Comparison of pulsed arterial spin labeling encoding schemes and absolute perfusion quantification

Abstract

Arterial spin labeling (ASL) using magnetic resonance imaging (MRI) is a powerful noninvasive technique to investigate the physiological status of brain tissue by measuring cerebral blood flow (CBF). ASL assesses the inflow of magnetically labeled arterial blood into an imaging voxel. In the last 2 decades, various ASL sequences have been proposed which differ in their ease of implementation and their sensitivity to artifacts. In addition, several quantification methods have been developed to determine the absolute value of CBF from ASL magnetization difference images. In this study, we evaluated three pulsed ASL sequences and three absolute quantification schemes. It was found that FAIR-QUIPSSII implementation of ASL yields 10–20% higher signal-to-noise ratio (SNR) and 18% higher CBF as compared with PICORE-Q2TIPS (with FOCI pulses) and PICORE-QUIPSSII (with BASSI pulses). In addition, quantification schemes employed can give rise to up to a 35% difference in CBF values. We conclude that, although all quantitative ASL sequences and CBF calibration methods should in principle result in the similar CBF values and image quality, substantial differences in CBF values and SNR were found. Thus, comparing studies using different ASL sequences and analysis algorithms is likely to result in erroneous intra- and intergroup differences. Therefore, (i) the same quantification schemes should consistently be used, and (ii) quantification using local tissue proton density should yield the most accurate CBF values because, although still requiring definitive demonstration in future studies, the proton density of blood is assumed to be very similar to the value of gray matter.

Introduction

Arterial Spin Labeling (ASL) is a noninvasive magnetic resonance imaging (MRI) technique that provides quantitative information about local tissue (LT) perfusion by assessing the inflow of magnetically tagged arterial water into an imaging slice. Cerebral blood flow (CBF) is determined from the signal intensity differences of the magnetic resonance (MR) images with and without tagging, thereby subtracting out the static magnetization of the imaging slice [1], [2]. ASL has the potential to be very useful for clinical applications and has been used, for example, in vascular and neuronal diseases such as stroke, arteriostenosis, schizophrenia, Alzheimer's, epilepsy, etc. [3], [4]. Moreover, ASL is a powerful tool to study the baseline physiological state of brain tissue and the basis of blood oxygenation level-dependent (BOLD) signals [5].

A number of ASL techniques have been developed, which can be broadly divided into continuous and pulsed tagging techniques. In the latter, inversion is done with a slice-selective or non-slice-selective 180° pulse, and with continuous labeling, the inversion is done adiabatically as the blood moves through a gradient field during a continuous radiofrequency (RF) pulse [1], [2]. Ideally, CBF is proportional to the ASL difference signal (control–tag image). However, several confounding factors have been reported that complicate the calculation of a quantitative CBF map. The major systematic error is caused by transit delay artifacts from the intravascular tagged blood [6], [7], [8], [9], [10], [11], [12]. Large amplitude blood vessel artifacts may appear due to the presence of tagged blood flowing through the imaging slice dedicated to perfuse other slices. These systematic errors can be minimized if a delay is introduced between the tagging and image acquisition longer than the longest transit time of blood from the arteries to the capillaries.

In addition, artifacts stemming from how blood water is magnetically labeled and the control images acquired, such as magnetization transfer (MT) effects, yield false estimates of absolute CBF [11], [13]. To control for these effects, different pulsed ASL (PASL) sequences have been proposed. These methods vary in their tagging schemes, their sensitivity to the blood entering from the distal side of the slice and the type of applied inversion pulses. Here, we evaluated signal-to-noise ratio (SNR) and magnetization difference of three widely used PASL sequences (for details of the sequences, see Methods section):

• “Q2TIPS”: PICORE labeling scheme (proximal inversion with a control for off-resonance effects) using a FOCI pulse with Q2TIPS [QUIPSS-II (Quantitative Imaging of Perfusion Using a Single Subtraction) with thin-slice TI1 periodic saturation] [14].

• “FAIR-QII”: FAIR (flow-sensitive alternated inversion recovery) combined with QUIPSS-II saturation using a hyperbolic secant (HS) pulse for inversion [15].

• “BASSI-QII”: PICORE labeling scheme [using asymmetric BASSI (bandwidth-modulated selective saturation and inversion) pulses] with QUIPSS-II saturation [16].

Furthermore, to quantify absolute CBF, it is necessary to determine the equilibrium magnetization of blood: M_0B. Since M_0B cannot easily be determined in vivo, its value is usually computed using magnetization M₀ either of LT, white matter (WM) or cerebrospinal fluid (CSF). In principle, all ASL sequences and quantification schemes should yield the same absolute CBF values. In the present study, the absolute CBF values for gray matter (GM) obtained using the methods described above are within the physiologically plausible range: 54–80 ml/g-min. However, the PASL sequences used resulted in ∼18% different absolute CBF values. In addition, a higher SNR for FAIR-QII compared to the two other ASL schemes (10–20% higher SNR) was found. Furthermore, substituting M_0B of blood with M₀ values from WM, LT and CSF can give rise to up to ∼35% different absolute CBF values. That is, the different ASL and quantification schemes tested do not result in the same image quality and absolute CBF although the values for CBF and SNR are comparable in magnitude.