed to show a significant change in temporal and spatial SNR. The constant clockwise label rotation scheme yielded the highest labeling efficiency. Significant vessel movement (>2 mm according to simulations) was observed in 50% of subjects. The measured territorial perfusion fractions showed good agreement with anatomical data. Conclusion Optimized labeling efficiency resulted in increased image quality and accuracy of territorial perfusion fraction maps. Labeling efficiency depends critically on off-resonance calibration, cardiac triggering, optimal label rotation scheme, and vessel location tracking.