Changes in cerebral perfusion detected by dynamic susceptibility contrast magnetic resonance imaging: normal volunteers examined during normal breathing and hyperventilation

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J. Biomedical Science and Engineering, 2009, 2, 210-215

doi: 10.4236/jbise.2009.24034 Published Online August 2009 (http://www.SciRP.org/journal/jbise/

JBiSE

Published Online August 2009 in SciRes. http:// www.scirp.org/journal/jbise

Changes in cerebral perfusion detected by dynamic

susceptibility contrast magnetic resonance imaging:

normal volunteers examined during normal breathing

and hyperventilation

Ronnie Wirestam1*, Christian Engvall2, Erik Ryding3, Stig Holtås4, Freddy Ståhlberg1,4, Peter Reinstrup2

1Department of Medical Radiation Physics, Lund University, Lund, Sweden; 2Department of Anaesthesiology & Intensive Care,

Lund University Hospital, Lund, Sweden; 3Department of Clinical Neurophysiology, Lund University Hospital, Lund, Sweden;

4Department of Diagnostic Radiology, Lund University, Lund, Sweden.

Email: [email protected]

Received 3 March 2009; revised 2 April 2009; accepted 24 April 2009.

ABSTRACT

Global cerebral perfusion parameters were

measured using dynamic susceptibility contrast

magnetic resonance imaging (DSC-MRI) in eight

healthy volunteers examined during normal

breathing and spontaneous hyperventilation.

DSC-MRI-based cerebral blood flow (CBF) de-

creased during hyperventilation in all volun-

teers (average decrease 29%), and the corre-

sponding global CBF estimates were 73±19ml/

(min100g) during normal breathing and 52±

7.9ml/(min100g) during hyperventilation (mean

±SD, n=8). Furthermore, the hypocapnic condi-

tions induced by hyperventilation resulted in a

prolongation of the global mean transit time

(MTT) by on average 14%. The observed CBF

estimates appeared to be systematically over-

estimated, in accordance with previously pub-

lished DSC-MRI results, but reduced to more

reasonable levels when a previously retrieved

calibration factor was applied.

Keywords: Magnetic Resonance Imaging; Perfu-

sion; Cerebral Blood Flow; Mean Transit Time;

Hypocapnia

1. INTRODUCTION

The use of dynamic susceptibility contrast magnetic

resonance imaging (DSC-MRI) for assessment of perfu-

sion-related parameters is promising [1,2], but the con-

cept shows a number of methodological complications.

For example, accurate registration of the arterial input

function (AIF), i.e. the concentration-versus-time curve

in an appropriate tissue-feeding artery, is hampered by

arterial signal saturation [3] and local geometrical distor-

tion [4] at peak concentration, as well as by partial-

volume effects [5]. Furthermore, the T2* relaxivities of

the paramagnetic contrast agent are, most likely, differ-

ent in arterial and tissue environments, and the non-lin-

ear relationship in whole blood between transversal re-

laxation-rate change (R2*) and contrast-agent concen-

tration needs to be considered when gradient-echo pulse

sequences are used [6]. Attempts to achieve absolute

quantification of perfusion parameters by standard DSC-

MRI have typically been characterized by overestimated

absolute values of cerebral blood volume (CBV) and

cerebral blood flow (CBF) [1,3,7,8,9], and these obser-

vations are attributed, at least in part, to a correspond-

ingly underestimated arterial concentration time integral.

Hence, most existing implementations of DSC-MRI

provide perfusion parameters only in relative terms.

Reproducible absolute quantification of CBF is indeed

desirable, for example, in the follow-up of tumour or

stroke therapy, for determining tissue at risk in acute

ischaemic stroke and when a global change in CBF can

be expected. The potential of DSC-MRI for absolute or

semi-absolute CBF quantification is not yet fully estab-

lished, and additional information is warranted. Hence,

in order to further investigate the usefulness of DSC-

MRI for absolute quantification as well as for detection

of controlled changes in perfusion, estimates of global

CBF and mean transit time (MTT) were acquired in a

group of normal volunteers examined during normal

breathing and spontaneous hyperventilation. The pri-

mary aim of this study was to test the capability of

DSC-MRI to detect and quantify changes in global cere-

bral perfusion caused by spontaneous hyperventilation.

2. METHODS

2.1. Subjects and Experimental Procedure

Eight healthy volunteers (mean age 33 years) were in-

*Corresponding author.

R. Wirestam et al. / J. Biomedical Science and Engineering 2 (2009) 210-215 211

cluded in the study (Table 1). Each subject was exam-

ined by DSC-MRI during normal breathing and hyper-

ventilation on different occasions. The time interval be-

tween the two DSC-MRI experiments ranged from 3 to

35 days (mean time interval 16.4 days), and every sec-

ond subject started with the normocapnic conditions.

Hypocapnia was induced by spontaneous hyperventila-

tion under external guidance. During the DSC-MRI ex-

periments, end-tidal pCO2 (ETCO2) was monitored and

the subjects were at rest in the supine position, breathing

normal air with addition of extra O2 to a total level of

50%, with their eyes open and supplied with earplugs.

The study was approved by the local ethics committee,

and written informed consent was obtained from each

volunteer.

2.2. DSC-MRI Experiment

DSC-MRI was performed using a 1.5 T MRI whole-

body unit (Siemens Magnetom Vision, Siemens Medical

Systems, Erlangen, Germany). At each DSC-MRI ex-

periment, the subject received 0.2 mmol/kg bodyweight

of a gadobutrol MRI contrast agent (Gadovist 1.0,

Schering AG, Germany), administered into a peripheral

arm vein at an injection rate of 3 ml/s and followed by a

saline flush.

The first passage of the contrast-agent bolus through

the brain was tracked using dynamic gradient-echo echo-

planar imaging (GRE-EPI) during approximately 75 s at

a temporal resolution of 1.65 s. Ten slices with a slice

thickness of 8 mm were recorded and the imaging pa-

rameters were as follows: Echo time 54ms, matrix size

128×128 and field of view 250×250 mm2.

2.3. Post-Processing and Data Analysis

Estimates of CBF in ml/(min100g) were calculated ac-

cording to (1):















)()(

)(max)(

dttRdttC

tRdttC

kCBF

artery

H (1)

The tracer concentrations in tissue (C) and in artery

(Cartery) were calculated (in arbitrary units) using the re-

lationship C(t)  –ln[S(t)/S0], where S(t) is the signal at

time t and S0 is the baseline signal observed before arri-

val of the contrast-agent bolus [1,2]. The constant kH =

(1-Hlarge)/[(1-Hsmall)] was set to 0.705 ml/g in the pre-

sent study [1]. Hlarge and Hsmall are the haematocrit values

in large and small vessels, respectively, and  is the

brain density. R(t) is the tissue residue function, obtained

by deconvolution of the measured tissue concentration

time curve with the AIF, and max [R(t)] is the peak value

of this function. Deconvolution was performed using a

singular value decomposition algorithm.

The area under the AIF curve, i.e. the time integral of

the arterial concentration Cartery(t), was determined from

the same arterial locations in both the normal and the

hyperventilation case. A correction for the combined

consequences of partial-volume effects, arterial signal

saturation and signal displacement (due to local geomet-

rical distortion) at peak concentration was applied. The

employed correction resembled the approach described

by Knutsson et al. [9], although in the present study a

large brain-feeding artery (the internal carotid artery),

rather than the superior sagittal sinus, was used for the

AIF time-integral rescaling.

The rescaling procedure was based on the combined

concentration-versus-time information from the large

brain-feeding artery (showing distorted curve shape at

peak concentration due to partial-volume effects, signal

saturation and/or local geometric distortions) and a

smaller artery used as the AIF in the deconvolution pro-

cedure (assumed to show a reasonable curve shape but

suffering from an underestimated area under curve). The

concentration curve from the smaller artery (in practice

obtained from pixels very close to the middle cerebral

artery or its branches) was rescaled, with retained shape,

to fit the flanks and baseline of the distorted curve from

the large artery. The time integral of the rescaled

small-artery curve was used in (1) as an approximation

to the true concentration time integral of the large

Table 1. Volunteer data (sex, age, ETCO2 levels) and observed whole-brain average DSC-MRI CBF estimates.

ETCO2 [kPa] CBF [ml/(min 100g)]

Volunteer no. Sex Age

[years] Normocapnia Hypocapnia Normocapnia Hypocapnia

1 M 40 6.0 4.6 106 49.0

2 M 39 5.6 4.1 79.7 61.8

3 M 35 5.0 3.7 70.1 53.6

4 M 31 5.7 2.7 84.1 61.9

5 M 31 5.8 3.1 63.0 48.1

6 M 30 5.8 4.4 52.4 46.6

7 M 30 5.6 4.0 84 58.1

8 M 29 5.3 3.6 47.5 39.9

212 R. Wirestam et al. / J. Biomedical Science and Engineering 2 (2009) 210-215

brain-feeding artery.

The retrieval of R(t) by deconvolution also allows for

calculation of the mean transit time using Zierler’s area-

to-height relationship [1,10,11]:





)(max

)(

dttR

MTT







Parametric CBF and MTT maps were calculated, in

absolute terms, using (1) and (2), respectively. For CBF

as well as MTT, whole-brain average estimates were

calculated as the mean of all brain-tissue voxel values in

the 10 slices obtained by DSC-MRI. Large-vessel con-

tributions were eliminated by excluding all pixels with

values exceeding 2.5 times the average CBF value of the

entire volume [12]. Spurious MTT values were similarly

removed by excluding a small number of pixels with

values below 0.2 times the mean MTT value and above

2.5 times the mean MTT value of the entire volume.

2.4. Statistical Analysis

A Wilcoxon matched-pairs signed-ranks test was applied

to determine whether or not the ETCO2, CBF and MTT

values observed during normal breathing were signifi-

cantly different from those observed during hyperventila-

tion. The CBF-versus-ETCO2 and MTT-versus-ETCO2

relationships were evaluated by linear-regression analyses.

3. RESULTS

In all 8 volunteers, the DSC-MRI-based CBF estimates

decreased when the subject was hyperventilating (Table

1 & Figure 1a), and the mean CBF decrease during hy-

perventilation was 29%. Similarly, in all 8 subjects a

longer MTT was observed during hyperventilation (Fig-

ure 1b), with a mean MTT increase of 14%.

Average whole-brain CBF and MTT estimates at nor-

mocapnic and hypocapnic conditions, together with the

corresponding ETCO2 levels, are given in Table 2 (mean

±SD, n=8). The statistical analysis showed that ETCO2

levels as well as CBF and MTT estimates differed sig-

nificantly between normal-breathing conditions and hy-

perventilation (p<0.01).

The obtained relationship between CBF and ETCO2 is

displayed in Figure 2, indicating that CBF increased with

ETCO2. Figure 3 shows MTT versus ETCO2 and the

trendline suggests a slight decrease in MTT when ETCO2

increases. The significant prolongation of MTT observed

during hyperventilation implies, according to the central

volume theorem (MTT=CBV/CBF), that the relative de-

crease in CBF, induced by hyperventilation, was larger

than the corresponding relative decrease in CBV.

4. DISCUSSION

It is indeed encouraging that the expected decrease in

CBF during hyperventilation could be detected in all of

the volunteers. Analysis of the results from the whole

population showed that the CBF estimates obtained dur-

ing hyperventilation were significantly different from

those seen during normal breathing conditions (p<0.01),

and the observed average CBF decrease of 29% is in

quite reasonable agreement with previous studies [13,14].

The observed CO2 reactivity of CBF during hypocapnia,

corresponding to approximately 2.1% of reduction in

global CBF per mmHg change in ETCO2, is not at all

unreasonable, and the current estimate is in excellent

agreement with previous findings by Fortune et al. [13]

and Reinstrup et al. [15]. Other investigators have ob-

served a somewhat higher CO2 reactivity [e.g. 14,16],

corresponding to approximately 3% reduction in CBF

per mmHg change in ETCO2, but the characteristics of

previously investigated populations may have differed

with regard to, for example, sex, age and state of health,

and some previous studies were limited to grey matter.

110

100

Figure 1. Individual estimates of (a) cerebral blood flow (CBF) and (b) mean transit time (MTT),

measured by DSC-MRI in eight normal male subjects during normal breathing and hyperventilation.

CBF [ml/(min 100g)]

a b

MTT [s]

Normal

breathing

Normal Hyper-

ventilation

Hyper-

ventilation breathing

R. Wirestam et al. / J. Biomedical Science and Engineering 2 (2009) 210-215 213

Table 2. Whole-brain average DSC-MRI CBF and MTT esti-

mates and the corresponding ETCO2 levels during normal

breathing and hyperventilation (mean±SD, n=8).

ETCO2

[kPa]

CBF

[ml/(min 100g)] MTT[s]

Normal breathing 5.6 ± 0.3273 ± 19 6.5 ± 0.65

Hyperventilation 3.8 ± 0.6452 ± 7.9 7.5 ± 0.49

Relative change[%] -33 -29 +14

Wilcoxon test p-value 0.008 0.008 0.008

Figure 2. Estimates of cerebral blood flow

(CBF) obtained during normal breathing and

hyperventilation as a function of end-tidal pCO2

(ETCO2).

Figure 3. Estimates of mean transit time (MTT)

obtained during normal breathing and hyperven-

tilation as a function of end-tidal pCO2 (ETCO2).

The observed prolongation of the MTT during hyper-

ventilation is related to a decreased vascular blood ve-

locity during hypocapnia [14], and this effect manifests

itself as a well-documented smaller relative decrease in

CBV than in CBF during the hypocapnic conditions in-

duced by hyperventilation [13,14]. Ito et al. [17] ad-

dressed this topic by investigating the relative impor-

tance of the arterial, capillary and venous blood-volume

fractions in hypocapnia. The authors concluded that

changes in human CBV during hypocapnia are caused

by changes in the arterial blood-volume component

without changes in the venous and capillary blood vol-

ume [17].

The absolute global CBF values obtained from the

present experiments were somewhat high, in accordance

with previous experimental DSC-MRI investigations

[1,3,7,8,9] and theoretical predictions [6]. Partial-volume

effects and local geometric distortions at peak concen-

tration [3,4] can be problematic, but these effects were

most likely reduced by the applied correction of the arte-

rial concentration time integral. Hence, the remaining

CBF overestimation is probably related to the funda-

mental problem with DSC-MRI in that the response to a

given contrast-agent concentration differs between large

vessels and the capillary environment [6]. In spite of the

applied AIF-area correction, accurate determination of

Cartery(t) and the associated time integral was difficult in

some cases, and the relatively large standard deviations

(SDs) seen in the DSC-MRI results might be a reflection

of this difficulty. Identification of an appropriate AIF

location is another crucial issue in CBF and MTT quan-

tification. It has recently been established that the de-

sired linear relationship between R2* and con-

trast-agent concentration in arteries can indeed be ob-

tained by careful selection of the AIF from pixels not

completely located inside the vessel [18]. In the present

study, we were aware of the AIF-selection guidelines

provided by Bleeker et al. for single-shot EPI [18], and

tried, as far as possible, to consider them in the AIF

identification procedure.

CBF [ml/(min 100g)]

ETCO2 [kPa]

Even if DSC-MRI turns out to provide inherently

overestimated CBF estimates they may still be useful,

provided that DSC-MRI results consistently can be

shown to exhibit a high degree of linear correlation with

a reference CBF technique, for example, Xe-133 SPECT

[9] or positron emission tomography (PET) [7]. The

possibility to rescale DSC-MRI-based perfusion esti-

mates by application of an appropriate calibration factor,

based on such comparative studies, has been suggested,

although it has been pointed out that the retrieval of a

universal conversion factor, applicable to a variety of

DSC-MRI implementations, may be challenging [7].

MTT [s]

ETCO2 [kPa]

A CBV calibration factor, applicable to the current

DSC-MRI setup, has previously been obtained, in the

same group of volunteers as examined in the present

study, using SPECT imaging of Tc-99m-labelled eryth-

rocytes as a reference CBV method [19]. This calibration

factor can theoretically be used also to appropriately

correct corresponding DSC-MRI-based CBF values,

since CBF=CBV/MTT (according to the central volume

theorem), provided that MTT values can be correctly

estimated. The global MTT estimates observed in the

present study (mean value 6.5 s at normoventilation)

214 R. Wirestam et al. / J. Biomedical Science and Engineering 2 (2009) 210-215

were quite reasonable, and in accordance with previ-

ously published PET results from normal subjects. For

example, Kaneko et al. [20] observed MTT values of 6.1

s in grey matter and 8.1 s in white matter, and the large

study by Leenders et al. [21] showed CBV-to-CBF ratios

of 5.7 s in insular grey matter and 7.3 s in white matter.

Application of the calibration factor to the present data

resulted in a corrected whole-brain average CBF of ap-

proximately 42 ml/(min100g). Literature values of nor-

mal global CBF in humans at rest vary over a consider-

able range [22,23], but are typically between 40 and

50ml/(min100g) for the adult population. For example,

Knutsson et al. [9] obtained a whole-brain average CBF

of 40ml/(min100g) (in elderly normal subjects) by

Xe-133 SPECT, Slosman et al. [24] observed a global

CBF of 43ml/(min100g) in male volunteers (age interval

29-38 years), also by use of Xe-133 SPECT, Dörfler et

al. [22] reported a global CBF estimate of 48ml/

(min100g) based on extracranial sonography and Mat-

thew et al. [25] observed 40 ml/(min100g) using H2

15O

PET. Finally, Yonas et al. [26] employed stable xenon

computed tomography (Xe-CT) and extracted regional

CBF values of 92ml/(min100g) in the highest-flow

compartments, 54ml/(min 100g) in mixed-cortical re-

gions (calculated from linear-regression equations and

corresponding to the age of 33 years) and an age-inde-

pendent white-matter regional CBF of 20ml/(min100g).

In conclusion, DSC-MRI showed promising results in

the detection of controlled perfusion changes, induced

by spontaneous hyperventilation, in individual subjects.

In accordance with previously reported DSC-MRI ex-

periments, uncorrected absolute CBF values appeared to

be overestimated.

5. ACKNOWLEDGEMENTS

This study was supported by the Swedish Research Council (project no.

13514), the Swedish Cancer Society and the Crafoord Foundation,

Lund.

REFERENCES

[1] K. A. Rempp, G. Brix, F. Wenz, C. R. Becker , F. Gückel,

and W. J. Lorenz, (1994) Quantification of regional

cerebral blood flow and volume with dynamic suscepti-

bility contrast-enhanced MR imaging, Radiology, 193,

637-641.

[2] L. Østergaard, R. M. Weisskoff, D. A. Chesler, C.

Gyldensted, and B. R. Rosen, (1996) High resolution

measurement of cerebral blood flow using intravascular

tracer bolus passages, Part I: Mathematical Approach and

Statistical Analysis, Magnetic Resonance in Medicine,

36, 715-725.

[3] R. Ellinger, C. Kremser, M. F. Schocke, C. Kolbitsch, J.

Griebel, S. R. Felber, and F. T.Aichner, (2000) The

impact of peak saturation of the arterial input function on

quantitative evaluation of dynamic susceptibility con-

trast-enhanced MR studies, Journal of Computer Assisted

Tomography, 24, 942-948.

[4] M. Rausch, K. Scheffler, M. Rudin, and E. W. Radu,

(2000) Analysis of input functions from different arterial

branches with gamma variate functions and cluster

analysis for quantitative blood volume measurements,

Magnetic Resonance Imaging, 18, 1235-1243.

[5] J. J. Chen, M. R. Smith, and R. Frayne, (2005) The

impact of partial-volume effects in dynamic suscepti-

bility contrast magnetic resonance perfusion imaging,

Journal of Magnetic Resonance Imaging, 22, 390-399.

[6] B. F. Kjølby, L. Østergaard, and V. G. Kiselev, (2006)

Theoretical model of intravascular paramagnetic tracers

effect on tissue relaxation, Magnetic Resonance in

Medicine, 56, 187-197.

[7] C. B. Grandin, A. Bol, A. M. Smith, C. Michel, and G.

Cosnard, (2005) Absolute CBF and CBV measurements

by MRI bolus tracking before and after acetazolamide

challenge: Repeatability and comparison with PET in

humans, NeuroImage, 26, 525-535.

[8] K. E. Sakaie, W. Shin, K. R. Curtin, R. M. McCarthy, T.

A. Cashen, T. J. and Carroll, (2005) Method for

improving the accuracy of quantitative cerebral perfusion

imaging, Journal of Magnetic Resonance Imaging, 21,

512-519.

[9] L. Knutsson, S. Börjesson, E. M. Larsson, J. Risberg, L.

Gustafson, U. Passant, F. Ståhlberg, and R. Wirestam,

(2007) Absolute quantification of cerebral blood flow in

normal volunteers: Correlation between Xe-133 SPECT

and dynamic susceptibility contrast MRI, Journal of

Magnetic Resonance Imaging, 26, 913-920.

[10] P. Meier and K. L. Zierler, (1954) On the theory of

indicator-dilution method for measurement of blood flow

and volume, Journal of Applied Physiology, 6, 731-744.

[11] K. L. Zierler, (1965) Equations for measuring blood flow

by external monitoring of radioisotopes, Circulation

Research, 16, 309-321.

[12] T. Ernst, L. Chang, L. Itti, and O. Speck, (1999)

Correlation of regional cerebral blood flow from

perfusion MRI and SPECT in normal subjects, Magnetic

Resonance Imaging, 17, 349–354.

[13] J. B. Fortune, P. J. Feustel, C. deLuna, L. Graca, J.

Hasselbarth, and A. M. Kupinski, (1995) Cerebral blood

flow and blood volume in response to O2 and CO2

changes in normal humans, Journal of Trauma, 39,

463-472.

[14] H. Ito, I. Kanno, M. Ibaraki, J. Hatazawa, and S. Miura,

(2003) Changes in human cerebral blood flow and

cerebral blood volume during hypercapnia and

hypocapnia measured by positron emission tomography,

Journal of Cerebral Blood Flow and Metabolism, 23,

665-670.

[15] P. Reinstrup, E. Ryding, L. Algotsson, L. Berntman, and

T. Uski, (1994) Effects of nitrous oxide on human

regional cerebral blood flow and isolated pial arteries,

Anesthesiology, 81, 396–402.

[16] W. D. Obrist, T. W. Langfitt, J. L. Jaggi, J. Cruz, and T.

A. Gennarelli, (1984) Cerebral blood flow and meta-

bolism in comatose patients with acute head injury.

Relationship to intracranial hypertension, Journal of

Neurosurgery, 61, 241-253.

[17] H. Ito, M. Ibaraki, I. Kanno, H. Fukuda, and S. Miura,

(2005) Changes in the arterial fraction of human cerebral

blood volume during hypercapnia and hypocapnia

R. Wirestam et al. / J. Biomedical Science and Engineering 2 (2009) 210-215 215

measured by positron emission tomography, Journal of

Cerebral Blood Flow and Metabolism, 25, 852-857.

[18] E. J. W. Bleeker, M. A. van Buchem, and M. J. P. van

Osch, (2009) Optimal location for arterial input function

measurements near the middle cerebral artery in

first-pass perfusion MRI, Journal of Cerebral Blood Flow

and Metabolism, 29, 840-852.

[19] C. Engvall, E. Ryding, R. Wirestam, S. Holtås, K.

Ljunggren, T. Ohlsson, and P. Reinstrup, (2008) Human

cerebral blood volume (CBV) measured by dynamic

susceptibility contrast MRI and 99mTc-RBC SPECT,

Journal of Neurosurgical Anesthesiology, 20, 41–44.

[20] K. Kaneko, Y. Kuwabara, F. Mihara, T. Yoshiura, M.

Nakagawa, A. Tanaka, M. Sasaki, H. Koga, K. Hayashi,

and H. Honda, (2004) Validation of the CBF, CBV, and

MTT values by perfusion MRI in chronic occlusive

cerebrovascular disease: A comparison with 15O-PET,

Academic Radiology, 11, 489-497.

[21] K. L. Leenders, D. Perani, A. A. Lammertsma, J. D.

Heather, P. Buckingham, M. J. Healy, J. M. Gibbs, R. J.

Wise, J. Hatazawa, S. Herold, R. P. Beaney, D. J. Brooks,

T. Spinks, C. Rhodes, R. S. Frackowiak, and T. Jones,

(1990) Cerebral blood flow, blood volume and oxygen

utilization: Normal values and effect of age, Brain, 113,

27-47.

[22] P. Dörfler, I. Puls, M. Schliesser, M. Mäurer, and G.

Becker, (2000) Measurement of cerebral blood flow

volume by extracranial sonography, Journal of Cerebral

Blood Flow and Metabolism, 20, 269-271.

[23] N. A. Lassen, (1985) Normal average value of CBF in

young adults is 50ml100 g-1 min-1, Journal of

Cerebral Blood Flow and Metabolism [Editorial], 5,

347-349.

[24] D. O. Slosman, C. Chicherio, C. Ludwig, L. Genton, S.

de Ribaupierre, D. Hans, C. Pichard, E. Mayer, J. M.

Annoni, and A. de Ribaupierre, (2001) 133Xe SPECT

cerebral blood flow study in a healthy population:

Determination of T-scores, Journal of Nuclear Medicine,

42, 864-870.

[25] E. Matthew, P. Andreason, R. E. Carson, P. Herscovitch,

K. Pettigrew, R. Cohen, C. King, C. E. Johanson, and S.

M. Paul, (1993) Reproducibility of resting cerebral blood

flow measurements with H2

15O positron emission

tomography in humans, Journal of Cerebral Blood Flow

and Metabolism, 13, 748-754.

[26] H. Yonas, J. M. Darby, E. C. Marks, S. R. Durham, and

C. Maxwell, (1991) CBF measured by Xe-CT: Approach

to analysis and normal values, Journal of Cerebral Blood

Flow and Metabolism, 11, 716-725.