FULL PAPER

Efficient Sampling of Early Signal Arrival for Estimationof Perfusion and Transit Time in Whole-Brain ArterialSpin Labeling

Wayne Lee,1* Rafal Janik,2 Amy Scouten,2 Bojana Stefanovic,2,3 and John G. Sled1,3

Arterial spin labeling can be used to measure both cerebral per-fusion and arterial transit time. However, accurate estimation ofthese parameters requires adequate temporal sampling of thearterial spin labeling difference signal. In whole-brain multisliceacquisitions, two factors reduce the accuracy of the parameterestimates: saturation of labeled blood in transit and inadequatesampling of early difference signal in superior slices. Label sat-uration arises when slices are acquired inferior-to-superiorsuch that slice selection in proximal slices spoils the label for adistal slice. Inadequate sampling arises when the time spentacquiring inferior slices is too long to allow early sampling ofthe difference signal in superior slices. A novel approach tomultislice imaging is proposed to address these two issues. Inround-robin arterial spin labeling, slices are acquired in a differ-ent order after every pair of control-label acquisitions. Round-robin arterial spin labeling enables the acquisitions of all slicesacross the same range of postlabel delays in a descendingsuperior-to-inferior order. This eliminates the temporal samplingproblem and greatly reduces label saturation. Arterial transittime estimates obtained for the whole brain with round-robinarterial spin labeling show better agreement with a single-sliceacquisition than do conventional multislice acquisitions. MagnReson Med 000:000–000, 2011. VC 2011 Wiley Periodicals, Inc.

Key words: arterial spin labeling; perfusion; cerebral bloodflow; arterial transit time

Arterial spin labeling (ASL) MR is a noninvasive tech-nique that can be used to measure both cerebral perfu-sion (f) and arterial transit time (tA). Abnormalities inthese important physiological parameters have beenassociated with the onset and progression of cerebrovas-cular disease (1). Although accurate estimates of perfu-sion can be obtained by ASL without measuring transittime (2–5), prolonged arterial transit times, such as thosedue to arteriovenous malformation (6) or stroke (7), canlead to significant underestimation of perfusion. Notonly there is growing interest in how pathology (8,9)affects arterial transit time but also there is a growingbody of studies investigating how tA changes with activ-ity (10) or differs by region (11–14), age (15), and gender

(11,15). Robust estimation of tA requires adequate tempo-ral sampling of the ASL difference signal as the labeledbolus transits through the cerebral microvasculature andinto the tissue. A range of postlabel delays (PLDs)including those that precede the peak in the differencesignal must be acquired (Fig. 1a). ASL datasets withmultiple PLDs are often acquired using multislice fast,single-shot 2D echo-planar imaging (EPI) acquisitions(3,6,9,12,15–19). However, there are two phenomena thatmay confound accurate estimates of perfusion and transittime using multislice acquisitions—saturated labeleffects and longer PLDs in superior slices.

Saturated Label Effects

Saturated label effects occur when labeled blood that isdestined to perfuse a distal slice is partially saturated byslice-selective imaging pulses while in transit (4). Thiseffectively reduces labeling efficiency, leading to signifi-cant perfusion underestimation (18). For this saturationeffect to occur in an ascending (inferior-to-superior, IS)slice acquisition order, blood velocity must exceed a criti-cal threshold, vcrit ¼ d/t, where d is the distance betweenslices and t is the time between slice acquisitions. Onlythe first slice acquired is not subject to this saturated labeleffect. In the case of conventional IS acquisitions, the firstslice is always the most inferior and the critical velocity isthe same for the remaining slices; d is equal to the slicegap (g) and t is equal to slice acquisition time (tslice). Basedon these parameters, it is evident that typical EPI acquisi-tion times (40–60 ms) and slice gaps (1–2 mm) are, respec-tively, too long and too small for the ascending slice ac-quisition to outpace even slow-moving blood (4 cm/s).Consequently, superior slices may have lower signal-to-noise ratio (SNR) and biased perfusion estimates due tosaturated label effects. It is important to note that saturatedlabel effects occur only if labeled and saturated blood ispresent in the vasculature when a slice is acquired. If allslices are acquired after the labeled blood has passedthrough the local vasculature (i.e., postpeak), then no satu-rated label effects will occur. This is typical of ASL stud-ies where only perfusion is measured. Furthermore,although acquiring data from superior-to-inferior (SI) is away to avoid this saturated label effect, the data wouldstill be the subject to longer PLD times described below.

Longer Postlabel Delay Times

In whole-brain imaging, even with rapidly acquiredascending slices (~40 ms per slice), the top third of the

1Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada.2Imaging Research, Sunnybrook Research Institute, Toronto, Ontario,Canada.3Department of Medical Biophysics, University of Toronto, Ontario, Canada.

Grant sponsor: NSERC-CHRP; Grant number: 351067-2008.

*Correspondence to: Wayne Lee, M.Sc., Hospital for Sick Children, 555University Ave., Toronto, ON M5G 1X8, Canada. E-mail: wayne.lee@sickkids.ca

Received 30 June 2011; revised 22 August 2011; accepted 30 August2011.

DOI 10.1002/mrm.23222Published online in Wiley Online Library (wileyonlinelibrary.com).

Magnetic Resonance in Medicine 000:000–000 (2011)

VC 2011 Wiley Periodicals, Inc. 1

brain will be acquired at delays longer than 600 ms. Themost inferior slice may be sampled at the desired PLDrange of 50–2000 ms, but upper slices would be sampledat PLDs from 650 to 2600 ms. These images may besampled too late in the label passage for estimation oftA and may also have lower SNR than earlier samples(Fig. 1b).

Typical ASL protocols are affected by both saturatedlabel effects and PLD sampling delays. Some representa-tive single-slice and multislice ASL protocols reported in

the literature and corresponding critical velocities aresummarized in Table 1 (3,6,8–10,12,15–22). The criticalvelocities for these multislice studies are under 5.5 cm/s,indicating a potential for saturated label effects. If wedefine upper regions of the brain as being 9 cm distal tothe most inferior slice, significant portions of the frontaland parietal lobes will be sampled at delays of over 450ms. This delay represents a shift of high SNR samplesbefore the signal peak, to low SNR samples at the end ofthe perfusion signal decay curve. This would affect theaccuracy of transit time estimates for upper brain slices.

The round-robin approach to ASL (RRASL) is proposedto improve temporal sampling of the difference signalcurve of distal slices in conventional multislice acquisi-tions. This approach also allows for SI multislice acquisi-tions that reduce the impact of saturated label effects.This study compares the effects of label saturation andlonger PLDs on the accuracy of perfusion and transit timeestimates in conventional multislice, round-robin, andsingle-slice acquisitions in healthy adult subjects.

MATERIALS AND METHODS

Round-Robin ASL

In the RRASL, the time at which a given slice is sampledis permuted after each pair of control-label images. Tosimplify the explanation of this strategy, the followingdiscussion will use a SI acquisition as the reference. Fol-lowing a pair of dummy scans discarded due to lack ofsteady state, alternating pairs of control and label scansare acquired from SI. For the next pair, the second-mostsuperior slice is acquired first; the most inferior slice isacquired second last; and the most-superior slice isacquired last. This permutation scheme continues untilevery slice is sampled at every achievable PLD. The num-ber of PLDs sampled is equal to the number of slices, and

FIG. 1. a: Sampling the rise and fall of the perfusion signal allows

for reliable estimation of both f and tA. b: Sampling delays inupper slices may not include the signal peak and have lowerSNR. Neither tA nor f can be accurately estimated in the absence

of a signal peak.

Table 1

Imaging Parameters for a Selection of Recent ASL Studies

PaperASLtype

No. ofslices

Slice thickness /gap (mm)

Slice acquisitiontime (ms)

PLD(ms)

Coverage(mm)

Delay forupper brainimaging (ms)

Criticalvelocity(cm/s)

Single slice pCASL 1 5 / NA 50–1750 1Conventional pCASL 19 5 / 5 60 50–1750 190 540 8.33

RRASL—IS pCASL 19 5 / 0 100 50–1750 190 0 5.00RRASL—SI pCASL 19 5 / 5 100 50–1750 190 0 VariesRef. 16 pCASL 27 5 / 0 35.5 1525 135 639 0.00

Ref. 17 CASL 8 8 / 2 20*^ 1000 80 180 <2.50Ref. 20 LL-FAIR 1 6 / NA 150–2050 6

Ref. 18 Q2TIPS 6 8 / 0 54 1500–1770 48 608 0.00Ref. 21 CASL, pCASL 1 5 / NA 1500 5Ref. 10 LL-FAIR 1 8 / NA 50–2250 8

Ref. 8 TILT 1 10 / NA 200–1600 10Ref. 19 CASL 5–8 5 / 0 52* 700–2000 25–40 936 0.00

Ref. 3 CASL 8 7 / 2 77^ 250–1500 72 770 0.65Ref. 15 QUIPSS II 7 6 / 2 40* 40–3640 56 450 <5.00Ref. 12 QUIPSS 10 6 / 3 55 1400 90 550 5.45

Ref. 6 pCASL 15–17 7 / 0 35 1525 105–119 450 0.00Ref. 9 CASL FEAST 5 14 / 2 80 300–900 80 450 2.50

Ref. 22 pCASL 9 5 / 1 45 1000 54 675 2.22

Slice acquisition times identified with a * are TE only and with a ^ were interleaved acquisitions. Minimum delay for upper brain imaging

refers to the acquisition delay between the first slice and a slice �9 cm distal. Critical velocity is calculated as slice gap divided by sliceacquisition time.

2 Lee et al.

the time gap between PLDs is equal to slice acquisitiontime. Figure 2 compares the sampling range of a conven-tional multislice acquisition to an equivalent round-robinacquisition. In this work, both SI (RRASL-SI) and IS(RRASL-IS) round-robin acquisitions will be examined.

Two limitations to this approach arise due to the effectof changing slice order on effective repetition time (TR)for a given slice. First, when a slice switches from beingacquired first to being acquired last, the effective TR forstatic tissue spins is (no. of slices ¼ 1) � (tslice) greaterthan all other acquisitions of that slice. As this onlyoccurs with the pair of images acquired for the longestPLD, we address the limitation by dropping this datapoint from the analysis. The second limitation is thatthere is a slight difference (equal to tslice) in effective TRbetween control and label images within a pair. How-ever, because TR is generally greater than three times theT1 of blood and tissue, this effect may be ignored.

Saturated Label Effects

Saturated label effects will occur in IS acquisitions whenblood flow velocity exceeds critical velocity. Although aSI acquisition would avoid these effects, it would be dif-ficult to accurately estimate both CBF and tA in inferiorslices of the brain due to longer PLD times. RRASL imag-ing alleviates the PLD sampling issue, allowing for SIacquisitions. However, rotating slice order reintroducesthe possibility of saturated label effects. The critical ve-locity at which saturated label effects would occur forRRASL-SI acquisitions is determined by the followingequation (see Appendix for derivation):

Vcrit;SIðz;pÞ ¼ ðw þ gÞðZ � pþ 1Þ �w

ðp� 1Þtslice forðz þ p� 1Þ > Z

½1�

where z is the current slice, p is the PLD sample index, wis slice thickness, g is slice gap, and Z is the total numberof slices. The PLD sample index is the only nonfixed pa-rameter in this equation. As it is negative in the numeratorand positive in the denominator, Vcrit,SI decreases withPLD index. This indicates that later PLD samples, regard-less of slice location, are more affected by saturated labeleffects. However, this also means that earlier PLDs, which

have higher SNR, are less affected by saturated labeleffects. Given a set of imaging parameters (slice gap, slicethick, and slice acquisition time), it is possible to deter-mine the critical velocity for all slices and PLDs.

For example, the whole-brain ASL data in this studyhave 19 slices, each 5-mm thick, a slice gap of 5 mm, anda slice acquisition time of 100 ms. The critical velocitiesfor a RRASL-SI acquisition (most-superior slice) and for aRRASL-IS acquisition (any slice, applies to conventionalIS acquisitions as well) are illustrated in Fig. 3. The first13 PLDs acquired with a RRASL-SI acquisition are lessprone to saturated label effects than the correspondingPLDs of an IS acquisition. This is a worst-case scenariofor RRASL-SI acquisitions because the most-superior slicewill almost always have preceding acquired inferior sli-ces. Slices acquired in lower portions of the brain aresampled more often without preceding inferior slices, andtherefore would not meet the condition for saturated labeleffects. In general, slices acquired with RRASL-SI acquisi-tions are less affected by saturated label effects thanRRASL-IS slices, which have lower vcrit (Fig. 4).

MR Protocol

All MRI data were collected with a 1.5-T MRI System(Signa EXCITE HD, GE Medical) using a standard eight-channel receive-only head array coil. Whole-brain andupper neck anatomical and angiography scans wereacquired using a 3D FSPGR sequence: flip angle (FA) ¼15�, TR/echo time (TE) ¼ 9/4.2 ms, field of view (FOV)¼ 28.8 cm � 19.2 cm, 192 � 128 � 140 matrix, slicethickness ¼ 1.5 mm, and scan time ¼ 242 s; an Axial2DTOF SPGR sequence: FA ¼ 50�, TR/TE ¼ 24/4.6 ms,FOV ¼ 28.8 cm � 19.2 cm, 192 � 128 matrix, 140 slices,slice thickness ¼ 1.5 mm, and scan time ¼ 484 s. Meaninternal carotid blood velocity was measured in the labelplane using PC-MR: TR/TE ¼ 6.224/3.06 ms, FOV ¼ 288mm � 192 mm, 192 � 128 matrix, slice thickness ¼ 5mm, venc ¼ 100 cm/s, number of excitations (NEX) ¼ 4.

T1 Mapping

Longitudinal relaxation time constant maps wereacquired using the variable FA approach (23). Three 3DFSPGR sequences were acquired: FA ¼ 2�, 9�, 19�, TR/TE ¼ 5.7/1.6 ms, FOV ¼ 24 cm, 128 � 128 � 40 matrix,slice thickness ¼ 5 mm, Bandwidth (BW) ¼ 31.25 kHz,scan time ¼ 88 s each. Rapid B1þ field maps were

FIG. 2. All slices are sampled over the same PLD range withround-robin ASL (light shading), whereas superior slices aresampled at significant delays with conventional acquisitions (dark

shading).

FIG. 3. Comparison of critical velocities by PLD index for the

most superior slice of a RRASL-SI acquisition and an inferior-to-superior acquisition (conventional or RRASL-IS).

Perfusion and Transit Time by Whole-Brain ASL 3

acquired using two SE-EPI sequences: FA ¼ 60�/120�

and 120�/240�, eight shots, TR/TE ¼ 4000/18 ms, FOV ¼24 cm, 128 � 128 matrix, 40 slices, slice thickness ¼ 5mm, BW ¼ 250 kHz, and scan time ¼ 72 s each.

ASL

ASL imaging was done using an in-house developedsequence similar to the balanced pCASL sequencedescribed by Dai et al. (21). Labeling comprised a seriesof 1000 slice-selective Hanning-shaped RF pulses with aFA of 35�, pulse duration of 0.5 ms, and spacing of 1.5ms, for a total labeling duration of 1500 ms. For allexperiments, a 5-mm labeling plane was positioned per-pendicular to the internal carotid arteries. Imaging wasacquired with single-shot EPI: TR/TE ¼ 5000/23 ms,FOV ¼ 24 cm, 64 � 64 matrix, slice thickness ¼ 5 mm,and slice gap (where appropriate) ¼ 5 mm. Four differentacquisitions were acquired in each subject. The samerange of PLDs (50–1750 ms, 18 increments) and numberof control-label pairs (3) were acquired with eachapproach. The benchmark dataset by which the othermultislice acquisition approaches were compared wasthe single-slice (SS) dataset. It is free of both slice timingdelays and of potential saturated label effects, and con-sisted of a SS located above the anterior commissure(AC)-posterior commissure (PC) line. The remaining mul-tislice datasets were all centered about this center slice.The conventional (Conv) dataset consisted of 19 slicesacquired from IS regions of the brain. The aforemen-tioned PLDs applied to the first slice only, and the slice-to-slice acquisition time was set to 60 ms. The RRASL—SI dataset consisted of the same slices as the conven-tional acquisition, but slices were acquired from SIregions of the brain. To achieve the aforementioned PLDsampling range, slice-to-slice acquisition time was set to100 ms. The RRASL—IS dataset was identical to theRRASL—SI dataset, but with slices acquired from IS. Anextra pair of control-label images was acquired at the be-ginning of every ASL acquisition to allow for the ASLsignal to reach steady state; these images were discardedbefore analysis. The data were acquired in five healthyadult volunteers (two females, mean age 25.2 years).

ASL—Saturated Label Effects

Data were acquired in a healthy volunteer (20 years, M) toinvestigate the impact of saturated label effects on the

ASL difference signal. A slice of interest was identifiedand seven datasets were acquired with 0–6 inferior slicesbefore this slice of interest (7–19 total slices). The PLD forthis slice was fixed at 600 ms for all acquisitions, and theslice acquisition time was 60 ms. Imaging parameters werethe same as above, except for a TR of 4 s and 15 control-label pairs being acquired for each dataset.

Postprocessing and Statistical Analysis

T1 Mapping

The two SE-EPI images and three FSPGR imagesacquired for T1 mapping were linearly aligned to the 2�

FSPGR image using FLIRT (24). A high-resolution T1

relaxation map was then generated using the methoddescribed by Cheng and Wright (23). The T1 map wasregistered into ASL space, separately for each acquisitionapproach, using linear registration between the 2� FSPGRimage and the appropriate ASL image.

Postprocessing of ASL Data

To minimize resampling errors, the ASL datasets wereall processed and analyzed in their own space. Follow-ing reconstruction, all ASL images were motion cor-rected using 2D (three parameter) in-plane motion cor-rection (2dImReg, AFNI) (25), with the first controlimage volume of each dataset acting as the reference tar-get. Following motion correction, a mean image wascomputed and masked for registration purposes. TheMontreal Neurological Institute (MNI) gray matter (GM)structural atlas was registered into ASL space using two-step nonlinear alignment and nearest neighbour resam-pling (FNIRT, FMRIB, Oxford, UK). Within the slice ofinterest only the frontal and parietal lobes were consis-tently present across all subjects.

Control minus label difference images were calculatedfor each PLD and repetition. Voxel-wise equilibrium mag-netization values (m0

a) were calculated as the mean con-trol signal divided by l, the brain/blood partition coeffi-cient. Frontal and parietal lobe regions of interest (ROIs)were defined in the SS data and the equivalent slice inthe multislice datasets using the MNI GM atlas andincluded voxels with a mean difference signal >0.5%(averaged across all PLDs and repetitions). Voxels alongthe brain’s midline were excluded to avoid intravascularlabeled blood. The average difference signal within theseROIs was then calculated for each PLD and repetition.

FIG. 4. Comparison of satu-

rated label effects across mul-tiple slices for RRASL-SI (a)and RRASL-IS (b). Areas inblack are strongly affected,areas in gray are less affected,

and areas in white are notsubjected to saturated label

effects.

4 Lee et al.

The ROI difference signals were then used to estimateperfusion and transit time, including 95% confidenceintervals, using a slow two-compartment model (see Eqs.2–4 below) (3) and a nonlinear robust estimator routine inMatlab (nlinfit; Mathworks, Natick, MA). Relevant param-eters and their assumed values are listed in Table 2.

DMðtÞ ¼ 0 for t < tA ½2�DMðtÞ ¼ 2fm0

aa expð�DtAÞ 1� expð�JtÞJ

�for tA < t < tA þ tL

þAðJ � C þ C expð�Jðt � tAÞÞ � J expð�Cðt � tAÞÞ

JCðJ � CÞ�

½3�

DMðtÞ ¼ 2fm0aa expð�DtAÞ 1

Jþ A

JðC � JÞ8>>:

9>>;�

� ððexpðJtL � 1Þ expð�Jðt � tAÞÞ� A

CðC � JÞ� ððexpðCtL � 1Þ expð�Cðt � tAÞÞ

�for t > tA þ tL

½4�

where A ¼ PS/Vbw, C ¼ 1/T1t, D ¼ 1/T1b, and J ¼ A þ D(slow solution). Inversion efficiency of the pCASLsequence was modeled using a numerical simulation ofthe Bloch equation across a range of flow velocities (5–60 cm/s). Results of this simulation were then empiri-cally tested using a water flow phantom (1-cm ID tubing)across the same range of velocities. Simulation and ex-perimental estimates for inversion efficiency were foundto be within 5% of one another. In this study, mean in-ternal carotid velocities of 25–35 cm/s were measured,which corresponded to an inversion efficiency of �0.85.A linear mixed effects model was used to determine ifacquisition approach had a significant effect on CBF andtA estimates (fixed effects ¼ acquisition approach, ROI;random effects ¼ subject; weighting based on parameterconfidence intervals; 31 degrees of freedom).

In addition to the ROI-based analyses, voxel-wise per-fusion and transit time estimates were also calculated forGM voxels of the SS common to all datasets. To improveSNR, this data were in-plane smoothed using a 6-mmfull width at half maximum gaussian filter. GM voxelswere identified using the MNI GM structural atlas.

Postprocessing of ASL Data—Saturated Label Effects

Processing of the saturated label effects data was carriedout in the same manner described above, except that

these data were not used for CBF estimation. Instead, themean GM difference signal in the slice of interest,excluding midline voxels, was calculated and plottedagainst the number of proximal slices. Linear regressionwas used to determine if the imaging of proximal sliceshad a significant impact on the ASL difference signal.

RESULTS

Motion

Minimal motion was detected in all ASL datasets.Within scan movement along the x- and y-axes did notexceed 1 mm (average ¼ 0.22 mm) and rotation aboutthe z-axis did not exceed 0.5� (average ¼ 0.11�).

Impact of Proximal Slices on the Difference Signal

The saturated label effects dataset had a slice gap of 5mm and a slice acquisition time of 60 ms. Hence, onlyintravascular labeled blood flowing at velocities greaterthan 8 cm/s would have an effect on distal slices. Figure5 shows the impact of proximal slices on the mean GMdifference signal. Percent signal change decreased signif-icantly with an increasing number of preceding acquiredproximal slices (y ¼ �0.06x þ 1.5; P <0.0001).

Impact of Proximal Slices and Longer PLD Times onParameter Estimation

A comparison of the perfusion-weighted data obtainedwith the four acquisition approaches and the subsequentmodel-based estimation for a single subject is shown inFig. 6. Data acquired with the benchmark single-slice ac-quisition and the SI round-robin acquisition had similarpeak height and position and led to similar estimates of f

Table 2Definition of Parameters Used for Modeling

Symbol Definition of parameters Units Value

f Blood perfusion mL blood/min/100 mL tissue Estimated

PS Permeability surface area product (26) mL water/min/100 mL tissue 1.5k Brain/blood partition coefficient (26) mL water (mL tissue)�1/mL water (mL blood)�1 0.9

tA Arrival time (from labeling plane to image slice) s EstimatedtL Labeling time s 1.5a Labeling efficiency Fraction of blood water protons inverted 0.85

T1t Longitudinal relaxation time of water in tissue s MeasuredT1b Longitudinal relaxation time of water in blood (27) s 1.4

m0a Equilibrium magnetization of arterial blood Magnetic moment (mL blood)�1 mean ctrl/k

FIG. 5. Percent signal change decreases as a function of preced-ing acquired inferior slices. PLD was 600 ms for all data points

and error bars represent standard error. Vcrit for this experimentwas 8 cm/s.

Perfusion and Transit Time by Whole-Brain ASL 5

and tA. Peak height and position were visibly shifted forRRASL-IS and conventional-IS acquisitions. This led to alower perfusion estimate for RRASL-IS and longer tA esti-mate for conventional-IS. Note that conventional-IS acqui-sition has fewer samples before the peak, likely biasingthe estimate of tA. Figure 7 illustrates the frontal and pari-etal ROIs as well as the perfusion and transit time esti-mates found in these regions, including 95% confidenceintervals, for all subjects and acquisition approaches.Because of variations in the slice of interest between sub-jects, the ROIs ranged in size from 55 to 132 voxels in thefrontal lobe and 69 to 150 voxels in the parietal lobes.Note the differences in the parameter estimates obtainedby the different methods. Linear mixed effects analysisconfirmed an effect of acquisition method on the esti-mates of both f and tA (P < 0.0001). Arterial transit timeestimates were longer with conventional-IS acquisitions(blue) than with the other approaches (,tA,CONV � tA,SS:218 ms, P < 0.0001). No significant difference in transit

time was found between either of the round-robin acquisi-tions and the benchmark SS acquisition (tA,RRASL-SI �tA,SS: �2 ms, P ¼ 0.96; tA,RRASL-IS � tA,SS: 34 ms, P ¼ 0.3).Transit times to the parietal lobe within this slice werefound to be significantly shorter than the frontal lobe(tA,FRONTAL � tA,PARIETAL: 108 ms, P < 0.0001).

The IS round-robin acquisition resulted in signifi-cantly lower difference signals and hence perfusion esti-mates than any of the other acquisition approaches(fRRASL-SI � fRRASL-IS: 10 mL/min/100 mL, P < 0.02; fSS �fRRASL-IS: 25 mL/min/100 mL, P < 0.0001). Although theconventional-IS acquisition transit times were signifi-cantly longer due to poor PLD sampling, the resultingperfusion estimates were comparable to those foundwith the SS acquisition (fCONV � fSS: 5 mL/min/100 mL,P ¼ 0.07). Perfusion in the parietal lobe of this slice wasslightly higher than that of the frontal lobe (fFRONTAL �fPARIETAL: �6 mL/min/100 mL, P < 0.007).

Single Voxel-Parameter Estimates

Single voxel transit time and perfusion estimates for a sin-gle subject are illustrated in Fig. 8. Voxels with negativeperfusion or transit time estimates are overlaid in pink.Black voxels were not part of the MNI GM mask. In Fig.8a, it is visually evident that a conventional acquisition(bottom left) overestimates tA relative to the SS andRRASL-SI datasets (top left and top right, respectively).The RRASL-IS dataset (bottom right) also had poor agree-ment with the SS dataset and includes a large number ofvoxels for which perfusion and transit time estimates werenegative (pink overlay). Perfusion estimates were compara-ble between SS, RASL-SI, and conventional datasets (Fig.8b). However, perfusion could not be estimated in a largeportion of the posterior brain in the conventional dataset.In general, datasets subject to saturated label effects (con-ventional and RRASL-IS) had more voxels for which per-fusion and transit time could not be estimated.

DISCUSSION

Accurate estimation of both perfusion and arterial transittime requires dynamic imaging of labeled water as it

FIG. 6. Raw data (solid lines, averaged at each PLD) and model

fits (dashed line) for subject 4 in the parietal lobe. Legend con-tains parameter estimates and 95% confidence intervals for perfu-sion (f: mL blood/min/100 mL tissue) and transit time (tA: s). SS

(green) and RRASL-SI (Red) raw data and fits are comparable.Conversely, the difference signal is significantly lower with a

RRASL-IS acquisition and noticeably delayed with a conventionalacquisition.

FIG. 7. a: ROI example for a single subject, frontal lobe in blue, and parietal lobe in orange. b: Arterial transit time and (c) perfusion esti-

mates for all subjects (shading), regions (frontal ¼ left panel and parietal ¼ right), and acquisition method (color). Horizontal bar repre-sents the parameter estimate. Vertical lines denote 95% confidence intervals. The data shown in Fig. 6 are shaded in orange.

6 Lee et al.

accumulates in the tissue. Multislice EPI acquisitions arecommonly used with ASL for measuring whole-brainperfusion. However, multislice imaging has two limita-tions that can affect estimation accuracy. Saturated labeleffects occur when the labeled bolus is saturated byproximal slice acquisitions, resulting in reduced effectiveinversion efficiency for distal slices. The accuracy of fand tA estimates is further confounded when upper sli-ces of the brain are acquired at significantly later PLDsthan more proximal slices due to multislice acquisitiontimes. This study investigated the effect of these twophenomena on perfusion and transit time estimates invivo. In addition, an alternative method of acquiringmultislice data that reduced these effects was proposedand tested. Experiments investigating IS acquisitionsdemonstrated an effect of label saturation on the ASLdifference signal. The difference signal and subsequentperfusion estimates obtained with IS acquisitions weresignificantly lower than those from with SS acquisitions.These findings are consistent with the saturated labeleffects reported in the literature, indicating that the ASLdifference signal in a given slice can be reduced by thesaturation of labeled blood during the acquisition of pre-ceding inferior slices (4,18).

The slice of interest in this study was subjected to aPLD sampling delay of 540 ms when acquired using aconventional multislice acquisition. The transit time esti-mates predicted for this slice using SS data and a PLDinterval of 100 ms were on the order of 900 ms. With theconventional multislice acquisition, transit time esti-mates were anticipated to be accurate as three datapoints were acquired before 900 ms when the peak per-fusion signal should arise. However, transit time esti-mates from data acquired with the conventional acquisi-tion were significantly longer (>200 ms) than those

predicted by the SS data. This bias is likely due to twofactors. First, in the SS data, there were nine data pointsacquired before the transit time signal peak (PLDs 50–>850 ms), whereas, in the conventional-IS data, thereare only four prepeak data points (PLDs 590–>890). Theloss of five prepeak, high SNR data points (PLDs 50–450)for five late-curve, low SNR data points (PLDs 1890–2290) increases the sensitivity of transit time estimatesto variance in the prepeak data points. Second, as men-tioned in the introduction, saturated label effects occuronly if labeled blood is present in the vasculature attime of imaging. This has the interesting effect of reduc-ing the difference signal before and around the signalpeak, because labeled blood was present in the vascula-ture, while the decay portion of the signal remains unaf-fected because no labeled blood was present at thesetimes. This systemic reduction in signal before andaround the signal peak coupled with fewer prepeak datasamples translates into erroneously longer transit timeestimates with conventional multislice acquisitions.These effects were also evident in the single-voxel datawhere tA estimates were noticeably longer in many vox-els of the conventional dataset. Additionally, saturatedlabel effects contribute to an increased number of poorfits in the RRASL-IS dataset. It should be noted that theestimates of model parameters such as inversion effi-ciency and partition coefficient also affect the accuracyof the perfusion estimate. However, by comparing closelyrelated acquisition protocols and applying the same bio-physical model to each, estimation bias caused by inac-curacy in these fixed model parameters does not affectthe relative performance of the protocols.

It is interesting to note that despite the presence ofsignificant errors in transit time estimation, the con-ventional multislice data still resulted in comparable

FIG. 8. Single voxel perfusion (a) and transit time (b) estimates in GM voxels for subject 4. Voxels with negative perfusion or transit timeestimates are overlaid in pink. Voxels in black were either WM or background voxels as identified by the MNI GM atlas.

Perfusion and Transit Time by Whole-Brain ASL 7

perfusion estimates to the SS data. This is likely due tothe relative insensitivity of perfusion estimates to transittime when the difference signal curve is sampled post-peak (2–5). However, in the absence of a difference signalpeak it is impossible to estimate transit time. Given amodel with two free parameters, if the parameter tA can-not be estimated, the accuracy of any estimate of the otherparameter, f, will be degraded. Furthermore, if an accurateestimate for transit time is not sought, then there is noreason to acquire multi-PLD data in the first place.

To address saturated label effects and longer PLDtimes in multislice ASL imaging, the round-robin acqui-sition approach was developed. By changing the slice ac-quisition order following every pair of control-labelimages, the issue of long sampling delays is eliminatedbecause all slices are sampled across the same range ofPLDs. Data acquired in this study showed no significantdifferences in the transit times estimates between eitherSS data or the RRASL data. As all slices are sampledsimilarly, this suggests that accurate whole-brain transittime estimates are achievable with RRASL.

A further benefit of this even sampling is that the direc-tion of slice acquisition, IS or SI, is no longer a concernwith respect to adequate temporal sampling. Conse-quently, SI acquisitions are possible, allowing for the mit-igation of saturated label effects. RRASL perfusion esti-mates were significantly (~13%) higher with SIacquisitions than with IS acquisitions. Although this indi-cates a reduced sensitivity to saturated label effects, theRRASL-SI perfusion estimates were still lower (14%) thanwhat was predicted by the SS data. This suggests that thesaturated label effects present in late PLD data points ofRRASL-SI (Figs. 3 and 4) still have an impact on the dif-ference signal and perfusion estimates. Data collected inthis study indicated interslice flow velocities of at least 8cm/s (Fig. 4). It may be possible to ameliorate these effectsby increasing the PLD sampling interval and therebyincrease the sampling range free of saturated label effects.However, this may come at the cost of decreased preci-sion for measuring tA.

The RRASL sequence implemented in this studyacquires a number of PLDs equal to the total number ofslices minus one. However, the RRASL sequence can bemodified to acquire fewer or more PLDs. In the case offewer PLDs, the slice order could be incremented by twoor more slices after every control-label pair. This wouldhalve the number of PLDs and double the PLD spacing,while maintaining the same range of PLDs. Althoughthis would introduce a slight discrepancy in PLDsbetween even and odd slices, the primary goal of havingmultiple early PLDs samples could still be achieved. Ifmore PLDs are desired, the sequence could be repeatedwith different initial PLD offsets.

This study found significant differences in transit timeand perfusion between frontal and parietal regions. How-ever, as the data being compared consisted of a SS, thesefindings could be due to within region variations intransit time and perfusion. Previous studies have shownsignificant across subject variability in transit timebetween these regions (11,14).

Imaging with 3D acquisitions avoids both saturatedlabel effects and PLD sampling delays (18).However,

rapid 3D acquisitions are prone to blurring and spatialdistortion (6,11,13,28). Moreover, the RRASL allows forgreater flexibility with respect to total scan time. Presum-ing n slices and m PLDs, conventional 3D imaging withslice phase encoding would require n phase encodes andm acquisitions, for a total scan time of n � m � TR. A2D RRASL acquisition of equivalent duration wouldhave n averages and equivalent SNR. However, if fewerthan n averages are necessary, the 2D RRASL sequencecould be acquired more rapidly. For example, the 2DRRASL data acquired in this study took 10 min of scantime for 19 slices, 18 PLDs, a TR of 5s, and three repeats.In contrast, given 19 slices and a TR of 5 s, only sixPLDs could be acquired with a 3D sequence in 10 min.

CONCLUSION

There are two major issues that affect the accuracy of per-fusion and transit time estimation in whole-brain multi-slice ASL imaging. The first of these issues, saturated labeleffects, is due to the saturation of labeled blood during theacquisition of preceding inferior slices. This effectivelyreduces inversion efficiency, reducing the difference signaland leading to perfusion underestimation. The second ofthese issues, sampling delays effects in superior regions ofthe brain, leads to poor sampling of the difference signalbefore its peak. This interacts with saturated label effectsand can result in overestimation of transit time. Theround-robin approach to multislice imaging allows for SIslice acquisitions while sampling all slices across thesame range of PLDs. RRASL-SI thus reduces saturatedlabel effects and eliminates the sampling delay issue.

APPENDIX A—DERIVATION OF CRITICAL VELOCITYFOR RRASL-SI

In RRASL-SI acquisitions the critical velocity will vary asa function of slice (z ¼ 1. . .Z, most inferior to most supe-rior) and the PLD sample index of said slice (p ¼ 1. . .P,earliest PLD to latest PLD), where Z and P are equal forRR acquisitions. If no inferior slices have been before theacquisition of the current slice (z) there is no saturatedlabel effect, this is true when (z þ p � 1) � Z. Otherwise,at least one inferior slice has been acquired before the ac-quisition of the current slice. Because the slice acquisitionorder is S-I (i.e., moving away from the current slice) thefirst saturated bolus to reach the current slice will havecome from the first slice acquired as part of the currentrepetition (shortest distance traveled and most time totravel). The first slice for a given acquisition (z, p) is

zFIRSTðz;pÞ ¼ ðz þ p� 1Þ � Z for ðz þ p� 1Þ > Z ½A1�

The distance between this slice and the current slice is

dðz;pÞ ¼ ðw þ gÞðz � zFIRSTðz;pÞÞ �w ½A2�

where w is the slice thickness and g is the gap betweenslices. This equation simplifies to

dðz;pÞ ¼ ðw þ gÞðz � z � pþ Z þ 1Þ �w ½A3�dðz;pÞ ¼ ðw þ gÞðZ � pþ 1Þ þw for ðz þ p� 1Þ > Z

½A4�

8 Lee et al.

The distance the closest proximal bolus has to travel toreach the current slice depends solely on the PLD sam-ple index (p). Because p represents the PLD index of thecurrent slice, then the time between the acquisition ofthe first slice and the current slice (zFIRST) is simply

tðz;pÞ ¼ ðp� 1Þtslice ½A5�

where tslice is the time between slice acquisitions. If wesubstitute Eqs. A4 and A5 into the critical velocity equa-tion (vcrit ¼ d/t), it is apparent that the velocity for satu-rated label effects is actually independent of slice,provided a proximal slice is acquired before the currentslice [(z þ p � 1) > Z]

Vcrit;SIðz;pÞ ¼ ðw þ gÞðZ � pþ 1Þ �w

ðp� 1Þtslice for ðz þ p� 1Þ > Z

½A6�

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