Open Journal of Medical Imaging, 2012, 2, 1-9 Published Online March 2012 (
Qualitative and Quantitative Perfusion Parameters
Determined by 3D Single-Shot GRASE ASL MR Imaging
Claus Kiefer*, Frauke Kellner-Weldon, Marwan El-Koussy, Martinus Hauf, Gerhard Schroth
Support Center for Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology,
University of Bern (Inselspital), Bern, Switzerland
Email: {*claus.kiefer, frauke.kellner-weldon, marwan.el-koussy, martinus.hauf, gerhard.schroth}
Received October 27, 2011; revised December 2, 2011; accepted December 13, 2011
Rationale and Objectives: A particular arterial spin (ASL) labeling technique, called 3D-single-shot GRASE ASL is
discussed with respect to the ability and limits of quantifying perfusion parameters. Materials and Methods: The tech-
nique enables the acquisition of perfusion weighted signal at multiple delay times (TI) in one scan. The readout part is a
gradient and spin-echo combination (GRASE) that uses switched gradient rephrasing of signals to produce several times
as many signals as turbo-spin-echo, which translates into faster imaging time and higher signal-to-noise ratio (SNR) per
imaging time. The technique provides the possibility for model based quantification of cerebral blood flow and the de-
termination of the bolus arrival information without use of contrast agent and thus the characterization and determina-
tion of regions that are supported by collaterals. Results: Whereas for a quantification of the permeability using ASL
the SNR is not high enough, at least qualitative permeability maps can be determined, if an optimal homogenous SNR
was guaranteed. This was accomplished in brain regions with a high blood supply, typically given in tumors, and by
using a correction for coil sensitivity at the highest possible additional scaling. Conclusion: The single-shot 3D
GRASE ASL can provide information about the principal blood supply, the transit delay of the blood flow due to a
stenosis or collaterals and a qualitative measure of the permeability.
Keywords: ASL; Perfusion; GRASE; Model; Permeability
1. Introduction
Arterial spin-labeling (ASL) was introduced [1] as a
noninvasive method capable of assessing cerebral perfu-
sion and the temporal dynamics of arterial blood inflow.
Whereas real continuous labeling has drawbacks be-
cause it requires near continuous wave RF transmit capa-
bility (dual-coil) that is often not available on imagers or
overloads the amplifier (duty cycle limits), the pulsed
technique can be used on clinical scanners. In pulsed ver-
sions an instantaneous RF pulse (several milliseconds) is
used to tag a slab of arterial blood. The implementation is
straightforward and provides a tagging efficiency above
97% [2], which has been shown to be largely insensitive
to variations in flow velocity. In comparison, the paper
on pCASL, by [3], actually showed highly variable re-
sults (sometimes positive, sometimes negative) mostly
due to incorrect setting of the phase. Computer simula-
tions of pCASL predict an optimal tagging efficiency of
85% for arterial flow velocities in the limited range from
10 to 60 cm/s. It is theoretically and experimentally
demonstrated in Wu’s paper that the tagging efficiency
of pCASL is dependent technically upon the resonance
offset, the flip angle of the RF pulse train, the phase and
physiologically upon the blood flow velocity. The phase
is a function of the slice selective gradient, the resonance
offset of the tagging/control plane, and the flow velocity
(a minor contribution compared to the former two). A
large range in inversion efficiency measured across the
subject group (50% - 76%) indicates that the velocity
dependence of the amplitude modulated control effi-
ciency may introduce additional variability into the per-
fusion calculations if not properly taken into account.
Swirling turbulent flow that causes multiple crossings
and then return across the labeling plane could certainly
be a problem. For measurement of cerebral perfusion,
ASL of the blood flowing through the carotid- and verte-
bral arteries is typically performed at a axial plane, which
is localized in the neck, just below the skull base. Turbu-
lent blood due to arteriosclerosis or a stenosis of the ver-
tebral or internal carotid artery at this level may signify-
cantly change the efficiency of the labeling. e.g., blood
flow through a high grade stenosis is increased, resulting
in a decrease of the labeling effect. The main pitfall in
labeling blood inflow at the skull base and measuring the
*Corresponding author.
opyright © 2012 SciRes. OJMI
effect distally in the brain are collaterals. The transit time
from the labeling pane to the brain is an essential pa-
rameter in determining the sensitivity of this method and
for the quantification of perfusion. If the brain areas are
supplied by collateral arteries from the opposite side or
via branches of the external carotid artery, the perfusion
may be sufficient, however, the transit time can be de-
layed resulting in an underestimation of the regional
cerebral blood flow, if time between labeling and meas-
urement is not long enough to detect also the delayed
collateral inflow. The CASL signal change is predicted to
be independent of the transit time if a sufficiently long
post-labeling delay (PLD) is used [4,5]. The CBF values,
measured by CASL are perfectly independent of transit
time to the vascular compartment, for both gray and
white matter as long as the transit time is shorter than the
PLD. However, in case of a stenosis, this delay can be
very long (in the range of more than two seconds) result-
ing in a signal loss due to T1 relaxation, which has to be
compensated for by an increased number of acquisitions.
In order to acquire full brain volumes, the acquisition
time is in the range of 7 minutes for 120 volumes using
pCASL. The quantification of CBF in brain areas, sup-
plied by stenotic arteries and via collaterals however ne-
cessitates at least doubling the SNR (see Figure 1) and
thus four times the number of acquisitions (in the exam-
ple 480 acquisitions or 28 minutes).
Figure 1. Effects of T1-relaxation on the ASL difference
signal for high (black curve, 90 ml/100 g/min) and low flow
(green curve, 30 ml/100 g/min) for time period of 4 seconds.
To overcome the problems with the labeling efficiency,
the labeling slice and the transit times, a particular arte-
rial spin labeling technique, called single-shot 3D-GRASE
ASL, was recently introduced [6], that enables the acqui-
sition of perfusion weighted signal at multiple delay
times (TI) in one scan. One of the major issues in quanti-
tative perfusion measurements using ASL, which might
result in inaccurate perfusion values, is the contamination
of the microvascular perfusion with different arrival times
for the labeled blood in different regions. This problem
can be solved by measuring the hemodynamic curve, ac-
quired at several delay times, and evaluating it in the
context of sophisticated perfusion models. Knowledge of
the regional arrival times of arterial blood furthermore
provide additional information to characterize the collat-
eral flow and may potentially be used to identify hemo-
dynamically impaired regions. The most common method
to measure arrival times of blood is dynamic sampling of
an injected bolus of contrast agent. However, due to the
current concerns regarding contrast use in patients with
poor renal function and ionizing radiation, an alternative
without detrimental effects would be of great benefit.
In order to quantify the perfusion parameters a two-
compartment model was used which is described in de-
tail in reference [7].
In this work the GRASE ASL technique is discussed
with respect to the ability and limits of quantifying per-
fusion parameters, especially the determination of the
permeability of vessels.
2. Materials and Methods
GRASE (gradient and spin echo) is an imaging technique,
that combines the essential features of turbo-spin echo
(TSE) and echo-planar imaging (EPI) methods. It uses a
train of refocusing 180˚ RF pulses as in TSE, placing
additional gradient recalled echoes for each spin echo of
the readout. GRASE therefore provides the option to
influence the speed by setting a turbo factor as well as an
EPI factor—so the gain in time as compared to conven-
tional spin echo is the TSE turbo factor times the EPI
factor. As a trade-off some image blurring and drop in
SNR occur due to the longer multiple echo readout and
modulations in signal intensity across k-space due to
signal decay. Separately, both TSE and EPI experience
these effects in subtly different ways. In EPI techniques,
the strength of the successive gradient echoes can decay
rapidly due to effects, causing the later echoes to
have a significantly reduced SNR as compared to the
earlier echoes. However, in GRASE, the 180˚ RF pulses
help to refocus intermittently the echoes to a maximum
within the decay envelope. According to Guenther et al.
the mean SNR of 3D-GRASE in gray matter therefore is
13.0 3.5 and 4.2 0.9 in white matter, for a 2D-EPI 4.7
1.3 and 1.3 0.2 respectively (4.5 mm partition/slice
Copyright © 2012 SciRes. OJMI
thickness). The refocusing also helps to reduce the arti-
facts related to magnetic susceptibility heterogeneity that
are so common in EPI imaging. But, in the case of fixed
metallic hardware for example, GRASE will clearly have
related artifacts that would be less obvious in similar
TSE scans.
In GRASE, the combination of spin echoes and gradi-
ent echoes at differing times leads to modulation in
measured signal strength over the multiple-echo readout
time, and thus imposes signal modulations over k-space.
Typically, the stronger spin echoes are used to fill the
centre of k-space and the weaker gradient echoes are
used in the periphery. This distribution tends to empha-
size overall SNR with an image similar to TSE, at the
expense of some fine detail as compared to a TSE scan
with similar turbo factor. However, some authors have
demonstrated that single shot GRASE can have improved
spatial resolution when compared with single shot EPI. A
large advantage of single-shot 3D techniques is that the
whole image volume is acquired at the same inflow time
TI. All partitions acquired throughout the echo train have
the same amount of perfusion weighting since there is
only one excitation pulse, which specifies the actual TI.
Aliasing artifacts in the 3D-encoding direction are re-
duced tremendously after application of the modulated
saturation pulses since the signal outside the imaging
slab is nulled directly before readout.
The ASL preparation part, where the labeling takes
places, is associated with problems concerning the sup-
pression of stationary tissue and high inflow from great
vessels. The latter could be accomplished by using small
diffusion gradients, but the required strength of these
gradients is difficult to estimate and time consuming to
determine it during the examination, so we did not make
use of it. Background suppression techniques [8,9] were
suggested using multiple nonselective inversion pulses to
null the signal of the stationary tissue while leaving the
signal of the labeled blood untouched (for perfect pulses).
Proposed technique uses modulated saturation pulses,
which result in two saturation bands on both sides of the
imaging slab. This suppresses intravascular blood flow-
ing into the imaging slab from not only one but also from
both sides, thus also reducing the signal of most venous
Because GRASE-ASL is based on the FAIR labeling
scheme [10], there are no problems with magnetization
transfer effects. This labeling method is based on the
subtraction of images acquired alternately using a slice-
selective and a non-selective inversion recovery sequence,
as opposed to techniques, in which blood is labeled only
proximally to the measured slice in the labeling phase,
and not in the control phase of the sequence. The mag-
netization transfer (MT) artifacts may then occur in the
perfusion-weighted images arising from macromolecular
spins in the imaged slice that have been excited by the
labeling radiofrequency (RF) pulse.
RF pulses or pulse trains used for tagging have imper-
fect slice profiles and can contaminate the magnetization
in the ROI. These effects can be on the order of a few
percent of the static tissue magnetization, which is the
same order as the ASL signal. Pulsed ASL techniques
typically use hyperbolic secant (sech) or similar adiabatic
pulses for efficient inversion that is relatively insensitive
to both B1 inhomogeneity and resonance offsets. Modi-
fied sech pulses such as frequency-offset-corrected in-
version (FOCI) pulses were used in the sequence ac-
cording to the proposals of Guenther et al. [6] For an
optimized parameter set the FOCI pulses were calculated
and the inversion profile of the longitudinal magnetiza-
tion was simulated in Matlab using the Bloch equations.
Perfusion parameters CBF and permeability surface
area product (PS) were estimated by fitting a FAIR
model without backflow [7] to the GRASE ASL differ-
ence data series. This model corrects for the assumption
that the capillary wall has infinite permeability to water.
The model incorporates an extravascular and a blood
compartment with the permeability surface area product
(PS) of the capillary wall characterizing the passage of
water between the compartments. The model predicts
that labeled spins spend longer in the blood compartment
before exchange. Permeability of the capillary wall to
water is measured in terms of PS, the permeability (P)
surface area (S) product of brain capillaries to water, per
volume of tissue. It has been measured by a number of
different methods in a number of species. Published val-
ues in whole human brain vary from 0.9 - 1.7 min–1 with
a mean value of 1.2 min–1 [11]. A blood water compart-
ment and an extravascular water compartment, each with
corresponding volumes and longitudinal relaxation times,
are separated by semi-permeable endothelium. An extra
component was introduced to the differential equations
of the model, which accounts for labeled water crossing
between the two compartments through the permeable
capillary wall. Like T1, the values for blood and ex-
travascular water are also different. The difference signal
will change as water moves between the two compart-
ments. R1 = 1/T1, which is necessary for the model, was
estimated from the same ASL dataset. The BAT-map,
delivered by 3D-single-shot GRASE, was used as a mea-
sure for the transit time in order to correct for a distribu-
tion of transit times centered on MTT. The PS parameter
maps finally were qualitatively compared with the ap-
parent k21 parameter maps of the DSC based evaluation
using the Nordic software [12] which includes the leak-
age correction algorithm of reference [13].
The sequence was delivered with a special feature, an
option called additional scaling that allows to influence
the image intensity and SNR in addition to the scaling
Copyright © 2012 SciRes. OJMI
Copyright © 2012 SciRes. OJMI
(beta, mu, FOCI-factor (flat top value of the gradient
modulation), flip, phase correction factor (slab shift resp.
offcenter frequency shift used to determine the rf-fre-
quency) were: 900, 24, 1.0, 500, 10.000.
done by the software of the manufacturer. This option in
some cases had to be set up to 70% to set the image in-
tensity within the brain matter to values near 3500 (total
range is 0.4095).
The GRASE-ASL was applied to patients with a ten-
torium-meningeoma and a high grade stenosis (ICA left).
GRASE and DSC sequences were performed on a 3
Tesla scanner, 32-channel receiver coil, additional scal-
ing 50% (maximum intensity value 3500). Postprocess-
ing of GRASE was done with a MATLAB written soft-
ware, the DSC data was evaluated with Nordic-ICE
software (options leakage correction and maps). The se-
quence parameters for the GRASE are as follows: voxel
size: 4.7 × 4.7 × 4.0 mm, matrix 64 × 64, TR 3200 ms,
TE 12.8 ms, Averages 2, Slice partial Fourier 6/8, PAT
mode None, Sat. region Thickness 120 mm, Phase over-
sampling 6%, Slice oversampling 15.4%, Slices per slab
26, FoV read 300 mm, FoV phase 50.0%, Slice thickness
4.0 mm, Dimension 3D, Reordering Centric, Bandwidth
2790 Hz/Px, Echo spacing 0.5 ms, Turbo factor 23, EPI
factor 17, Bolus length 4000 ms. The bandwidth was de-
termined by minimizing the artifacts in the difference
images measuring a stationary spherical water phantom.
The sequence currently just allows to select 50% or 68%
FoV phase which is related to 6 or 9 minutes measure-
ment time. With regard to the shorter time we choose
50% and accepted the infolding artifacts.
3. Results
The problem of a lack of SNR and T1-relaxtion is illus-
trated in a simulation of perfusion using flow modified
Bloch equations: T1 for tissue and labeled blood at 3T
were set to 1200 ms (gray matter) and 1600 ms respec-
tively (Figure 1). The maximal difference signal (con-
trol-label) with respect to the normalized initial signal is
only 0.015 (1.5% of the entire signal per voxel) and is
further reduced to 0.005 for high flow (90 ml/100 g/min)
and 0.003 for low flow (30 ml/100 g/min) after a delay of
4 sec. Especially the last value for low flow character-
izes the problem associated with the ASL based quanti-
fication of CBF with respect to the SNR within regions
with a pathological reduction of flow, as in ischemic or
stenotic tissue areas.
The gain in speed in our GRASE sequence is roughly
TSEturbofactorEPIfactor23 17391
which allows to acquire the N = 14 volumes (26 slices) in
a time of 6 minutes. The 3D-infolding artifacts only
concern the upper two slices whereas the right-left in-
folding due to the small FOV phase (50%) was a prob-
lem in about four of forty cases. The FOCI-pulses and
the inversion profile for a slab shift of 100 mm, shown in
Figure 2, indicate a nearly perfect rectangular shape. The
fitting results in Figure 3 demonstrate that the hemody-
namic behavior can be reproduced by the two-compart-
ment perfusion model—the correlation of measured and
fitted curves is better than 0.95. The related CBF and BAT
maps for the meningeoma patient are shown in Figures 4.
For the background suppression scheme we used
T1opt = 700 ms and delay = 100 ms (TI = inflow time –
delay, inflow time = start + (len – 1) × inc = 2800 ms (start
= 200, inc = 200, len = 14)). The two inversion pulses then
are at times τ1 = 1,265,860 μs, τ2 = 2,363,490 μs.
For the saturation mode a series of sinc pulses with a
flip angle 90 degree, duration T = 10240 μs, was used the
saturation was performed before and after labeling.
The parameter for the FOCI inversion rf pulse shapes
(a) (b)
Figure 2. Off-resonance FOCI-pulse for a slab shift of 100 mm (a) gradient, rf-amplitude, rf-phase; and (b) inversion profile.
Figure 3. For four different representative voxels the model based calculated curves (red dashed curves) are compared to the
measured curves (blue solid curves): the correlation of the measured and fitted curves is better than 0.95.
Figure 4. Integrated GRASE inflow curves (apparent CBV), CBF, anatomy, BAT maps for a patient with a meningeoma
(before embolization).
Copyright © 2012 SciRes. OJMI
Copyright © 2012 SciRes. OJMI
strated in Figure 8: within the stenotic brain hemisphere
the supply by a vessel with delayed blood arrival but
with a relatively high CBF value between 50 and 60 ml/
100 g/min is presented.
The CBF values in white matter vary between 10 and 20
ml/100 g/min which is in good agreement with the work
of Pohmann et al. [14], where 15.6 3.2 ml/100 g/min in
the left and 15.2 4.8 ml/100 g/min in the right hemi-
spheric white matter were reported. In Figure 5 the dif-
ference images are shown for TI ranging from 200 ms to
2800 ms in steps of 200 ms. Due to a high blood supply
of the meningeoma, and thus a high contribution of the
CBF related signal to the measured signal, the PS pa-
rameter derived from the GRASE data is at least qualita-
tively comparable to the apparent leakage (K21) para-
meter of the DSC evaluation (Figure 6).
It is furthermore interesting to note that the bolus
length doesn’t seem to have a great influence on the CBF
values in gray matter: for bolus lengths b = 2000, 3000
and 4000 ms the (mean/maximum) CBF values in the
cortex were (56/101), (64/104) and (61/103) ml/100 g/
min respectively. This was the reason why we did not
limit the bolus length by saturation pulses but used the
full length of 4 seconds in order to achieve the maximum
SNR for pulsed ASL. In the case of the patient with the
left ICA occlusion (Figure 7) the apparent CBV, the
CBF and BAT maps are in excellent agreement with the
indication and the expectations. In a few voxels within
areas on the stenotic brain hemisphere and the white
matter the CBF values are underestimated (<10 ml/100
g/min) due to a too low SNR. The power of the multiple
delay technique however detecting collaterals is demon-
Figure 5. Dynamic difference maps (one slice, TI = 200,
400, …, 2800 ms) for the patient with the meningeoma (be-
fore embolization).
(a) (b)
Figure 6. Permeability (a) and k21 (b) maps calculated from GRASE-ASL and Nordic (DCE) data sets resp. for the patient
with the meningeoma (before embolization).
Figure 7. Patient with a stenosis (left ICA occlusion): integrated inflow (apparent CBV), CBF, anatomy and bolus arrival.
Figure 8. Patient with the stenosis (see Figure 7): difference signal picked up at the marked position (peak value is equal to
the CBF value).
Copyright © 2012 SciRes. OJMI
Copyright © 2012 SciRes. OJMI
4. Discussion
The 3D-Single-Shot GRASE ASL technique provides the
possibility for model based quantification of perfusion
and the determination of the bolus arrival information
without use of contrast agent. In order to get at least
qualitative leakage (K21) or permeability-surface (PS)-
maps, an optimal homogenous SNR should be guaran-
teed. This is accomplished in brain regions with a high
blood supply, typically given in tumors, and by using a
correction for coil sensitivity at the highest possible ad-
ditional scaling. Whereas for a quantification of the per-
meability using ASL the SNR is not high enough, as
could be demonstrated in [15], at least qualitative per-
meability maps can be determined, if an optimal ho-
mogenous SNR was guaranteed. In summary the single
shot 3D-GRASE ASL technique is a promising perfusion
imaging application for clinical routine. The optimized
FOCI pulses for on/off-resonance inversion guarantee a
non-disturbed magnetization behavior within the regions
of interest and a labeling efficiency of 97% which is
clearly higher than the one of pcasl (85%, velocity and
turbulence dependent). The effects of swirling turbu-
lences and high flow in the labeling slice on the labeling
efficiency are avoided. The model based evaluation of
hemodynamic enables to determine the bolus arrival resp.
transit time parameter and thus the characterization and
determination of regions that are supported by collaterals.
The model based evaluation of hemodynamic also en-
ables to derive qualitative permeability and leakage maps
so that at least ratios of pathological and normal tissue
can be determined.
In order to investigate the effects of different transit
times on the ASL signal a sufficient SNR must be guar-
anteed, especially in case of stenosis. This however ne-
cessitates, as already mentioned, measurement times in
the range of 30 minutes and more, the cooperation of the
patient—both conditions can currently not easily be met
by the patients. Therefore, a direct comparison of pCASL
and GRASE especially with respect to the quantification
of the CBF in stenotic and collateral supplied regions is
projected. Future experiments will be performed on per-
fusion phantoms for this reason, enabling long measure-
ment times and artificial stenoses.
In view of the current parameterization of the GRASE
sequence (TI = 0 - 2800 ms) it becomes clear that the
essential contributions to the perfusion signal are domi-
nated by the arteries and arterioles. Measurements of the
late parenchyma phase (max.TI = 4 - 5 sec) however
would implicate long measurement times as well, which
is not acceptable for the clinical routine and the patients—
in case of three time points (TI = 3500, 4000, 4500 ms)
and N = 16 averages (necessary at 3Tesla) an additional
measurement time of 16 minutes has to be taken into
account. Due to the limitations related to the maximum
duty cycle of the rf amplifier, the measurement time, the
T1-relaxation and the SNR, the ASL technique currently
only depicts restricted clinical insights into perfusion
Beside these aspects there are some problems related
to a right-left infolding of big vessels and/or the cranium.
This depends on the limitation of the FOV (and matrix
size) in the R-L direction (phase) to 50% and the fact that
this parameter is not arbitrarily adjustable—with respect
to the EPI factor (maximum 16 - 17) the next value is
62% - 68% which corresponds to 9 - 10 minutes instead
of 6minutes measurement time.
Concerning the calculation of the images and addi-
tional sequence features, a special option for coil sensi-
tivity correction (prescan-normalize) had to be imple-
mented in the sequence program code to ensure a homo-
geneous SNR over the entire slice. It is also currently
being checked, if the post-processing steps for the addi-
tional scaling and the prescan-normalize (up to now in
this order) can be exchanged to maximize the SNR and
its homogeneity.
In face of all these facts however, the single-shot 3D
GRASE ASL can provide information about the principal
blood supply, the transit delay of the blood flow due to a
stenosis or collaterals and a qualitative measure of the
5. Acknowledgements
This article was financially supported by the Swiss Na-
tional Science Foundation: SPUM Consortium (33CM30-
124114). We also are grateful to Matthias Guenther for
useful discussions.
[1] J. A. Detre, W. G. Zhang, D. A. Roberts, A. C. Silva, D. S.
Williams, D. J. Grandis, A. P. Koretsky and J. S. Leigh, “Tis-
sue Specific Perfusion Imaging Using Arterial Spin La-
beling,” NMR in Biomedicine, Vol. 7, No. 1-2, 1994, pp.
75-82. doi:10.1002/nbm.1940070112
[2] E. C. Wong, R. B. Buxton and L. R. Frank, “A Theoretical
and Experimental Comparison of Continuous and Pulsed
Arterial Spin Labeling Techniques for Quantitative Per-
fusion Imaging,” Magnetic Resonance in Medicine, Vol.
40, No. 3, 1998, pp. 348-355.
[3] W. C. Wu, M. Fernandez-Seara, J. A. Detre, et al. “A
Theoretical and Experimental Investigation of the Tag-
ging Efficiency of Pseudocontinuous Arterial Spin La-
beling,” Magnetic Resonance in Medicine, Vol. 58, No. 5,
2007, pp. 1020-1027. doi:10.1002/mrm.21403
[4] T. Mildner, H. E. Moller, W. Driesel, et al. “Continuous
Arterial Spin Labeling at the Human Common Carotid
Artery: the Influence of Transit Times,” NMR in Bi omedi-
cine, Vol. 18, No. 1, 2005, pp. 19-23.
[5] D. C. Alsop and J. A. Detre, “Reduced Transit Time Sen-
sitivity in Noninvasive Magnetic Resonance Imaging of
Human Cerebral Blood Flow,” Journal of Cerebral Blood
Flow & Metabolism, Vol. 16, No. 6, 1996, pp. 1236-1249.
[6] M. Guenther, K. Oshio and D. A. Feinberg, “Single-Shot
3D Imaging Techniques Improve Arterial Spin Labeling
Perfusion Measurements,” Magnetic Resonance in Medi-
cine, Vol. 54, No. 2, 2005, pp. 491-498.
[7] L. M. Parkes and P. S. Tofts, “Improved Accuracy of Hu-
man Cerebral Blood Perfusion Measurements Using Ar-
terial Spin Labeling: Accounting for Capillary Water
Permeability,” Magnetic Resonance in Medicine, Vol. 48,
No. 1, 2005, pp. 27-41. doi:10.1002/mrm.10180
[8] F. Q. Ye, J. A. Frank, D. R. Weinberger, et al., “Noise
Reduction in 3D Perfusion Imaging by Attenuating the
Static Signal in Arterial Spin Tagging (ASSIST),” Mag-
netic Resonance in Medicine, Vol. 44, No. 1, 2000, pp.
[9] S. Mani, J. Pauly, S. Conolly, et al., “Background Sup-
pression with Multiple Inversion Recovery Nulling: Ap-
plications to Projective Angiography,” Magnetic Reso-
nance in Medicine, Vol. 37, No. 6, 1997, pp. 898-905.
[10] S. G. Kim, “Quantification of Relative Cerebral Blood Flow
Change by Flow-Sensitive Alternating Inversion Recov-
ery (FAIR) Technique: Application to Functionalmapping,”
Magnetic Resonance in Medicine, Vol. 34, No. 3, 1995,
pp. 293-301. doi:10.1002/mrm.1910340303
[11] O. B. Paulson, M. M. Hertz, T. G. Bolwig, et al., “Filtra-
tion and Diffusion of Water across the Blood-Brain Bar-
rier in Man,” Microvascular Research, Vol. 13, No. 1, 1997,
pp. 113-124. doi:10.1016/0026-2862(77)90120-0
[12] Nordic Software.
[13] J. L. oxerman, K. M. Schmainda and R. M. Weisskoff,
“Relative Cerebral Blood Volume Maps Corrected for
Contrast Agent Extravasation Significantly Correlate with
Glioma Tumor Grade, Whereas Uncorrected Maps Do Not,”
American Journal of Neuroradiology, Vol. 27, No. 4,
2006, pp. 859-867.
[14] R. Pohmann, “Accurate, Localized Quantification of White
Matter Perfusion with Single-Voxel ASL,” Magnetic Res-
onance in Medicine, Vol. 64, No. 4, 2010, pp. 1109-1113.
[15] J. P. Carr, D. L. Buckley, J. Tessier, et al., “What Levels
of Precision Are Achievable for Quantification of Perfu-
sion and Capillary Permeability Surface Area Product
Using ASL?” Magnetic Resonance in Medicine, Vol. 58,
No. 2, 2007, pp. 281-289. doi:10.1002/mrm.21317
Copyright © 2012 SciRes. OJMI