ASL
Arterial Spin Labeling Perfusion MRI
By Manuel Taso, PhD, Instructor in Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Division of MRI Research, Boston, USA
First published in the free digital book of Olea Medical ‘Spin to the Limit:
MRI Physical and Principles Challenges‘
Basics of Arterial Spin Labeling - Arterial Spin Labeling (ASL) is a non-invasive MRI-based method allowing tissue blood flow imaging without the use of contrast agents. Originally proposed in 1992, it has since then become a widely used method for measuring perfusion in the body using MRI. This section aims to provide a short and non-exhaustive overview of the technique by going over the basics, labeling methods and non-exhaustive mention of some clinical applications.
First published in the free digital book of Olea Medical ‘Spin to the Limit:
MRI Physical and Principles Challenges‘
Basics of Arterial Spin Labeling - Arterial Spin Labeling (ASL) is a non-invasive MRI-based method allowing tissue blood flow imaging without the use of contrast agents. Originally proposed in 1992, it has since then become a widely used method for measuring perfusion in the body using MRI. This section aims to provide a short and non-exhaustive overview of the technique by going over the basics, labeling methods and non-exhaustive mention of some clinical applications.
ASL relies on the use of arterial blood water as an endogenous tracer, which has numerous advantages such as excellent diffusivity across membranes and wide availability. Analogous to positron emission tomography (PET), in which labeled radioactive water is injected and then decays, a “bolus” is formed by radiofrequency (RF) labeling of upstream arterial blood water. The process is followed by a waiting time (referred to as post-labeling delay, or PLD) and then image acquisition, forming what is referred to as a “labeled” or “tagged” image. This time is required to let labeled blood reach and exchange within the tissue of interest.
The RF labeling is performed by modifying the longitudinal magnetization (Mz) of blood by different means (saturation or inversion), forming the “labeled” image. The same experiment is then repeated without any labeling, producing what is referred to as a “control” image. By subtracting the labeled from the control image, static tissue signal is mostly removed, leaving the blood flow related signal only (dM = Mcontrol – Mlabel). This process is highlighted in Figure 1.
Since ASL relies on modification of the blood longitudinal magnetization, it is important to mention that the label decays with the longitudinal relaxation time (T1) of blood and tissue, leading to a label lifetime of a few seconds.
A non-exhaustive overview of different existing labeling methods and imaging sequences is provided hereafter.
The RF labeling is performed by modifying the longitudinal magnetization (Mz) of blood by different means (saturation or inversion), forming the “labeled” image. The same experiment is then repeated without any labeling, producing what is referred to as a “control” image. By subtracting the labeled from the control image, static tissue signal is mostly removed, leaving the blood flow related signal only (dM = Mcontrol – Mlabel). This process is highlighted in Figure 1.
Since ASL relies on modification of the blood longitudinal magnetization, it is important to mention that the label decays with the longitudinal relaxation time (T1) of blood and tissue, leading to a label lifetime of a few seconds.
A non-exhaustive overview of different existing labeling methods and imaging sequences is provided hereafter.
Control
Labeled
Perfusion
Figure 1: Basic illustration of an ASL experiment
1. Labeling methods
ASL labeling methods can be categorized into two main categories: spatially-selective and velocity-selective methods.
Spatially-selective methods are by far the most commonly used, and can be further subdivided between pulsed and continuous labeling.
Pulsed-labeling methods, or PASL, rely on inversion at a single time-point of a large volume to ensure that sufficient blood is labeled. Figure 2 illustrates one of the multiple PASL implementations named FAIR (Flow-Sensitive Alternating Inversion Recovery) proposed by Kwong et al.[1] and Kim[2], in which the labeled image is formed by a non-selective inversion – inverting all spins within the volume of the transmitting RF coil.
For the control condition, a selective inversion is employed to invert only the slice of interest, hence effectively labeling blood in a volume determined by the subtraction of the non-selective from the selective experiment. With the use of robust adiabatic inversions, PASL methods usually achieve very high labeling efficiency (> 90%) but also low-power (i.e. SAR) deposition. As seen on Figure 2, PASL methods also benefit from ease of implementation. However, their SNR can be somewhat limited and some uncertainties pertaining to blood flow quantification arise because of unknown bolus duration. To address this issue, post-inversion saturation is commonly used with methods such as QUIPSS II or Q2TIPS[3-4], to define the bolus duration. FAIR is described here, but other pulsed labeling methods have also been developed and successfully applied.
Spatially-selective methods are by far the most commonly used, and can be further subdivided between pulsed and continuous labeling.
Pulsed-labeling methods, or PASL, rely on inversion at a single time-point of a large volume to ensure that sufficient blood is labeled. Figure 2 illustrates one of the multiple PASL implementations named FAIR (Flow-Sensitive Alternating Inversion Recovery) proposed by Kwong et al.[1] and Kim[2], in which the labeled image is formed by a non-selective inversion – inverting all spins within the volume of the transmitting RF coil.
For the control condition, a selective inversion is employed to invert only the slice of interest, hence effectively labeling blood in a volume determined by the subtraction of the non-selective from the selective experiment. With the use of robust adiabatic inversions, PASL methods usually achieve very high labeling efficiency (> 90%) but also low-power (i.e. SAR) deposition. As seen on Figure 2, PASL methods also benefit from ease of implementation. However, their SNR can be somewhat limited and some uncertainties pertaining to blood flow quantification arise because of unknown bolus duration. To address this issue, post-inversion saturation is commonly used with methods such as QUIPSS II or Q2TIPS[3-4], to define the bolus duration. FAIR is described here, but other pulsed labeling methods have also been developed and successfully applied.
Control
Labeled
Figure 2: Flow-Sensitive Alternating Inversion Recovery (FAIR) labeling diagram
Figure 2: Flow-Sensitive Alternating Inversion Recovery (FAIR) labeling diagram
Continuous labeling was the original implementation proposed back in 1992 by Williams et al.[5], implemented as a continuous saturation technique using the concept of flow-driven adiabatic inversion as originally proposed for angiography purposes[6]. Although offering higher SNR compared to pulsed methods, continuous ASL (CASL) suffers from significant shortcomings limiting its potential for widespread use, such as hardware incompatibility (clinical MR scanners do not allow continuous RF operation), but also limited multi-slice capabilities because of magnetization transfer (MT) effects. A CASL modification of the control experiment used an amplitude-modulation[7] scheme allowing multi-slice but lowering labeling efficiency, while the use of a separate tagging coil was also successfully developed to avoid MT effects[8].
Later, a modification of CASL was proposed to cope with those limitations. By replacing a continuous RF waveform with short, repeated RF pulses in the presence of a slice-selective gradient, Dai et al.[9] showed that a high SNR could be retained compared to pulsed labeling methods, while improving labeling efficiency (> 80%) and allowing multi-slice and/or 3D acquisitions. This method is now referred to as pseudo-continuous ASL (Figure 3), or PCASL, and has become the most used ASL method for brain imaging as recommended by a white paper published in 2015[10]. A modification of PCASL applying an additional gradient to restrict the labeling plane to individual vessels has been proposed, allowing vessel-selective imaging for specific perfusion territory mapping[11-13].
Later, a modification of CASL was proposed to cope with those limitations. By replacing a continuous RF waveform with short, repeated RF pulses in the presence of a slice-selective gradient, Dai et al.[9] showed that a high SNR could be retained compared to pulsed labeling methods, while improving labeling efficiency (> 80%) and allowing multi-slice and/or 3D acquisitions. This method is now referred to as pseudo-continuous ASL (Figure 3), or PCASL, and has become the most used ASL method for brain imaging as recommended by a white paper published in 2015[10]. A modification of PCASL applying an additional gradient to restrict the labeling plane to individual vessels has been proposed, allowing vessel-selective imaging for specific perfusion territory mapping[11-13].
Control
Labeled
Figure 3: Pseudo-continuous ASL (PCASL) labeling diagram
Finally, another avenue for ASL imaging exploits velocity selectivity (VS) instead of spatial selectivity[14]. VS-ASL methods are based on the use of motion-sensitizing gradients, akin to diffusion for the simplest implementation[15], that lead to spin dephasing and signal attenuation above a cutoff velocity determined by gradients timing and amplitude. This provides inherent advantages over spatially-selective ASL, especially when considering complex vascular geometry, as well as reduced transit time sensitivity by being able to label closer to the tissue of interest.
However, most velocity-selective methods rely on saturation instead of inversion, offering only half the SNR of most spatially-selective ASL methods. They also have some sensitivity to B0 and B1 inhomogeneities, although significant efforts were directed towards improving this issue[16]. Recently, velocity-selective inversion methods[17] have been proposed to match the SNR of inversion-based methods.
However, most velocity-selective methods rely on saturation instead of inversion, offering only half the SNR of most spatially-selective ASL methods. They also have some sensitivity to B0 and B1 inhomogeneities, although significant efforts were directed towards improving this issue[16]. Recently, velocity-selective inversion methods[17] have been proposed to match the SNR of inversion-based methods.
2. Additional considerations
ASL is intrinsically a low SNR method as the measured difference represents a small fraction of the fully-relaxed magnetization (≈1-2%). This makes it sensitive to various sources of signal fluctuations, such as hardware instabilities or bulk/physiological motion, degrading image quality in long/segmented acquisitions but also precluding the use of ASL for dynamic studies in which temporal stability is essential. Early on, it was reported that a combination of saturation and inversion pulses for background suppression, in an analogous way to what is achieved in subtraction-based angiography techniques[18], could be helpful in mitigating signal instabilities by reducing static tissue signal up to 100-fold[19-20], albeit with a small loss of labeling efficiency due to imperfect inversion[21]. This method has been particularly beneficial when coupled to segmented imaging techniques for volumetric acquisitions or potentially for extra-cranial use of ASL, but also for imaging challenging areas.
3. Imaging sequences
As a magnetization preparation, ASL can be combined with a variety of imaging sequences, which is a major advantage: this explains the variety of reported implementations.
Early experiments applied ASL with single-shot echo planar imaging (EPI). Today, EPI remains very popular especially because of motion robustness, but also due to advances such as simultaneous-multi-slice (SMS) excitation allowing the acquisition of a significant number of slices within a single TR[22]. Other popular options for 2D imaging have been based on balanced SSFP or single-shot fast spin echo (FSE), mainly for extra-cranial applications[23] for which segmented acquisitions are challenging because of motion. However, volumetric acquisitions have also been implemented and optimized, mainly with 3D spiral FSE[24] or GRASE sequences[25]; they have facilitated the translation of ASL from a research technique to a clinically useful and usable method. Other 3D implementations relying on Cartesian FSE[26-28] imaging have also been more recently proposed and have shown significant potential for brain and body applications – but can still be considered under research development.
Early experiments applied ASL with single-shot echo planar imaging (EPI). Today, EPI remains very popular especially because of motion robustness, but also due to advances such as simultaneous-multi-slice (SMS) excitation allowing the acquisition of a significant number of slices within a single TR[22]. Other popular options for 2D imaging have been based on balanced SSFP or single-shot fast spin echo (FSE), mainly for extra-cranial applications[23] for which segmented acquisitions are challenging because of motion. However, volumetric acquisitions have also been implemented and optimized, mainly with 3D spiral FSE[24] or GRASE sequences[25]; they have facilitated the translation of ASL from a research technique to a clinically useful and usable method. Other 3D implementations relying on Cartesian FSE[26-28] imaging have also been more recently proposed and have shown significant potential for brain and body applications – but can still be considered under research development.
4. Blood flow quantification
Another strength of ASL lies in its straightforward quantification of absolute blood flow in physiological units (mL/100g/min). The standard and simplest ASL model for quantification, known as the standard kinetic model[29-30], is widely used and based on modified Bloch equations with few assumptions and little additional measurement to derive quantitative blood flow from the measured signal. Key assumptions include full delivery of the labeled bolus (achieved by using a PLD/TI longer than the transit time required to reach the tissue), total and instantaneous exchange of the blood with the tissue and similar T1 between the blood and the tissue. However, while recommended because of its robustness and simplicity, this model referred to as single-compartment, can be improved by incorporating additional information such as transit time or tissue T1, at the cost of longer acquisition times and complex processing.
CLINICAL APPLICATIONS
Originally, ASL found its place in cerebral perfusion imaging, in both neuroscience research and clinical neuroradiology. However, recent years have also seen the emergence of more and more clinical applications beyond the brain.
1. Neuro applications
ASL has been used to answer a wide range of neuroscientific or clinical questions. ASL has been applied in neurovascular diseases and especially in ischemic stroke[31], where the identification of perfusion deficits combined with diffusion imaging enables to define the ischemic core and the penumbra[32]. Numerous studies have investigated the diagnostic and prognostic value of ASL in ischemic stroke.
Oncology applications have also been widely developed, since perfusion can help discriminate between different brain tumors. For example, ASL has been successfully applied to diagnose and grade gliomas[33], but also to follow the effect of therapies such as antiangiogenics[34] or separate tumor recurrence from radiation necrosis[35]. Finally, ASL has also been widely used for diagnosis and clinical research in Alzheimer’s disease (AD) and other neurodegenerative conditions[36]. Indeed, it was found that ASL could potentially provide early markers for neurodegenerative diseases by showing cerebral hypoperfusion strongly linked to metabolic alterations as seen with PET imaging.
Oncology applications have also been widely developed, since perfusion can help discriminate between different brain tumors. For example, ASL has been successfully applied to diagnose and grade gliomas[33], but also to follow the effect of therapies such as antiangiogenics[34] or separate tumor recurrence from radiation necrosis[35]. Finally, ASL has also been widely used for diagnosis and clinical research in Alzheimer’s disease (AD) and other neurodegenerative conditions[36]. Indeed, it was found that ASL could potentially provide early markers for neurodegenerative diseases by showing cerebral hypoperfusion strongly linked to metabolic alterations as seen with PET imaging.
2. Beyond the brain
Early on in its development, ASL was shown to be feasible and useful outside of the brain, especially for renal imaging[37]. Since then, ASL has been applied in a variety of organs and conditions. While providing an exhaustive overview of extracranial ASL applications is beyond the scope of this review, a few selected applications can be highlighted. Particularly, renal ASL has seen increased interest as implementations are becoming more available for multiple applications, such as evaluation of renal masses[38] but also as a biomarker of renal function in various pathologies such as diabetes-induced nephropathy[39] or renal transplants[40].
Besides renal imaging, many other organs have been successfully explored with ASL such as the heart, pancreas, placenta or lungs in a variety of conditions.
Besides renal imaging, many other organs have been successfully explored with ASL such as the heart, pancreas, placenta or lungs in a variety of conditions.
“When I started working in the MR field, I got interested in a method to measure perfusion called Arterial Spin Labeling or ASL. The amazing aspect is that, from the start, ASL was a method able to be quantitative. However, nearly 30 years after the first two papers[1-2], while it has finally made it into the clinic, ASL is still quite not used as a quantitative imaging method. The measurement of the cerebral blood flow as a physiological parameter is still left unresolved. While all manufacturers sell ASL as a “quantitative” method, it is not exactly as such in FDA’s definition, because of the lack of any reference standard to be compared to. This means that in case of systematic error, there is no way to actually assess it. For that reason, I have been trying to move ASL more and more into its quantitative potential. First, by working on different methodologies like QUASAR for example, which measures all aspects of the ASL signal and uses a model-free estimate of the perfusion[3]. Beyond that, we were the first in Singapore to publish results of a very large test-retest study, trying to show that we could actually achieve measurements with a reasonable precision[4]. Again, we could not compare ASL to a gold standard, but we could at least demonstrate in individual healthy volunteers the extent of the confidence interval for the parameters’ measurement. However, we still did not know whether there was a systematic bias in the quantification of perfusion with ASL.”
“Quantification is important for one major reason. If any measurement of any physiological parameter is performed over time in a patient in a longitudinal study, we have to be able to demonstrate that a change observed between time-point A and time-point B is due to a change in the patient physiology and not a result from the imprecision of the method – where the bias might have varied between the first and the second observation.The only way to assess this conclusion is to have a reference standard. For most applications, if we are measuring a static parameter, it is relatively easy to make a standard. If we know a bit of chemistry, we can create standards usually called phantoms in MRI. By scanning them repeatedly, we can assess how stable the measurements are over time. The analysis of that stability can be considered for the quantification, in order to separate the effect resulting from the measuring device, in this case the MR scanner, from the patient. For that reason, 5 years ago, we founded a company to develop a quantitative standard for imaging. The aim was to be able to disentangle the differences between measurements at 2 time-points, potentially linked to degradation of the device on the one side, or degradation of the state of the patient on the other side. Our first product was addressing ASL quantification with the development of a dedicated perfusion phantom, a very complex and expensive system. Its use is so far mostly restricted to large research centers that cannot only test the quality of their equipment, but also develop their own sequences. We also produced a series of other simpler phantoms for measuring static aspects. Since our company was founded, others followed soon; there are now probably half a dozen companies providing MRI phantoms for this particular niche aspect, i.e. making sure that measurements are quantitative and reproducible. If we were the first ones providing phantoms for functional parameters, companies dating back from the 1980s provided calibration standards for T1 and T2 measurement.”
Xavier Golay, PhD.
References
1 Williams DS, Detre JA, Leigh JS, Koretsky AP. Magnetic resonance imaging of perfusion using spin inversion of arterial water. Proceedings of the National Academy of Sciences. 1992;89(1):212-216.
2 Detre JA, Leigh JS, Williams DS, Koretsky AP. Perfusion imaging. Magnetic resonance in medicine. 1992; 23(1):37-45.
3 Petersen ET, Lim T, Golay X. Model-free arterial spin labeling quantification approach for perfusion MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine. 2006;55(2):219-232.
4 Petersen ET, Mouridsen K, Golay X. The QUASAR reproducibility study, Part II: Results from a multi-center Arterial Spin Labeling test–retest study. Neuroimage. 2010;49(1):104-113.
WHAT NOW?
After almost 30 years of existence, ASL has developed into a robust non-invasive physiologic and functional imaging technique. Through pioneering developments and clinical applications, it has gained traction as highlighted by several international initiatives, some sponsored by the International Society for Magnetic Resonance in Medicine (ISMRM) such as the Open Source Initiative for Perfusion Imaging (OSIPI).
Among these, a consensus formed by a group of worldwide-leading experts has produced a set of recommendations for clinical use of ASL for brain applications[10], facilitating clinical translation. More recently, an equivalent set of recommendations was produced for renal perfusion imaging[41] using ASL as a result of a European Union COST action named PARENCHIMA. This shows the synergy and widespread interest in non-contrast ASL perfusion imaging within but also beyond the brain. Combined with hardware, pulse sequences and image reconstruction developments that have happened simultaneously, a multitude of advanced applications have emerged.
Among these, a consensus formed by a group of worldwide-leading experts has produced a set of recommendations for clinical use of ASL for brain applications[10], facilitating clinical translation. More recently, an equivalent set of recommendations was produced for renal perfusion imaging[41] using ASL as a result of a European Union COST action named PARENCHIMA. This shows the synergy and widespread interest in non-contrast ASL perfusion imaging within but also beyond the brain. Combined with hardware, pulse sequences and image reconstruction developments that have happened simultaneously, a multitude of advanced applications have emerged.
These are currently mostly used in research settings, but there is no doubt that significant ASL advances are on their way to the clinic. Among them, improving the accuracy of ASL quantification by accurately characterizing labeling efficiency[42-43], accounting for arterial transit time by simultaneous measurement of multiple parameters such as blood flow and transit delay using time-encoded technique[24] or MR fingerprinting[44] are rapidly evolving research areas. In parallel, improvements in velocity-selective ASL methods such as acceleration-selective ASL[45] or velocity-selective inversion-based in the brain[17] but also abdomen[46-47] have been extremely promising. Finally, the push towards AI-based reconstructions will also benefit ASL applications by dramatically improving image quality and scan times as already seen with compressed sensing[27,48] based reconstruction methods and pioneering work using deep neural networks for image reconstruction and denoising[49-51]. Altogether, there is little doubt that the future of ASL is extremely bright and that all these past and future developments will ensure its role as a powerful and versatile imaging technique throughout the body. //
Read more about the history, physics, applications and future of MRI in the book:
https://en.calameo.com/olea-medical/read/0065238418579b9d4b159
‘Spin to the Limit: MRI Physical and Principles Challenges’(free of charge) of Olea Medical.
https://en.calameo.com/olea-medical/read/0065238418579b9d4b159
‘Spin to the Limit: MRI Physical and Principles Challenges’(free of charge) of Olea Medical.
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Ex¬ploring label dynamics of velocity-selective arteri¬al spin labeling in the kidney. Magnetic Resonance in Medicine. 2021. [48] Taso M, Munsch F, Zhao L, Alsop DC. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow & Metabolism. 0271678X20982382. [49] Kim KH, Choi SH, Park SH. Improving arterial spin labeling by using deep learning. Radiology. 2018;287(2):658-666. [50] Gong K, Han P, El Fakhri G, Ma C, Li Q. Arterial spin labeling MR image de¬noising and reconstruction using unsupervised deep learning. NMR in Biomedicine. 2019;e4224. [51] Huang W, Luo M, Liu X, Zhang P, Ding H, Xue W, Ni D. Arterial spin labeling images synthesis from sMRI using unbalanced deep discriminant learning. IEEE transactions on medical imaging. 2019;38(10):2338-2351.
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Arterial spin labeling MRI is able to detect early hemodynamic changes in diabetic nephrop¬athy. Journal of Magnetic Resonance Imaging. 2017;46(6):1810-1817. [40] Artz NS, Sadowski EA, Wentland AL, Djamali A, Grist TM, Seo S, Fain SB. Reproducibility of renal perfusion MR imaging in native and transplanted kidneys using non-con¬trast arterial spin labeling. Journal of Magnetic Resonance Imaging. 2011;33(6):1414-1421. [41] Nery F, Buchanan CE, Harteveld AA, Odudu A, Bane O, Cox EF, Fernández-Seara MA. Consen¬sus-based technical recommendations for clinical translation of renal ASL MRI. Magnetic Resonance Materials in Physics, Biology and Medicine. 2020;33(1):141-161. [42] Aslan S, Xu F, Wang PL, Uh J, Yezhuvath US, Van Osch M, Lu H. Estimation of labeling efficiency in pseudocontinuous arterial spin labeling. Magnetic resonance in medicine. 2010;63(3):765-771. [43] Chen Z, Zhao X, Zhang X, Guo R, Teeuwisse WM, Zhang B, Koken P, Smink J, Yuan C, van Osch MJ. Simultaneous measurement of brain perfusion and labeling efficiency in a sin¬gle pseudo-continuous arterial spin labeling scan. Magnetic resonance in medicine. 2018;79(4):1922- 1930. [44] Wright KL, Jiang Y, Ma D, Noll DC, Gris¬wold MA, Gulani V, Hernandez-Garcia L. Estima¬tion of perfusion properties with MR fingerprinting arterial spin labeling. Magnetic resonance imag¬ing. 2018;50:68-77. [45] Schmid S, Ghariq E, Teeu¬wisse WM, Webb A, van Osch MJ. Accelera¬tion-selective arterial spin labeling. Magnetic resonance in medicine. 2014;71(1):191-199. [46] Franklin SL, Bones IK, Harteveld AA, Hirschler L, van Stralen M, Qin Q, de Boer A, Hoogduin JM, Bos C, van Osch MJ, Schmid S. Multi-organ comparison of flow-based arterial spin labeling techniques: Spatially non-selective labeling for cerebral and renal perfusion imaging. Magnetic resonance in medicine. 2021;85(5):2580-2594. [47] Bones IK, Franklin SL, Harteveld AA, van Osch MJ, Schmid S, Hendrikse J, Moonen C, van Stralen M, Bos C. 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