Perfusion: ASL Basics & Analysis
Yang Li1

1Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

This educational talk covers the data acquisition of arterial spin labeling (ASL) technique and steps to obtain quantified cerebral blood flow (CBF) map. ASL data acquisition is usually composed of three components: labeling module to tag blood water in major feeding arteries, post-labeling delay module to allow tagged blood to reach tissue, and readout module to acquire images. ASL data processing involves motion correction and perfusion quantification using general kinetic model. In addition, common issues met in practice and examples of clinical application of ASL are briefly discussed.

Introduction

Perfusion is defined as the amount of blood delivered to capillary beds in a given tissue per unit time. It is an important physiological parameter that reflects the integrity of vascular system. Disorders of cerebral blood flow (CBF) can be found from mild neurodegenerative disease to acute ischemic stroke. Arterial spin labeling (ASL) MRI permits noninvasive quantification of perfusion by using arterial blood water as the intrinsic diffusible tracer [1]. After more than two decades of technical development, ASL has evolved to a mature MRI technique. A consensus paper on recommended implementation of ASL perfusion MRI for clinical applications has been published in a joint effort of the ISMRM Perfusion Study Group and the European ASL in Dementia consortium [2].

General Concept of ASL

ASL uses the radiofrequency (RF) pulses and gradients to invert the longitudinal magnetization of blood water spins in the feeding arteries. After a delay in which the labeled spins travel into the tissue, an image whose signal consists of static tissue and labeled blood is acquired, which is referred to as “label” image. Subsequently, a “control” image without labeling is also acquired, representing signals from static tissue and unlabeled blood. After subtraction of “label” and “control”, signal from static tissue is canceled out, leaving only labeled blood signal. The difference image is therefore proportional to the local tissue perfusion. Due to the low signal-to-noise ratio of labeled blood water spins, multiple pairs of “control” and “label” images need to be acquired and averaged. The averaged difference image is then entered into perfusion kinetic models to obtain quantified CBF map.

ASL Data Acquisition

Labeling module: the two widely used labeling schemes are: pulsed ASL (PASL) and pseudo-continuous ASL (pCASL) [3, 4]. PASL uses a very short pulse (~10 ms) to invert a thick slab (~100 mm) of arterial water spins. It has the advantage of high labeling efficiency and low SAR. While pCASL applies a series of small-flip-angle RF pulses and gradients (this is why this scheme is referred to as pseudo-continuous) that lasts typically ~2 seconds to invert the blood water spins passing through the thin labeling plane. This labeling scheme provides higher SNR than PASL yet higher SAR. Due to the fact that RF transmitter in clinical MRI could not offer continuous RF field with long duration, true continuous ASL is usually not feasible.

Post-labeling delay module: The waiting time (~2 seconds) after labeling, post-labeling delay (PLD), allows the labeled water spins to travel from major feeding arteries to capillary bed [5]. The selection of proper PLD value is a tread-off between minimizing arterial artifacts and retain signal from labeled water spins that decays at the rate of 1/T1. If PLD is too short, most of the labeled spins still reside in large arteries; while if PLD to too long, the final CBF image will be of less signal because the inverted labeled spins has recovered to similar level with control spins.

Readout module: Perfusion signal is usually at the level of ~0.5% of fully recovered magnetization. The signal intensity is also decaying while readout. Therefore, readout sequence with high acquisition efficiency and high SNR is preferred in ASL. 3D segmented acquisition, such as Gradient Spin Echo (GRASE) and stack of spirals, are commonly used [6, 7]. On the other hand, 2D multi-slice acquisition, such as EPI, is also widely used for the brain due to their availability on most MRI scanners [8].

Other advanced techniques: Besides the widely used PASL and pCASL, there are several variations of ASL that serves different imaging purpose. Multi-delay ASL acquires images at several delay time points to obtain temporal information of bolus arrival time [9]. Time-encoded ASL utilizes the flexible block design of labeling module to achieve similar results yet with increase acquisition efficiency [10]. Vessel-selective ASL allows flow territory mapping [11]. Velocity-selective ASL tags flowing water spins and thus enables larger amount of labeled bolus [12]. Most recently, when combining with MR fingerprinting, several parametric maps of hemodynamics can be obtained within one ASL scan [13, 14].

ASL Data Analysis

Pre-processing: Due to the intrinsic low SNR of ASL signal, averaging across repeated acquisitions of control and label images is often necessary. Motion between images need to be corrected to align control and label volumes, such that their subtraction and average is free of bright or dark rings at brain boundary.

CBF quantification: The difference image of control and label is proportional to CBF. In order to obtain quantified values, difference image is divided by a proton density image acquired in a separate scan to normalize the signal intensity. The normalized difference image along with assumed constant parameters are then entered into a formula to calculate CBF map, where the formula is derived from general kinetic model [2].

Group statistical analysis: After obtaining CBF maps, region-of-interest (ROI) values can be extracted when overlaying segmentation result of anatomical image (e.g. T1 weighted) to the perfusion maps. These outcome variables can then be readily used for study-specific statistical analyses.

Available toolboxes: There are several existing software tools that implemented the ASL processing steps above, such as ASLtbx, BASIL, ASAP [15-17]. Notably, a cloud-computing based tool, ASL-MRICloud, has been deployed online recently and is capable of automated processing [18]. User only needs a web browser to upload data and download results.

Common Issues in Practice

Several factors could result in artifacts in CBF map. For the labeling module, the labeling efficiency will be compromised if the labeling plane in pCASL is not perpendicular to targeted arteries or placed at artery segments that are tortuous. For PLD, signals in artery may dominate CBF map if PLD is shorter than the recommended values in white paper. For acquisition, head motion or cardiac pulsation artifacts could be difficulty to correct if a segmented readout scheme is used [19].

Clinical Application

The main areas of clinical applications for ASL are cerebrovascular disease, dementia, and neuro-oncology [20]. Different perfusion patterns can be found in these neurological diseases due to different pathological mechanisms.

Acknowledgements

None.

References

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Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)