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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.

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.

This article aims to provide the reader with an overview of recent developments in Arterial Spin Labeling (ASL) MRI techniques. A great deal of progress has been made in recent years in terms of the SNR and acquisition speed. New strategies have been introduced to improve labeling efficiency, reduce artefacts, and estimate other relevant physiological parameters besides perfusion. As a result, ASL techniques has become a reliable workhorse for researchers as well as clinicians. After a brief overview of the technique's fundamentals, this article will review new trends and variants in ASL including vascular territory mapping and velocity selective ASL, as well as arterial blood volume imaging techniques. This article will also review recent processing techniques to reduce partial volume effects and physiological noise. Next the article will examine how ASL techniques can be leveraged to calculate additional physiological parameters beyond perfusion and finally, it will review a few recent applications of ASL in the literature.

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, cerebral hemodynamics were assessed in two patients with CVOD and a healthy control subject. We demonstrated 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.