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Published online before print November 30, 2007, 10.1148/radiol.2461062100

(Radiology 2007;246:572.)

A more recent version of this article appeared on December 1, 2007
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© RSNA, 2007

Technical Developments

Cerebral Border Zones between Distal End Branches of Intracranial Arteries

MR Imaging1

Jeroen Hendrikse, MD, PhD, Esben Thade Petersen, MSc, Peter Jan van Laar, MD, and Xavier Golay, PhD

1 From the Department of Radiology, University Medical Center Utrecht, Hp E 01.132, PO Box 85500, 3508 GA Utrecht, the Netherlands (J.H., P.J.v.L.); Department of Neuroradiology, National Neuroscience Institute, Singapore (E.T.P., X.G.); and MRI Facility, Singapore Bioimaging Consortium, Agency for Science, Technology and Research (A*STAR), Singapore (X.G.). Received December 9, 2006; revision requested February 20, 2007; revision received March 23; accepted April 25; final version accepted July 2. Supported in part by Philips Medical Systems, the Netherlands Organization for Scientific Research, and the following Singapore grants: NMRC/0919/2004, NMRC/CPG/009/2004, and NHGA-RPR/04012. Address correspondence to J.H. (e-mail: j.hendrikse{at}umcutrecht.nl).


   ABSTRACT
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 ABSTRACT
INTRODUCTION
 Materials and Methods
Results
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Implication for Patient Care...
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This study had institutional review board approval; informed consent was obtained from all participants. The study purpose was to prospectively determine whether a longer arterial transit time (ATT), from the proximal vasculature in the neck toward the distal end branches of the intracranial arteries, can be utilized to identify cerebral border zone regions. A magnetic resonance (MR) imaging method based on noninvasive arterial spin-labeling (ASL) perfusion MR imaging with image acquisition at a series of increasing delay times was used to quantify regional ATTs. Fifteen healthy volunteers (age range, 22–34 years; nine men, six women) were included. ASL perfusion MR imaging demonstrated an increase in ATT in the cerebral border zone regions, extending from the frontal and occipital horns of the lateral ventricle to the frontal and parietooccipital cortices, relative to ATT in non–border zone regions. Cerebral blood flow and arterial blood volume in these anterior and posterior border zone regions were significantly lower (P < .001) than in non–border zone regions.

© RSNA, 2007


   INTRODUCTION
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 ABSTRACT
 INTRODUCTION
Materials and Methods
Results
 Discussion
 Advances in Knowledge
Implication for Patient Care...
 References
 
The cerebral border zone regions, or watershed regions, are distinguished from main territory regions on the basis of their vascular supply by the distal end branches of the cerebral arteries (1). With a systemic drop in perfusion pressure or a decrease in perfusion pressure distal to a carotid artery occlusion, the border zone regions are at increased risk of ischemia and infarction (1–3). Three border zone regions are usually distinguished (1,4,5): The anterior border zone lies between the cortical branches of the anterior cerebral artery and the middle cerebral artery (MCA); the posterior border zone lies between the cortical branches of the MCA and the posterior cerebral artery; and the internal border zone lies along and above the lateral ventricle, between the deep and superficial branches of the MCA, or between the superficial branches of the MCA and anterior cerebral artery (1).

A fixed location of the cerebral border zone regions is presumed when classification of infarcts into either a border zone or a territory type is performed (3,4). However, it is very likely that, similar to the variability in flow territories of individual brain feeding arteries, considerable between-subject variation exists in the exact location of cerebral border zone regions (6,7). Therefore, studies on the pathophysiology of ischemia in the cerebral border zone regions would benefit from an imaging method capable of cross-sectional delineation of the border zone regions in an individual patient (8).

Arterial spin-labeling (ASL) magnetic resonance (MR) imaging with acquisition of a series of images at increasing delay times in a single examination for assessment of flow dynamics, comparable to conventional angiography (9–11), has been developed. In addition to providing information on intravascular hemodynamics, such ASL MR imaging signal at multiple delay times can be exploited in perfusion imaging for measurements of regional differences in arterial transit time (ATT) and cerebral blood flow (CBF) (12–15).

We hypothesized that, because of the distal location of the border zone regions between the end branches of the intracranial arteries, the travel distance of the arterial blood from the proximal vasculature in the neck toward these regions is relatively long. As a result of the longer arterial travel distance, the cerebral border zone regions may possess a longer ATT relative to that of the surrounding brain tissue. Thus, regional heterogeneity in ATT may be utilized as a parameter for delineating the cerebral border zone regions. The purpose of our study, therefore, was to prospectively determine whether a longer ATT, from the proximal vasculature in the neck toward the distal end branches of the intracranial arteries, can be utilized to identify the cerebral border zone regions.


   Materials and Methods
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 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
 Advances in Knowledge
Implication for Patient Care...
 References
 
The institutional review board of the Singapore National Neuroscience Institute approved this study. Informed consent was obtained from all participants. Philips Medical Systems (Best, the Netherlands) provided equipment support. The authors had control of the data and the information submitted for publication.

Volunteers
We included 15 healthy volunteers (age range, 22–34 years; nine men, six women)—MR technicians and PhD students recruited from our department—who did not report a history of neurologic disease or vascular pathology to the authors (J.H., E.T.P.). All 15 volunteers were imaged in baseline conditions, without a cerebrovascular reactivity measurement. Cerebrovascular reactivity (30-second breath-hold challenge) measurements (see below) were also obtained in a subgroup of six randomly selected volunteers (age range, 22–34 years; four men, two women).

MR Imaging
All baseline MR imaging was performed with a 3.0-T imager (Intera; Philips Medical Systems). All images were acquired by using the quadrature body coil as a transmit coil and an eight-element phased-array head coil as a receiving coil. For ASL perfusion MR imaging, we used the recently developed quantitative signal targeting by alternating radiofrequency pulses labeling of arterial regions, or QUASAR, pulse sequence (16). This sequence is capable of acquiring images at multiple inversion times (TIs) after labeling of the arterial water protons with a high temporal resolution and features a bolus saturation scheme for clear definition of the arterial blood bolus (16). The readout is performed by using conventional multisection single-shot gradient-echo echo-planar imaging with a small flip angle. This resulted in the following imaging parameters for the QUASAR pulse sequence: four sections; section thickness, 7 mm; ascending section order; section gap, 2 mm; matrix, 64 x 64; field of view, 240 mm; flip angle, 30°; repetition time msec/echo time msec, 4000/23; first TI, 50 msec; {Delta}TI, 200 msec; bolus length (start time for bolus saturation), 1050 msec; stop time for bolus saturation, 2250 msec; number of acquisition time points (ie, TIs), 18; single-shot echo-planar imaging; sensitivity encoding factor, 3.0; inversion slab width, 150 mm; and section or inversion gap, 30 mm. Forty pairs of control and labeled acquisitions were performed, for a total imaging time of 5 minutes 20 seconds. MR imaging was repeated twice, with and without additional "crusher" or bipolar gradient pulses (velocity encoding, 3 cm/sec), allowing elimination of the signal from fast-moving spins. In addition to these baseline MR imaging examinations, cerebrovascular reactivity was tested in the subgroup of six volunteers with a 30-second breath-hold challenge. During a 5-minute QUASAR sequence, three breath holds of 30 seconds were performed. As with baseline MR imaging, the images were acquired twice, with and without additional "crusher" gradients (velocity encoding, 3 cm/sec).

To show the difference between the nonselective ASL perfusion MR imaging method for the detection of regional differences in ATT and a previously described selective ASL perfusion MR imaging method for flow territory measurements, examples of both types of images were acquired (17). Selective ASL perfusion MR imaging was performed in the left and right internal carotid arteries and the posterior circulation.

Postprocessing
All images were exported to a personal computer operating with Windows and Interactive Data Language, version 6.1 (ITT Visual Information Solutions, Boulder, Colo). The labeled and nonlabeled ASL images were first subtracted to produce {Delta}M images (where M is magnetization). The arterial equilibrium magnetization, or M0,a, was measured in a single voxel within the sagittal sinus in the most superior section to ensure inflow of nonsaturated venous blood. The relaxation of blood was set to 1650 msec, and the labeling efficiency was assumed to be 100% ({alpha} = 1.0) (18). From the {Delta}M images, the ATT, CBF, and arterial blood volume (ABV) were calculated by using a model-independent method (16).

ATT Assessment
As part of CBF quantification, the onset of the arterial input function (AIF)—also called the ATT—needs to be assessed. For measuring these onsets, an edge-detection algorithm originally proposed by Canny (19) was used. In its actual implementation, the perfusion-weighted signal {Delta}M(t) (where t is time) is first convolved with a gaussian function and is subsequently differentiated by means of a standard Sobel kernel. The rising edge can be estimated on the basis of the maxima of the derivative. The raw ATT maps were subsequently used to demonstrate regional difference in ATT. To increase the signal-to-noise ratio and enable clear visualization of the regions with long ATT, 8-mm spatial filtering was performed on the raw ATT maps. Software (Interactive Data Language, version 6.1) was used to perform the spatial filtering. Thresholding of the filtered ATT data was performed in which the cerebral regions with a long ATT were defined on the basis of an ATT of more than 500 msec. Gray matter regions with a relatively short ATT were defined for comparison as having an ATT of less than 400 msec. Maps of the apparent effective relaxation rate (R1app,eff), which includes effects from flow and low flip angle perturbation, were calculated on the basis of the saturation recovery curve of the ASL control images. Gray matter segmentation was based on histogram R1app,eff values of the tissue (R1app,eff < 1200 msec–1 = gray matter and R1app,eff > 1200 msec–1 = white matter). Because of the low signal-to-noise values in the white matter, only gray matter ATT segmentation and gray matter quantitative region of interest (ROI) analysis were performed. ROIs for anterior and posterior border zone regions were selected according to the ATT mask and a gray-matter mask. ATT measurements and ROI placement were performed by one of the authors (E.T.P., with 4 years of experience in brain MR imaging). ROIs (size range, 1–2 cm2) were placed in the middle of the area with ATT values greater than 500 msec. On the basis of these ROIs, ATT, CBF, and ABV were calculated by using the nonfiltered data. With the ASL technique used in our study, we measured ABV rather than total cerebral blood volume (20).

Deconvolution
The deconvolution procedures were performed by one of the authors (E.T.P.). As described previously in detail (16), we obtain the AIF as measured in a voxel filled with arterial blood if we multiply c(t) by the magnetization difference 2·M0,a. AIF(t) = (2·M0,a)·c(t), where M0,a is the equilibrium magnetization in a blood-filled voxel and c(t) is the delivery function or fractional AIF. A deconvolution of the measured perfusion-weighted signal intensity–time curve {Delta}M(t) by the AIF provides the residue function multiplied by the relaxation function and the perfusion rate: f·R(t – {tau}) = f·r(t – {tau})·m(t – {tau}), where f is the perfusion value, R is the overall residue function, and r(t – {tau}) is the residue function that describes the fraction of labeled spins arriving at a voxel at time {tau} that still remain within the voxel at time t. The magnetization relaxation term m(t – {tau}) quantifies the longitudinal magnetization fraction of labeled spins arriving at the voxel at time {tau} that remain at time t. By definition, the R(t – {tau}) is a positive decreasing function, with R(0) = 1, and the flow f can be obtained from the maximum of R with no other assumption needed. The only remaining unknown is the AIF itself. The estimation of the residue function R(t – {tau}) and f was performed with a block circulant singular value decomposition deconvolution method (21). Subtraction of the "crushed" ASL data from the "noncrushed" ASL data was performed to estimate the ABV and to obtain the shape of the AIF. Localized AIFs were selected on the basis of ABV. Typically, voxels with more than 1.2% ABV were selected to avoid ill-defined AIFs (16). The euclidean distance was then calculated between any voxel and the nearest valid AIF. If more than one AIF was found at the same distance, the averaged AIF was then used to further calculate the perfusion (16).

Statistical Analysis
In 15 volunteers, the differences in ATT, CBF, and ABV between the anterior border zone, posterior border zone, and non–border zone gray matter were analyzed with a two-tailed paired t test. In the subgroup of six volunteers, the percentage change in CBF (percentage vasomotor reactivity) during breath holding compared with during the resting condition was analyzed with a two-tailed paired t test. Software (SPSS for Windows, version 10.0.7; SPSS, Chicago, Ill) was used for statistical analysis. Results are presented as means ± standard errors of the mean, and P < .05 was considered to indicate a statistically significant difference.


   Results
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 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
Discussion
 Advances in Knowledge
Implication for Patient Care...
 References
 
Arrival of Magnetic Label
With images acquired at a series of time points, a delayed arrival of the magnetic label is present in the anterior and posterior aspects of the MCA flow territory (Fig 1). At longer delay times (>2000 msec), the signal diminishes, mainly because of the T1 decay of the magnetic label.


Figure 1
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  		Figure 1: Fifteen transverse images (4000/23; flip angle, 30°: 7-mm sections) show temporal evolution of the arrival of labeled arterial water at the brain tissue at a series of time points after magnetic labeling of the proximal vasculature, from 0 msec (upper left corner) to 2900 msec (lower right corner). Color represents arrival of labeled blood at brain tissue. Amount of MR labeling signal from high to low is represented as, respectively, yellow, red, green, and blue. Longer ATTs can be appreciated in anterior and posterior aspects of the MCA flow territory. Light gray areas in background correspond to brain regions with the longest ATTs (>500 msec).

 
ATT Images
On the filtered ATT maps (Fig 2), an increase in ATT was observed at the location of the anterior border zone from the frontal horn of the lateral ventricle to the frontal cortex and the location of the posterior border zone from the occipital horn of the lateral ventricle to the parietooccipital cortex. The filtered ATT maps for one of the middle sections in all 15 volunteers showed the intersubject variability in the extent of the regions with short and long ATTs (Fig 3).


Figure 2
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  		Figure 2: Four typical transverse sections in one volunteer (4000/23; flip angle, 30°; 7-mm sections). Images on right are most-apical images. A, Anatomic images derived from saturation recovery curve of ASL control images (R1app,eff images). B, ATT images were calculated in seconds with edge detection of the upslope of the "noncrushed" ASL data. C, Filtered ATT maps were used to clearly visualize the regions with long ATTs. D, Gray matter segmentation of the ATT maps was performed on the basis of the R1app,eff values of the tissue (R1app,eff < 1200 msec–1 = gray matter). E, CBF maps and, F, ABV maps were calculated with the deconvolution method. G, With the selective ASL MR imaging method, flow territory imaging was performed in the right (red) and left (green) internal carotid arteries and in the posterior circulation (blue).

 

Figure 3
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  		Figure 3: Transverse filtered ATT maps (4000/23; flip angle, 30°; 7-mm sections) for all 15 volunteers for one of the middle sections show anteriorly and posteriorly located border zone regions with long ATTs in all volunteers. Intersubject variability in the extent of the regions with long ATTs is observed.

 
Quantification of CBF and ABV
The CBF of 61.7 mL/100 g/min ± 3.3 in the anterior border zone and 62.4 mL/100 g/min ± 3.2 in the posterior border zone was significantly lower than the CBF in the non–border zone gray matter of 78.3 mL/100 g/min ± 3.2 (P < .001) (Table 1). The ABV was decreased in the anterior border zone (0.88 mL/100 g ± 0.07) and the posterior border zone (1.19 mL/100 g ± 0.11) compared with that in the non–border zone gray matter (1.64 mL/100 g ± 0.08, P < .001) (Table 1).


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  		Quantification of ATT, CBF, and ABV in 15 Volunteers

 
ROI Time Curves
A delayed arrival of the magnetic label in the border zone areas was found in the signal intensity–versus-time curves for a typical subject, with the upslope of the signal at 700–800 msec for the anterior and posterior border zone areas, compared with 400 msec for the non–border zone gray matter area (Fig 4). Similar curves were observed for all 15 volunteers.


Figure 4
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  		Figure 4: Signal intensity–versus-time curves for typical subject of ROIs placed in anterior border zone (red), area of other gray matter (blue), and posterior border zone (green) on transverse filtered ATT map (upper right corner). A delayed arrival of the magnetic label in the border zone areas can be appreciated in the upslope of the signal at 700–800 msec for the anterior and posterior border zone areas compared with the upslope of the signal at 400 msec for the non–border zone gray matter area. Similar curves were observed for all 15 volunteers. M = magnetization.

 
Reactivity Analysis
The average increase in CBF during the breath-hold challenge (in six volunteers) was 60% ± 15 for the anterior border zone, 87% ± 20 for the posterior border zone, and 79% ± 16 for the non–border zone gray matter (P = not significant) (Fig 5).


Figure 5
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  		Figure 5: Transverse, A, ATT and, B, CBF images (4000/23; flip angle, 30°; 7-mm sections) in resting conditions (left) and during a breath-hold challenge (right) for a typical subject show a decrease in ATT and an increase in CBF during the breath-hold challenge.

 

   Discussion
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 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
Advances in Knowledge
Implication for Patient Care...
 References
 
On the basis of regional differences in ATT, the ASL perfusion MR imaging method at multiple delay times depicted areas with increased travel distance. The locations of the brain regions with the longest ATTs are in accordance with the anterior and posterior cerebral border zones, which extend, respectively, from the frontal and occipital horns of the lateral ventricle to the frontal and parietooccipital cortices (1). Furthermore, significantly lower CBF, lower ABV, and preserved cerebrovascular reactivity values were measured in the brain regions with long ATTs.

Little attention has been paid to the border zones as physiologic phenomena, in the sense that the border zones may show different hemodynamic characteristics even in the absence of cerebrovascular disease. In our study, we used the longer travel distance to reach the distal end branches of the intracranial arteries, resulting in longer ATTs, to show the potential location of the cerebral border zone regions in healthy subjects. In patients with symptomatic cerebrovascular disease, the location of an infarct in either a cerebral border zone region or a territory region may add information about the etiology of the infarction (22). However, anatomic information alone is not sufficient to define the cerebral border zone regions (23). Infarcts in the cerebral border zones are thought to derive from hemodynamic causes, although results of recent studies (1,23) have demonstrated that, as with cortical border zone infarctions, thromboembolism may be the major underlying pathophysiologic mechanism. Recent concepts suggest a combined contribution of both impaired hemodynamics and thromboembolism to ischemia in cortical and deep white matter border zone regions (23). Additional hemodynamic information not provided by anatomic scans may add information about the potential cause of an infarct, which may have implications for therapy and the future development of therapeutic options (24).

We demonstrated the presence of a frontal cortical gray matter area with increased ATT that corresponds to the expected anatomic location of the anterior border zone at the frontal cortex (1,23). Furthermore, we demonstrated the presence of a parietooccipital cortical gray matter area with increased ATT that corresponds to the expected anatomic location of the posterior border zone at the parietooccipital cortex (1,23). With respect to the posterior circulation, a 470-msec longer ATT was observed for the occipital cortex as a whole. A previous article reported a 500-msec longer ATT in the occipital lobe than in the parietal lobes (25). A possible explanation for the increased ATT of the posterior circulation is differences in arterial geometry. The blood flowing downstream through the posterior cerebral arteries travels a long distance parallel to the imaging sections to reach the cortex of the occipital lobe, while the blood in the MCAs travels directly superior to the parietal lobe.

The observed decrease in CBF and ABV in the border zone regions was unexpected and may indicate vulnerability of the border zone regions to decreased perfusion pressure in combination with thromboembolism in the proximal vasculature (22,26). However, no significant regional differences in CBF reactivity were detected between border zone and non–border zone gray matter regions. The decrease in ABV may represent a lower arterial vessel density with either fewer or smaller-diameter arteries in these regions (27). With preserved CBF reactivity, we believe that the regions with increased ATT are not hemodynamically impaired in the absence of cerebrovascular disease. However, in accordance with general fluid dynamics, a pressure decrease upstream in a branching network will naturally cause the largest pressure decrease at the most distal locations.

In our study we chose to favor the fastest arriving spins by measuring the upslope of the signal. The advantage of this method over alternative measures, such as time-to-peak or center of mass of the bolus curve, is the better representation of distance rather than the potential dispersion due to resistance. The use of a single fixed ATT is arguable because, by our own definition, differences in the travel distance define the cerebral border zones. Still, the use of different thresholds for different sections may only be necessary for the highest sections because the mean ATTs from the bottom to the top sections are comparable, except for the sections most distally located with respect to the labeling slab (28). It was believed that a single threshold in the group of subjects analyzed in our study was sufficient to prove the concept of the method.

The regional heterogeneity in ATT, as presented in our study with a nonselective ASL perfusion MR imaging method at a series of delay times, is conceptually different from selective ASL perfusion MR imaging methods with labeling of individual brain feeding arteries. Results of our study suggest that regions with long ATTs are located most peripheral to the supplying arteries and correspond to the cerebral border zones between end branches of the cerebral arteries. In contrast to our technique, selective ASL perfusion MR imaging techniques show the flow territories supplied by individual brain feeding arteries (29). Although in vivo and postmortem studies of flow territories have been conducted, no studies other than those of infarct classification have been performed to demonstrate the cerebral border zone regions because of the absence of imaging or reference-standard methods of defining the cerebral border zone regions.

In our study, the label within the vasculature was used for assessment of the AIF and ATT. Still, a potential limitation of quantitative ASL CBF measurements is the presence of label within the vasculature (30,31). Label within the larger vessels may cause overestimation of the CBF in the non–border zone gray matter regions relative to the CBF in the border zone regions, which have smaller cortical vessels. To avoid such an overestimation, we used vascular "crusher" gradients to eliminate the contribution of large-vessel signal to the CBF quantification. Although care was taken to avoid intravascular signal by using bipolar gradients, intravascular contributions of small arteries may still have contributed to the signal. Most likely these small arteries will be present throughout the brain parenchyma; however, we cannot exclude that the regional differences in ATT, ABV, and CBF were partly caused by a regional heterogeneity in vessel diameters in addition to a real difference in ATT, ABV, and CBF. Potentially, long ATTs in specific brain regions could result in an underestimation of CBF. However, with a T1 decay of 1650 msec at 3.0 T (32) and the use of multiple inversion times to quantify the CBF and an upper limit of the regional ATT of 1010 msec, it is unlikely that prolonged ATTs would have resulted in an underestimation of the CBF in our study.

In patients with severe cerebrovascular disease with ATTs greater than 2000–2600 msec, an underestimation of CBF in regions with long ATTs may occur. In contrast to the current study, previous studies with ASL at multiple inversion times did not reveal regional heterogeneity in ATT (13,14,25,33). However, those studies were not specifically designed to reveal these regional differences. Still, some regional heterogeneity can be retrospectively appreciated in the ATT images presented in the literature (25,28). In our study, we used an ATT cutoff of 500 msec, which was based on the current ASL imaging parameters, to separate long ATTs. It should be noted that with different imaging parameters—for instance, a larger gap between the labeling slab and the imaging sections—the optimal ATT cutoff value will change. With the typically low ASL perfusion MR imaging signal in the white matter, a limitation of our technique was that only ATT segmentation and ROI analysis of gray matter CBF and ABV could be performed. However, at the current field strength of 3.0 T, we could appreciate that the white matter, especially the supraventricular deep white matter, had a longer ATT than the gray matter. The advantages of the current ASL perfusion MR imaging method are the combination of noninvasiveness and the possibility of ATT, CBF, and ABV quantification (16).

In conclusion, with the ASL perfusion MR imaging method at multiple delay times, we demonstrated both regional heterogeneity and intersubject variability in ATTs. The advantages of the ASL perfusion MR imaging approach are noninvasiveness, a relatively short imaging time of 5 minutes, and combined assessment of regional heterogeneity in ATT and quantification of regional CBF and ABV (16). Added to current neuroimaging protocols (34–37), the presented method may enable better understanding of regional cerebral hemodynamics, both in health and disease.



   Advances in Knowledge
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
 Advances in Knowledge
Implication for Patient Care...
 References
 
  • The locations of brain regions with long arterial transit times (ATTs) are in accordance with the anterior and posterior border zone regions, which extend, respectively, from the frontal and occipital horns of the lateral ventricle to the frontal and parietooccipital cortices.
  • Lower cerebral blood flow and arterial blood volume values were measured in gray matter regions with long ATTs than in gray matter regions with short ATTs.
  • No regional differences were found in cerebrovascular reactivity between regions with short and long ATTs.

 


   Implication for Patient Care
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
 Advances in Knowledge
Implication for Patient Care...
References
 
  • In patients with cerebrovascular disease, the location of an infarct in either a cerebral border zone region or a territory region may add information about the etiology of the infarction and may have potential implications for therapy.

 


   FOOTNOTES
 

Abbreviations: ABV = arterial blood volume • AIF = arterial input function • ASL = arterial spin labeling • ATT = arterial transit time • CBF = cerebral blood flow • MCA = middle cerebral artery • ROI = region of interest

Published online before print

See Materials and Methods for pertinent disclosures.

Author contributions: Guarantors of integrity of entire study, J.H., E.T.P., X.G.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; literature research, J.H., P.J.v.L.; experimental studies, J.H., E.T.P.; statistical analysis, J.H., E.T.P.; and manuscript editing, all authors


   References
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
 Advances in Knowledge
Implication for Patient Care...
 References
 

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