Recent progress in ASL

Abstract

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.

Figure 1

A basic pseudo-continuous ASL experiment. The arterial water is inverted as it crosses a plane through the neck prior to acquiring the “labeled” image. A second “control” image is acquired without the label. The subtraction of these two images yields a perfusion weighted image.

Figure 2

A basic velocity selective labeling pulse train.

Figure 3

Velocity Selective Saturation pulses using a BIR-8 scheme in order to reduce eddy current artefacts. In the control case, the velocity selective gradients are turned off.

Figure 4

Velocity Selective Inversion pulse described by Qin et al. A 180 degree hard pulse is broken into nine 20 degree segments. Additional 180’s are interspersed to refocus off-resonance effects and correct for B1 inhomogeneity. These follow an MLEV phase pattern. In the control case, the velocity selective gradients are always positive (shown in grey).

Figure 5

A vessel selective pseudo-continuous labeling pulse train. G_x and G_y represent the additional in-plane gradients which allow to label individual vessels within the labeling plane. These gradients are played in the gap between RF pulses, typically at the same time as the rephaser of the slice selective gradient. In the control case, the sign of the RF pulses is alternated every other pulse (shown in grey).

Figure 6

Vessel-selective labeling schemes. The left panel shows a vessel-encoded labeling pattern that labels specific groups of arteries. The in-plane gradients generate a linear phase variation along the gradient direction, resulting in an approximately sinusoidal inversion profile across the label plane. The individual perfusion territories are then calculated by either solving a set of linear equations or by using a clustering approach. The right panel shows a schematic of the super-selective labeling scheme, which can isolate individual arteries by rotating the “labeling band” during the pseudo continuous labeling train of pulses.

Figure 7

The ASL signal can be modeled by describing the concentration of label in the voxel using one compartment and two compartments. The two-compartment model has also been modified in order to capture flow-thru effects.

Figure 8

ASL signal models capture the concentration of the label in a voxel as they move through the arterial compartment into the extravascular compartment, using the two-compartment model (without the pass-thru artery) in the middle panel of figure 7. Generally, the observed signal is the sum of the two compartments, unless the arterial compartment is suppressed during the acquisition.

Figure 9

The TRUST pulse sequence consists of a labeling module to isolate the venous inflow, followed by a T2 weighting module to differentiate between oxygenated and deoxygenated blood.

Figure 10

A Look-Locker ASL acquisition consisting of a labeling module immediately followed by a train of fast acquisitions to capture the inflow of the label into the arterial tree and its uptake by the extra vascular compartment.

Figure 11

Bolus Arrival Time (Arterial Transit Time) information can be obtained by multiple ASL acquisitions with varying duration and delay of the labeling function. The Hadamard encoding sequence constitutes an efficient combination of timings in order to collect the ASL input function.

Figure 12

ASL fingerprinting. A pseudo-random labeling scheme is used to generate an image time series whose signal is unique for a given combination of hemodynamic parameters. The top panel indicates a labeling function, the second panel shows the concentration of the label in the arterial compartment. Similarly, the third panel shows the concentration of the label in the extra-vascular compartment. The total magnetization (spin history) of the whole voxel is plotted in the fourth compartment. Finally, the bottom panel shows the observed time series from two different voxels.