Download Quantification of endothelial permeability, leakage space, and blood

JOURNAL OF MAGNETIC RESONANCE IMAGING 11:575–585 (2000)
Original Research
Quantification of Endothelial Permeability,
Leakage Space, and Blood Volume in Brain Tumors
Using Combined T1 and T2* Contrast-Enhanced
Dynamic MR Imaging
X.P. Zhu, MD, PhD,1 K.L. Li, PhD,1 I.D. Kamaly-Asl, MB, ChB, FRCS,1
D.R. Checkley, MSc,2 J.J.L. Tessier, PhD,2 J.C. Waterton, PhD, MRSC,2 and
A. Jackson, PhD, MBChB, MRCP, FRCR1*
This study describes a method for imaging brain tumors
that combines T1-weighted (T1W) and T2*-weighted (T2*W)
dynamic contrast-enhanced acquisitions. Several technical improvements have been made to produce high-quality
three-dimensional mapping of endothelial permeability
surface area product (k) and leakage space (vl), based on
T1W data. Tumor blood volume maps are obtained from
T2*W images with a complete removal of residual relaxivity effects. The method was employed in 15 patients with
brain tumors (5 gliomas, 5 meningioma, and 5 acoustic
schwannoma). Mean values of vl were significantly greater
in acoustic schwannomas (53% ⴞ 9%) than in meningiomas (34% ⴞ 7%) or gliomas (22% ⴞ 4%). Mean values of vl
in meningioma were significantly greater than those of
gliomas. Mean values of rCBV correlated closely with k.
There was also a positive correlation between k and vl for
pixels with low k values. This relationship was weaker in
areas of high k. The highest mean ratios of k to vl (kep) were
seen in two patients with glioblastoma, one patient with
transitional cell meningioma, and one patient with angioblastic meningioma. Pixel-by-pixel comparison showed a
strong correlation between rCBV and k in 11 of 15 patients. However, decoupling between pixel-wise rCBV and
k was found in four patients who had lesions with moderate k and vl elevation but no increase of rCBV. Results from
this study suggest that in assessing the angiogenic activities in brain tumors it is advisable to monitor simultaneously changes in tumor blood volume, vessel permeability, and leakage space of tumor neovasculature. J. Magn.
Reson. Imaging 2000;11:575–585. © 2000 Wiley-Liss, Inc.
Index terms: perfusion; endothelial permeability; T1 and T2*
relaxation; vascular input function; brain neoplasms; angiogenesis
1
Division of Imaging Science and Biomedical Engineering, Stopford
Medical School, University of Manchester, Manchester M13 9PT,
United Kingdom.
2
AstraZeneca, Macclesfield, Cheshire SK10 4TG, United Kingdom.
*Address reprint requests to: A.J., Imaging Science and Biomedical
Engineering, University of Manchester, Manchester M13 9PT, UK.
E-mail: A [email protected]
Received August 23, 1999; Accepted January 31, 2000.
© 2000 Wiley-Liss, Inc.
THE GROWTH OF TUMORS over a certain size is dependent on the development of a vascular supply adequate for the metabolic needs of the neoplastic tissues.
Once a growing tumor reaches a critical mass, equivalent to approximately 1 mm3 or 1 ⫻ 106 cells, diffusion
of metabolites becomes insufficient, and new blood vessel growth occurs (1,2). This process, known as angiogenesis, is regulated by stimulatory and inhibitory cytokines released from tumor cells, endothelial cells, and
macrophages (1,3–5). Numerous studies have recently
described the role of the endothelial mitogen vascular
endothelial growth factor (VEGF), which plays a critical
role in stimulating angiogenesis in many tumors including gliomas and meningiomas. The production of
VEGF and possibly of other angiogenesis-inducing
agents is stimulated, at least in part, by regional hypoxia as the tumor outgrows its existing blood supply
(6,7). The level of expression of VEGF varies with tumor
type and grade (8,9), and the greatest expression is seen
in cerebral gliomas where levels are greatest in more
malignant grade tumors (8,10,11). A similar relationship between VEGF and tumor behavior is seen in meningiomas, where high levels of VEGF expression have
been described in vascular aggressive tumors and in
tumors associated with peritumoral edema (12–14).
These observations have led to the development of new
therapeutic agents designed to inhibit angiogenesis
with the intention of restricting tumor growth and dissemination (5,15,16). The introduction of this class of
therapeutic agent requires the development of imaging
strategies to demonstrate the presence of angiogenic
activity or the success of angiogenic inhibition strategies.
Imaging of angiogenesis relies on the identification of
surrogate markers of angiogenic activity. The relationship between tumor behavior and microvascular density on histological studies has led several groups to
investigate the measurement of cerebral blood volume
(CBV) using positron emission tomography or MRbased methods (17–20). In gliomas, CBV appears to
correlate closely with tumor grade and prognosis but it
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576
Zhu et al.
is not a specific marker for angiogenesis and represents
only one aspect of angiogenic activity. Other workers
have attempted to improve the specificity of imaging
techniques by identifying parameters that are sensitive
to the marked variation in vessel size (21,22) or the
resulting disturbances in blood flow, associated with
angiogenesis (23). Since VEGF activity is associated
with marked increase in endothelial permeability, one
alternative approach to the imaging of angiogenesis is
to quantify the permeability of vascular endothelia
(8,18,24,25). This can be achieved by the application of
established pharmacokinetic models of contrast distribution (26 –29) to data acquired from dynamic MR relaxivity-based studies. In practice, the acquisition of
permeability, blood volume, and vascular morphology
data is desirable. A few groups have so far proposed
methods to combine these measurements in the same
subject (22,30 –33). Two groups, including our own,
applied combined techniques to patients (22,32).
In this paper we describe a novel imaging protocol
combining data from two separate dynamic acquisitions. The first dynamic series uses a T1-weighted
(T1W) gradient-echo volume imaging technique to provide data for calculation of permeability surface area
product and leakage space using a multicompartment
model described by Tofts and Kermode (28). The first
contrast injection also serves to pre-enhance tumor tissue in preparation for the second, T2*W series. The
T2*W series is acquired with high temporal resolution
to allow accurate derivation of parameters based on
first-pass kinetics and recirculation abnormalities
(17,23,34). To obtain reproducible measurements of tumor blood volume and vessel permeability using this
methodology, we recently introduced several key technical improvements (23,32,35,36). The aim of this
study was threefold. First, we intended to apply the new
imaging protocol to a group of brain tumor patients and
to compare the resulting parametric images, to gain a
better understanding of the angiogenic activity of brain
tumors. Second, we aimed to compare the use of MRderived indices of blood volume, contrast distribution
volume, and endothelial surface area product in intraaxial tumors (which derive their blood supply from the
cerebral circulation) with extra-axial tumors (which derive their blood supply from the external carotid circulation or from combined internal and external carotid
supplies). Third, we wished to assess the complementary value of each parameter, to determine the advantages of using the combined method in tumor vascular
characterization.
Table 1
Demographic Details and Diagnoses for 15 Patients Included in
the Study
Patient
no.
Age
(yr)
Sex
Diagnosis
Tumor
locationa
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
67
59
39
44
49
49
48
75
65
52
33
71
52
77
53
F
M
M
M
F
M
M
F
F
F
M
F
M
F
M
Acoustic schwannoma
Acoustic schwannoma
Acoustic schwannomab
Acoustic schwannoma
Acoustic schwannoma
Fibroblastic meningiomab
Transitional meningiomab
Angioblastic meningioma
Meningioma
Meningioma
Grade III astrocytomab
Glioblastoma multiformeb
Glioblastoma multiformeb
Glioblastoma multiformeb
Grade III astrocytomab
Lt CPA*
Lt CPA
Rt CPA
Rt CPA
Lt CPA
Parafalcine
Tentorial
Convexity
Parafalcine
Tentorial
Frontal
Parietal
Occipital
Occipital
Thalamic
CPA ⫽ cerebello-pontine angle.
Diagnoses were histologically confirmed in 8/15 cases.
a
b
MATERIALS AND METHODS
Patients
Fifteen patients with known intracranial neoplasms
were included in the study. All patients gave informed
consent, and the Central Manchester Healthcare Trust
Medical Ethics Committee approved the study. Table 1
shows the demographic and histological data for each
patient. Five patients with acoustic schwannoma and
five patients with meningioma received no treatment
prior to the study. All patients with gliomas received
steroid therapy for 1– 8 days prior to imaging.
Image Acquisition
Imaging was performed on a 1.5 T ACS Gyroscan NTPT6000 (Philips Medical Systems, Best, The Netherlands; maximum gradient strength 23 mT/M and maximum slew rate 105 mT/M/msec) using a birdcage
head coil. Scanning parameters are shown in Table 2.
Prior to scanning, a 16 G catheter was inserted into an
antecubital vein using local anesthetic. This large needle bore was employed to minimize resistance to manual injection, but adequate injection rates can be attained with 18 G needles. Routine clinical T1W and
T2W imaging was performed in all patients prior to
dynamic studies.
The imaging protocol for dynamic contrast-enhanced
Table 2
Imaging Sequences, Scan Parameters, and Length of Dynamic Scans
Scan
FE
2°
10°
35°
T1dy
T2dy
Sequences
3D
3D
3D
3D
2D
T1W-FE
T1W-FE
T1W-FE
T1W-FE
T2*W-FEEPI
TR/TE/␣ (msec/°)
Matrix
⌬t (sec)
Tdy (min)
4.3–7/1.1–1.6/2°
4.3–7/1.1–1.6/10°
4.3–7/1.1–1.6/35°
4.3–7/1.1–1.6/35°
221/30/35°
128 ⫻ 128 ⫻ 25
128 ⫻ 128 ⫻ 25
128 ⫻ 128 ⫻ 25
128 ⫻ 128 ⫻ 25
128 ⫻ 128 ⫻ 9
—
—
—
5.1–8.7
1.86
—
—
—
11–12
1.9
Combined T1 and T2* Dynamic MR Imaging
577
studies consisted of three consecutive three-dimensional (3D) radiofrequency (RF) spoiled (T1W) field echo
acquisitions with an array of flip angles (␣ ⫽ 2°, 10° and
35°) to allow calculation of T1 maps. The third sequence
was then repeated (n ⫽ 80–120) to produce a T1W
dynamic data set (T1dy) with a time resolution of 5.1–
8.7 seconds and a duration of 11–12 minutes. Contrast
[0.1 mmol/kg of gadodiamide (Gd-DTPA-BMA)] was
given as a manual intravenous bolus injection over a
period of 4 seconds following the seventh (⌬t ⫽ 5.1
seconds) or fifth (⌬t ⫽ 8.7 seconds) dynamic scan. T2*W
dynamic data (T2dy) were then acquired using a multislice field echo (FE) echoplanar (T2*W-FEEPI) sequence
with 60 dynamic acquisitions each with a time resolution of 1.86 seconds. A second bolus of contrast agent
(0.1 mmol/kg of Gd-DTPA-BMA) was given following the
10th dynamic acquisition.
cay, and a2, m2 are amplitudes and rate constants of
the slow decay.
Maps of R10, C(t) and the VIF were then used to
calculate the permeability surface area product, k, and
leakage space, vl, on a pixel-by-pixel basis using the
triexponential model described by Tofts and Kermode
(28):
C(t) ⫽ D䡠{b 1䡠exp(⫺m 1t) ⫹ b 2䡠
exp(⫺m 2t) ⫺ (b 1 ⫹ b 2)䡠exp(⫺k䡠t/vl)}
(5)
where b1 ⫽ k 䡠 a1/(k/vl ⫺ m1) and b2 ⫽ k 䡠 a2/(k/vl ⫺
m2).
Multislice maps of rate of change of T2* [⌬R2* ⬅
⌬(1/T2*)] were calculated from the T2*FEEPI dynamic
data signals for each dynamic phase:
Parametric Image Calculation
⌬R2*(t) ⫽ ⫺(1/TE)䡠ln(S(t)/S(0))
(6)
All data were transferred to an independent workstation (Sun Microsystems) for analysis. Maps of proton
density (M0), and intrinsic longitudinal relaxation rate
(R10 ⬅ 1/T10) maps were calculated by fitting the
steady-state T1-FE signals S(␣) with the Ernst formula
(assuming TE Ⰶ T2*):
where S(0) and S(t) are signal intensities measured from
the T2*W dynamic series before and after contrast bolus injection. The gamma variate model (37) was used to
fit the first-pass ⌬R2*(t) data:
S(␣) ⫽ M 0䡠sin␣䡠(1 ⫺ E1 0)/(1 ⫺ cos␣䡠E1 0)
⌬R2*(t) ⫽ q(t ⫺ tD) re ⫺(t⫺tD)/b
(1)
where ␣ has three discrete values (␣ ⫽ 2°, 10°, and 35°),
and E10 ⫽ exp(⫺TR 䡠 R10).
4D (x, y, z, t) postinjection longitudinal relaxation rate
[R1(t)] maps were calculated for each T1W dynamic
phase using signal intensity data from pre- and postcontrast T1-FE images [S(t) ⫺ S(0)]:
R1(t) ⫽ ⫺(1/TR)䡠ln{[1 ⫺ (A ⫹ B)]
⫼[1 ⫺ cos␣䡠(A ⫹ B)]}
冘
N2
(3)
where ᑬ1 is the relaxivity of Gd-DTPA-BMA determined
experimentally, ᑬ1 ⫽ 4.39 s⫺1 mM⫺1 (at 37°C and at
1.5 T).
The time course of intravascular contrast concentration was used to calculate an effective vascular input
function (VIF). The VIF was calculated by fitting the
plasma contrast concentration time course data Cp(t)
from the superior sagittal sinus to a biexponential decay curve:
VIF ⬅ C p(t)
⫽ D䡠[a 1䡠exp(⫺m 1䡠t) ⫹ a 2䡠exp(⫺m 2䡠t)]
where q, tD, r, and b are fitting constants. Relative
cerebral blood volume (rCBV) maps were calculated
from the area under the gamma variate curves. Relative
mean transit time (rMTT) maps were calculated by measuring the full width of the curve at half-height (38). The
relative height of the recirculation peak in the T2* data
was represented using a parametric map of relative
recirculation (rR), given by:
(2)
where ␣ ⫽ 35°, TR ⫽ 4.3–7.0 seconds, A ⫽ [S(t) ⫺
S(0)]/(M0 䡠 sin␣), B ⫽ (1 ⫺ E10)/(1 ⫺ cos␣ 䡠 E10).
4D Gd-DTPA-BMA concentration [C(t)] maps were
then calculated from the 4D R1(t) maps:
C(t) ⫽ [R1(t) ⫺ R10]/ᑬ1
(7)
(4)
where D is the dose of Gd-DTPA-BMA, a1, m1 are amplitudes and rate constants of the fast exponential de-
rR ⫽
关⌬R2* experimental共t兲 ⫺ ⌬R2* theoretical共t兲兴
t ⫽ N1
⌬R2* peak 䡠 共N2 ⫺ N1兲
(8)
where ⌬R2*theoretical(t) is generated from fitting constants q, r, and b of the gamma variate in Eq. [7],
⌬R2*peak is the peak value of ⌬R2*theoretical(t), N1 marks
the onset of the recirculation, and N2 is the end time of
the dynamic run. Negative values of rR indicate residual relaxivity effects as a result of inadequate suppression by the Gd contrast pre-load.
Measurement of Fitting Errors
Parametric maps of scaled fitting error (SFE) were produced to assess the goodness of fit of the model in all
cases. Maps of SFE were used with the triexponential
model applied to T1W data [SFE(T1)] and with the
gamma variate model used with T2*W [SFE(T2)]
SFE ⫽
冑冘 冘
共T ⫺ A兲 2
T2
⫻ 100%
(9)
578
where A is measured C(t) or ⌬R2*(t), and T is C(t) or
⌬R2*(t) calculated from triexponential (Eq. [5]) or
gamma variate (Eq. [7]).
Coregistration of Data Sets
Movement in individual dynamic series was considered
minimal following image review, and motion correction
was not performed prior to parametric image generation. For each patient the parametric images, k and
rCBV, generated from T1W and T2*W images, respectively, were spatially coregistered to allow direct pixelby-pixel comparison. Coregistration used an automated difference minimization method implemented in
the ENvironment for Visualizing Images (ENVI, floating
point software, Colorado). The map of k was warped
using a cubic spline interpolation to conform to the
rCBV map.
Data Analysis
Data Quality
Visual inspection of source images, subtracted dynamic images, and parametric maps was used to assess
image quality. T1W images, including source and subtracted dynamic series, were viewed as a movie to identify significant patient movement. SFE(T1) maps were
used to identify areas where fit to the triexponential
model was poor. T2* images were also viewed as a movie
to identify patient movement. The presence of residual
relaxivity effects in T2*W images was assessed in three
ways: 1) maps of rR, examined for the presence of negative values; 2) SFE(T2) maps; and 3) rMTT maps, used
to identify areas within tumors where fit to the gamma
variate model was poor or where rMTT appeared abnormally short or long [SFE(T2) ⬎ 30%, rMTT ⱕ 2 or rMTT ⱖ
16 seconds].
Qualitative Assessment of Images and Maps
The parametric maps of k, vl, rCBV, SFE(T1), and
SFE(T2) from each case were reviewed by a neuroradiologist to examine the distribution of abnormalities
within tumors and other tissues. Correlation in variability of measured parameters was subjectively assessed and recorded.
Quantitative Assessment of Images and Maps
Regions of interest were identified in each patient by
manual identification of tumor boundaries from anatomical images. In patients with gliomas, the necrotic
central tumor core was excluded from the region of
interest (ROI) on the basis of poor fit quality [SFE(T1) ⬎
20%). Values of k greater than 1.2 min⫺1 were excluded
from analysis since the majority of these will represent
errors due to measurement of “pseudo-permeability” in
large blood vessels (39). The mean values of R10, rCBV,
k, vl, and efflux rate constant (kep ⬅ k/vl) (40) for each
tumor were calculated, and comparisons between tumors were performed using Student’s t-tests.
The relationships among k, vl, and rCBV in tumor
areas were examined in each case using a pixel-by-pixel
comparison. The data were reviewed on scatter plots to
Zhu et al.
identify any correlated behavior between parameters or
any clustering of pixels. Linear regression analyses
were performed when scatter plots demonstrated apparent linear correlation. Correlation coefficients of k
vs. vl [R(k,vl)] and k vs. rCBV [R(k,rCBV)] were calculated.
Linear regression was conducted with outlier regression, and plots of residuals were examined to ensure
that observed relationships were not outlier-dependent
effects. The relationships among mean values of k, vl,
and rCBV were also examined using the linear regression analyses.
RESULTS
Data Quality
Examination of dynamic series showed no evidence of
significant motion during data collection in any case.
Examination of parametric maps demonstrated no artifacts resulting from patient motion in brain areas.
Parametric maps of SFE(T1) demonstrated good concordance to the Toft’s data model. Mean SFE(T1) values
within meningiomas, acoustic schwannomas, and enhancing rims of gliomas were below 10.1%. In four of
the five patients with gliomas, a central area of high
SFE(T1) was identified corresponding to the “necrotic
core” commonly observed in these tumors. Normal
brain tissue demonstrated fit failure (SFET1 ⬎ 30%), in
keeping with an intact blood-brain barrier.
T2*W dynamic images showed no evidence of residual
relaxivity (T1 shine-through) effects. Maps of rR had no
negative values in any tumors (meanrR ⫺ 2 ⫻ sdrR ⬎ 0,
where meanrR is the mean rR value of pixels in tumor
region and sdrR is the standard deviation of rR) in any of
the cases described. Within the meningiomas, acoustic
schwannomas, and enhancing rims of gliomas, mean
SFE(T2) values were consistently below 14%, indicating
close conformance to the gamma variate model; mean
values of rMTT ranged from 4.6 to 13.1 seconds.
Qualitative Assessment of Images
Imaging appearances were similar in four patients with
glioma. Four tumors demonstrated an enhancing rim of
tissue with low SFET1 and SFET2. This rim contained a
core of nonenhancing tissue in which pixels demonstrated very high values of both SFET1 and SFET2 or fit
failure. In the fifth patient there was no necrotic core,
and the tumor appearance in k and vl maps was similar
to the tissue seen in the tumor rim in the other four
cases. In the enhancing portions of the tumors, elevation of k and vl showed clear mismatches in spatial
distribution in two patients with glioblastomas (case 12
in Fig. 1 and case 13). However, the distribution of k
and rCBV values appeared very similar in four cases
(cases 11–14). The rCBV in the tumor rim approached
or exceeded values seen in normal gray matter in three
cases of glioblastoma. Areas with high values of k were
seen much more frequently in three patients with glioblastoma than in two with grade III astrocytomas.
In five patients with acoustic schwannoma, the central core of the tumor demonstrated pixel values in
rCBV maps similar to those in normal brain tissues.
Both k and vl maps showed elevation of values in all
Combined T1 and T2* Dynamic MR Imaging
579
Figure 1. Images from a patient (case 12) with
a glioblastoma multiforme. A: Parametric map
of MO. B: Parametric map of R10. C and D: Preand postcontrast image from T1W dynamic series. E and F: Pre- and postcontrast image from
T2*W dynamic series. G: Parametric maps of
rCBV calculated from T2*W data. H: Map of k
from T1W data. I: Map of vl from T1W data.
Parametric maps demonstrate a rim of high k,
vl, and rCBV with a central necrotic core. The
distribution of k and rBCV values within the
rim is similar, with particularly high values in
a posterior tumor nodule. In contrast, values of
vl in the nodule are relatively low compared
with values observed within the tumor rim. A
second nodule of tumor tissue is also seen medial to the main body of the tumor. This nodule
has high blood volume and k but low vl. Pixelby-pixel comparison shows a close correlation
between k and rCBV in tumor rim [R(k,CBV) ⫽
0.70]. There is no such relationship between k
and vl [R(k,vl) ⫽ ⫺0.08].
cases. In two cases (case 4 in Fig. 2 and case 1), a
prominent vascular rim was seen surrounding the extracanalicular component of these two relatively large
schwannomas. These rims were identified by very high
levels of rCBV and k. The k values in the vascular rims
lay above 0.36 min⫺1 but below the threshold value of
1.2 min⫺1 described above. Pixels in these vascular
areas were therefore included in the tumor ROI used for
quantitative analysis. There is a clear mismatch in spatial distribution in one of the large acoustic schwannomas (case 4 in Fig. 2) between k and vl maps but not in
smaller acoustic schwannomas.
Meningiomas demonstrated very variable behavior,
with rCBV values ranging from low values similar to
those of white matter to very high values similar to
those observed in major blood vessels. Parametric maps
of k and vl showed elevation in all tumors, and once
again a clear spatial mismatch between k and vl was
evident in two tumors, one transitional meningioma
(case 7), and one angioblastic meningioma (case 8). The
values of k and rCBV were widely variable, with markedly elevated areas within these two tumors. In one
case (case 9, Fig. 3), marked elevation of k and vl was
observed throughout the entire cerebral meninges,
which appeared normal on the rCBV map. Since there
was no history of prior lumbar puncture or inflamma-
Figure 2. Images from a patient (case
4) with a right-sided acoustic schwannoma. A: Parametric map of rCBV from
T2*W data. B: Map of k from T1W data.
C: Map of vl from T1W data. The distribution of k is similar to vl but not as
much as to rCBV. There are high values of k and rCBV in both the periphery and extracanalicular portion. In
contrast, high values of vl are seen
within the intracanalicular portion
and in the main body of the tumor.
Pixel-by-pixel comparison yields R(k,vl)
⫽ 0.19 and R(k,CBV) ⫽ 0.55.
tory meningeal cerebrospinal fluid (CSF) pressure from
prior lumbar puncture, cerebral hemorrhage, or meningitis, we assume these changes reflected diffuse meningiomatosis.
Quantitative Assessment of Images
Table 3 shows the mean values of R10, k, vl, kep, and
rCBV for each tumor. Comparison of R1, k, vl, kep, and
rCBV values between tumor types demonstrated significantly lower values of R10 in the acoustic schwannoma
group compared with meningiomas (P ⬍ 0.01) and
gliomas (P ⬍ 0.01). Mean vl values of acoustic schwannoma are significantly larger than meningiomas (P ⬍
0.02) and gliomas (P ⬍ 0.001). Mean vl values of meningiomas are significantly larger than gliomas (P ⬍
0.05). No significant group differences were found between mean values of either k, kep, or rCBV (P ⬎ 0.05).
Examination of scatter plots (Fig. 4) of k versus vl
from individual tumors showed clustering of the majority of pixels at k values below 0.3 min⫺1. Three clusters
representing acoustic schwannoma, (green), meningioma (blue), and glioma (red) are clearly separated from
their vl values (Fig. 4a). Figure 4a also demonstrates a
positive correlation between k and vl for pixels with low
k values. Correlation analysis of mean values of k and vl
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Zhu et al.
Figure 3. Images from a patient (case 9) with
a posterior falcine meningioma. A and B: Preand postcontrast image from T1W dynamic
series. There is diffuse enhancement of leptomeninges on the postcontrast T1W images.
C: Parametric map of rCBV from T2*W data.
D: Map of k from T1W data. E: Map of vl from
T1W data. The tumor shows relatively homogeneous distribution of all parameters. The
calvarial meninges show marked increase in
k and vl while the rCBV map shows no apparent abnormality.
shows the same trend: r(kmean ⬍ 0.6 min⫺1, where k is
the mean value of each tumor) ⫽ 0.62, n ⫽ 15, P ⬍
0.02, and r(kmean ⬍ 0.3 min⫺1) ⫽ 0.81, P ⬍ 0.001.
Such dependency was no longer observed, ie, r(kmean ⬍
1.2 min⫺1) ⫽ 0.30, P ⬎ 0.10, while pixels with high
values of k (⬎0.3 min⫺1) were included. The spatial
mismatch between k and vl values in a subgroup of
meningiomas or gliomas is seen most clearly. In Fig. 4b,
the pixels in the three glioblastomas, plotted in violet,
spread much more widely along the k axis than those in
the two astrocytomas, plotted in orange.
Estimates of correlation between k and vl on a pixelby-pixel basis, R(k,vl), show evidence of this relation-
ship, with a positive correlation between k and vl observed in 11 cases. In the four remaining cases, the
pixel-by-pixel correlations between k and vl are insignificant, reflecting the presence of large proportions of
pixels with high values of k within the tumors, viz. two
glioblastomas multiforme (cases 12 and 13), one transitional cell meningioma (case 7), and one angioblastic
meningioma (case 8). These four tumors also have the
largest ratio of mean k to vl, in terms of efflux rate
constants (kep), as shown in Fig. 5.
The scatter plots of k vs. rCBV for all pixels in 15
tumors demonstrate a positive correlation between k
and rCBV (Fig. 4c). There was also a close correlation
Table 3
MR Parameters From 15 Patients, Including Mean Values of Longitudinal Relaxation Rate (R10), Permeability Surface Area Product (k),
Leakage Space (vl), Efflux Rate Constant (kep), and Relative Cerebral Blood Volume (rCBV)a
Patient no.
Diagnosis
R10 (s⫺1)
k (min⫺1)
vl (%)
kep (min⫺1)
rCBV (A.U.)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
AS
AS
AS
AS
AS
FM
TM
AM
M
M
GIII
GM
GM
GM
GIII
0.55 ⫾ 0.08
0.42 ⫾ 0.15
0.50 ⫾ 0.15
0.64 ⫾ 0.26
0.64 ⫾ 0.19
0.88 ⫾ 0.10
0.89 ⫾ 0.18
1.11 ⫾ 0.40
0.94 ⫾ 0.08
0.90 ⫾ 0.12
0.95 ⫾ 0.30
0.80 ⫾ 0.11
0.87 ⫾ 0.31
0.47 ⫾ 0.10
0.95 ⫾ 0.18
0.33 ⫾ 0.12
0.18 ⫾ 0.06
0.19 ⫾ 0.16
0.29 ⫾ 0.12
0.15 ⫾ 0.07
0.10 ⫾ 0.06
0.44 ⫾ 0.35
0.53 ⫾ 0.28
0.20 ⫾ 0.16
0.12 ⫾ 0.05
0.07 ⫾ 0.06
0.24 ⫾ 0.27
0.25 ⫾ 0.26
0.17 ⫾ 0.21
0.10 ⫾ 0.09
62 ⫾ 11
42 ⫾ 11
45 ⫾ 12
66 ⫾ 16
52 ⫾ 13
33 ⫾ 7
39 ⫾ 19
35 ⫾ 9
42 ⫾ 6
20 ⫾ 7
24 ⫾ 14
15 ⫾ 7
21 ⫾ 10
22 ⫾ 11
26 ⫾ 12
0.56 ⫾ 0.23
0.43 ⫾ 0.12
0.43 ⫾ 0.18
0.47 ⫾ 0.36
0.28 ⫾ 0.09
0.32 ⫾ 0.10
1.34 ⫾ 1.12
1.70 ⫾ 1.52
0.49 ⫾ 0.21
0.64 ⫾ 0.09
0.44 ⫾ 0.81
1.96 ⫾ 2.53
1.58 ⫾ 0.13
1.00 ⫾ 0.09
0.37 ⫾ 0.28
2.91 ⫾ 0.18
0.71 ⫾ 0.06
1.09 ⫾ 0.07
2.77 ⫾ 0.09
0.88 ⫾ 0.05
1.58 ⫾ 0.09
5.05 ⫾ 0.12
4.90 ⫾ 0.11
2.21 ⫾ 0.07
1.75 ⫾ 0.11
0.83 ⫾ 0.03
2.23 ⫾ 0.16
2.00 ⫾ 0.15
1.77 ⫾ 0.14
0.99 ⫾ 0.03
AS ⫽ acoustic schwannoma, M ⫽ meningioma, FM ⫽ fibroblastic meningioma, TM ⫽ transitional meningioma, AM ⫽ angioblastic
meningioma, GIII ⫽ grade III astrocytoma, GM ⫽ glioblastoma multiforme, A.U. ⫽ arbitrary unit.
a
Combined T1 and T2* Dynamic MR Imaging
581
Figure 4. Pixel-by-pixel scatter plots. A: k against vl, including all pixels in 15 tumors. Three types of tumor are rendered with
different colors, ie, green represents acoustic schwannomas (n ⫽ 5), blue for meningiomas (n ⫽ 5), and red for gliomas (n ⫽
5). B: k against vl, including five histologically confirmed gliomas. Violet represents glioblastoma (cases 12–14), and orange
represents grade III anaplastic astrocytoma (cases 11 and 15). C: k against rCBV in 15 tumors (green: acoustic schwannomas,
blue: meningiomas, red: gliomas). D: k against rCBV in glioma patients (violet: multiforme glioblastomas, orange: anaplastic
astrocytomas). In the less aggressive tumors, only scattered individual pixels are seen at k values above 0.3 min⫺1. In the more
aggressive tumors, values of k above 0.3 min⫺1 are common.
between mean values of rCBV and mean values of k
across the entire patient group [r(kmean ⬍ 1.2
min⫺1) ⫽ 0.90, n ⫽ 15, P ⬍ 0.001]. However, there
is no clear separation among clusters of acoustic
schwannomas (green), meningiomas (blue), or gliomas (red) on either the k or rCBV axis. Figure 4d
shows a scatter plot of k vs. rCBV in the subgroup of
gliomas. There is more elevation of k and rCBV values
in patients with glioblastoma multiforme (cases 12–
14, violet) than the anaplastic astrocytomas (cases 11
and 15, orange).
Pixel-by-pixel comparisons demonstrated a close correlation between k and rCBV in 11/15 tumors. In 4/15
cases, 2 small acoustic schwannomas (cases 2 and 5), 1
anaplastic astrocytoma (case 15), and 1 meningioma
(case 10), with low mean values of rCBV, this relationship was not observed. Figure 6 shows an example of
one of these cases (case 10). The rCBV map in this case
demonstrates a heterogeneous tumor with low values
between normal gray and white matter. Both k and vl
maps delineate the tumor clearly against the background of nonenhancing brain tissues.
DISCUSSION
The extraction fraction of standard MR contrast
agents such as gadodiamide varies with tissue type
(18). Normal brain tissue is an exception since the
intact blood-brain barrier allows no leakage of contrast agent. However, in most brain tumors, the
blood-brain barrier is disrupted, and contrast agent
leakage does occur. In perfusion mapping techniques
based on T2* contrast mechanisms, the loss of contrast agent from the intervascular space can result in
reduction of the induced field gradients and consequent underestimation of regional tumor blood volume. In addition, signal intensity may be paradoxically increased due to T1 enhancement, so-called T1
shine-through, which will cause a further underestimation of tumor blood volume (20,41,42). These
problems complicate the true estimation of regional
blood volume in the absence of genuine intravascular
“blood pool” contrast agents.
In 1985, Budinger and Huesman (43) have attempted
to separate flow and extraction of contrast agent from
vessels using MR imaging data, and several groups
582
Zhu et al.
Figure 5. Relationship between mean values of kep and rCBV for each tumor. There
is clear elevation of mean kep values in patients with glioblastoma multiforme, and
angioblastic and transitional cell meningiomas. Squares, glioma; triangles, acoustic
schwannoma; diamonds, meningioma, with
standard error bars.
have recently described methods that combine blood
volume and vessel permeability measurements of tumors (22,30 –33,44,45). Donohue and co-workers (22)
calculated parametric maps representing vascular
blood volume, leakage space and vessel size from simultaneous acquired spin-echo and gradient-echo data in
patients with brain tumors. The vessel size was measured using a technique that exploits the differences in
susceptibility effects on tumor capillaries on T2 and T2*
images (21). The permeability and corrected rCBV maps
were calculated using the Weisskoff method by secondary estimation of the T1 relaxivity changes from T2*W
data (33). This was the first attempt to acquire a set of
MR images representing three aspects of tumor capillary beds in a clinically acceptable time. However, the
spin-echo/gradient-echo EPI imaging provided only five
slices to accommodate high temporal and in-plane spatial resolution (22). Furthermore, the permeability measurement suffers from inaccuracy when large leakage is
present in tumors (33). Ostergaard and co-workers (44)
have also used Weisskoff’s method in their study on the
early changes in tumor blood volume and permeability
following dexamethasone treatment in patients with
brain tumors.
In this paper, we have calculated perfusion maps
based on T2*W dynamic data. A dose of 0.1 mmol/kg of
Gd-DTPA-BMA has been found to be optimal for preenhancement to avoid significant residual relaxivity effects (23). The absence of relaxivity effects was confirmed by examining the shape, size, and recirculation
level of the bolus passages from the T2*W dynamic
data. The preload method worked well for all cases in
this study, whether contrast leakage was large or small.
After elimination of T1 shine-through, values on the
T2*W-rCBV map may be considered a close approximation of true tumor tissue blood volume. We have calculated permeability and leakage space maps based on
T1W dynamic data using the method described by Tofts
and Kermode (28). This method represents a comprehensive approach, which aims to calculate a true absolute value for endothelial permeability surface area
product (k). The rationale behind this more complex
and methodologically demanding approach is to produce a measurement that is independent of variables,
such as receive gain, contrast dosage, patient cardiovascular status, etc. (46,47). This approach is required
for monitoring changes in tumor neovasculature during
treatment and for determining the surrogate end points
for treatment in longitudinal comparison and multicenter trials (48).
In this paper we introduce a few refinements to the
Tofts and Kermode method, which are the keys to im-
Figure 6. Images from a patient (case
10) with a tentorial meningioma (histology not available). A: Parametric
map of rCBV from T2*W data. B: Map
of k from T1W data. C: Map of vl from
T1W data. The tumor shows very low
values of rCBV below those of normal
gray matter but is conspicuous on
both k and vl maps. Spatial distribution of k is weakly correlated with vl
but not at all with rCBV, with R(k,vl) ⫽
0.22, R(k,CBV) ⫽ 0.10, respectively.
Combined T1 and T2* Dynamic MR Imaging
prove the accuracy of the technique. First, a 3D RF
spoiled gradient recalled field echo (GRE) with very
short TR and TE is employed to reduce the acquisition
time required for 3D R10 and M0 and 4D C(t) mapping.
3D R10 and M0 maps over a large volume were obtained
within seconds (36). The use of short TR is also essential in minimizing the vascular proton exchange rate
dependency of T1-rCBV (49). The use of short TE is
important in minimizing errors in C(t) estimations,
which result from the breakdown of the linear relationship between R1(t) and C(t) (see Eq. [3]) when T2* shortening becomes dominant after bolus injection (50). In
this study, we measured mean peak values of C(t) of
each tumor. The mean peak values of C(t) were below
1.4 mmol/L in all 15 tumors. The use of very short TRs
and TEs (TR ⱕ 7 msec and TE ⱕ 1.6 msec) renders the
attenuation of signal due to the terms related to T2*
contribution (e⫺TE/T2*) negligible where amplitudes of
C(t) are in the range of 0 –1.4 mmol/L. A computer
simulation based on Eqs. [1–3], with or without inclusion of T2* terms, was used prior to the study to confirm
that the error due to neglecting the contribution of T2*
changes is small. For example, underestimation of C(t)
was less than 1.6% in the worst modeled condition with
[Gd] of 1.4 mmol/L and other simulation parameters as
follows: natural T2* of tumor 80 msec, natural T1 of
tumor 1000 msec, TR 7 msec, TE 1.6 msec, T1 relaxivity of Gd-DTPA-BMA 4.39 s⫺1 mM⫺1, and T2 relaxivity
of Gd compounds 5.5 s⫺1 mM⫺1 at 37° and 1.5 T (51).
The fast acquisition of a high-quality R10 map is valuable on its own. Prolonged T1 relaxation times have
been found in all acoustic schwannomas in this study.
The finding is consistent with tumor appearance in
T1W images. All acoustic schwannomas are hypointense on T1W images, a result in agreement with the
literature (52).
The second improvement to the Tofts and Kermode
methods concerns the computation of the tissue concentration in contrast media. Both 3D R10 and M0
maps were used to convert the 4D contrast-enhanced
dynamic images S(x, y, z, t) to 4D absolute tissue concentration maps, C(x, y, z, t). The use of subtraction
between S(t) and S(0) instead of dividing S(t) by S(0) (see
Eq. [2]) considerably reduced the computation instability resulting from fluctuation in small values of S(0). In
addition, the use of a very short TE (1.1–1.6 msec) has
been effective in minimizing the competing susceptibility effects (32).
Finally, we significantly improved the signal-to-noise
ratio (SNR) of Cp(t) curves (35). The measurement of the
contrast concentration time course in the vascular
compartment can be affected by the contribution of
intravascular blood flow to the MRI signal, which is
most troublesome during first pass of contrast bolus
(39,53). Presaturation of in-flowing spins by use of a
separate presaturation slab can reduce inflow effects
but carries a significant time cost. In this study, we
have used the combination of a large volume selection
gradient and hard RF pulses to provide presaturation of
inflowing spins by positioning the volume in such way
that the proximal superior sagittal sinus runs through
the volume parallel to the slab selection gradient. This
allows measurement of the VIF from the distal superior
583
sagittal sinus with a high SNR, negligible inflow effects,
and no time penalty (35).
We compared the parametric maps of k, vl, and rCBV
qualitatively and quantitatively. In most tumors the
majority of the tissue had low permeability (k ⬍ 0.3
min⫺1), and k values in these pixels showed a positive
correlation with vl. The relationship does not persist
with higher values of k, allowing confident selection of a
cutoff value of 0.3 min⫺1 for pixels with high permeability (Fig. 4a). The presence of a large portion of pixels
above k ⫽ 0.3 min⫺1 was seen in two glioblastomas, one
transitional cell meningioma, and one angioblastic meningioma. It must be remembered that all the intraaxial gliomas in this study had at least 24 hours of
steroid therapy prior to investigation, which can be
expected to reduce both k and rCBV to some extent
(44). The spatial mismatch between k and vl (29) mirrors the increases of kep in these four tumors, which
have the largest ratio of k to vl in the patient group
(Table 3 and Fig. 5). These observations support the
findings of previous workers that increased endothelial
permeability is indicative of malignant grade and aggressive tumor behavior. The weakening of the correlation between vl and k for high k values results, in part,
from the contribution of increased perfusion in these
tumors when the observation time is short and the
impact of the first pass on the measurement of k is
significant (54,55).
To the best of our knowledge, this work is the first to
associate T2*W and T1W measurements in order to
generate endothelial permeability and perfusion maps
in the same study in a clinical environment. However,
several investigators (20,41) have previously compared
mean rCBV values of brain tumors measured from
T2*W dynamic data with mean rCBV values measured
from T1W dynamic data. Hacklander et al (20) compared mean T1-rCBV and T2*-rCBV values of brain
tumors and those of normal brain tissues. They found a
close correlation between mean T1-rCBV and T2*-rCBV
values of brain tumors, although Gd contrast agent
leaking from vessels into extravascular space in tumors
had the opposite effect on ⌬R1(t) and ⌬R2*(t) curves.
They also found that mean T1-rCBV and T2*-rCBV values of normal brain tissue were not correlated (20).
In this study, we found, not surprisingly, that the appearances of normal brain tissues in k maps, which are
calculated from T1W data sets, and rCBV maps, which
are calculated from T2*W data sets, are different, because
normal brain tissues are well perfused and their vessels
are not porous to Gd-DTPA-BMA. (The only exception is
the choroid plexus, which is highly permeable and normally has high k and high vl values.) Nevertheless, in
tumors, close correlations between mean k and mean
rCBV have been obtained. In addition, the comparison of
rCBV and k values demonstrated a close correlation at a
pixel-by-pixel level in all except four tumors. These findings serve to support the value of the combined technique
as well as the description of previous workers in human
and animal models of a close link between vascular density and increased permeability in areas of angiogenesis
(56). However, such a strong correlation may be partly
due to the impact of the first pass on the k measurements,
as discussed above (54,55).
584
This study has included two extra-axial brain tumors
(meningioma and schwannoma) and one intra-axial brain
tumor. Our intention was not to use the parametric mapping technique as a diagnostic tool for the separation of
the different types of tumors. In general, it is simple to
differentiate intra-axial from extra-axial tumors by standard imaging alone. One of advantages of quantitative
techniques such as endothelial permeability measurement is their ability to identify physiological parameters
that are dependent on local tissue characteristics, such
as vascular density and angiogenic activity. Indeed, we
have shown in this paper that 1) k, rCBV, and vl maps
obtained from both intra- and extra-axial tumors are
highly heterogeneous, eg, k and rCBV values were extremely low in necrotic regions, whereas at the rim of an
extra- or intra-vascular tumor values of k and rCBV are
often high; and 2) there is no significant differences in
mean values of endothelial permeability surface products
between three tumor types although, as expected, the
distribution volume of contrast agent does vary.
An important observation is the apparent decoupling
between perfusion and permeability in four extra- and
intra-axial tumors in this study (4/15), for which k and vl
are higher but rCBV values are lower than white (n ⫽ 3)
and gray matter (n ⫽ 1). The spatial correlation between
pixels in k and rCBV maps is poor (Fig. 6). Apparently, in
these four tumors areas of high contrast leakage do not
have significant increases of tumor blood volume. Such
decoupling between permeability and blood flow may be
of immediate significance, not only indicating inefficient
blood supply (57), but also reflecting the difference of time
scales involved in the different angiogenic processes. Antiangiogenic treatment (VEGF inhibition) has been shown
to reduce k in a period of hours, while CBV remains
unaltered (5,8,25,58). It may be postulated therefore that
a loss of colocation of k and rCBV associated with the low
tumor blood volume described here will be one initial
marker of successful inhibition of angiogenic drive. Evidence that this type of decoupling does occur is also seen
in the case illustrated in Fig. 3, where a meningioma is
associated with extensive permeability abnormalities of
apparently normal meninges. For this patient, the entire
calvarial meninges has increased k and vl, but no abnormality in rCBV is seen. The reasons for this discrepancy
are unknown, but it may represent unstable meninges or
extensive enplaque disease. Whatever the reasons, it is
clear that measures of k, vl, and rCBV are offering complementary information.
In conclusion, we have described a clinical MR imaging
protocol that allows quantitative mapping of endothelial
permeability, leakage space, and perfusion in one study,
based on T1W and T2*W dynamic data. The T1W data
provide high spatial resolution and extensive data coverage as well as vascular input information free of inflow
effects. The T2*W data provide good, but less extensive
tissue coverage with a high temporal resolution and appear to be free of residual relaxivity effects. Parametric
maps of intrinsic T1, endothelial permeability, leakage
space, and rCBV were obtained and compared qualitatively and quantitatively. Three different types of untreated primary brain tumors, ie, acoustic schwannoma,
meningioma, and glioma, have different contrast leakage
spaces and can be separated with a high confidence level
Zhu et al.
by their mean vl values. Qualitative and quantitative
analysis of k and rCBV maps shows that the two maps are
highly comparable for tumors with high k and high rCBV.
High-grade and more aggressive tumors are characterized
by higher tumor blood volume, high permeability, and a
high efflux rate constant with the spatial mismatch between k and vl. In addition, disagreement between tumor
appearances on k and rCBV maps occurs in some tumors
with low rCBV. Such a decoupling not only indicates that
the tumor has inefficient blood supply (57) but also may
reflect the continuous modulation of VEGF activities according to the metabolic demands in tumors (59). Although we have studied this technique in a restricted
sample of cerebral tumors, these findings suggest that the
methodology we propose in this paper for simultaneous
measurements of perfusion and permeability may thus
provide a technique for assessing the various vascular
hemodynamic variations associated with antiangiogenic
treatments in longitudinal and therapeutic trials.
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