Quantitative Evaluation of Carotid Plaque Composition by
In Vivo MRI
T. Saam, M.S. Ferguson, V.L. Yarnykh, N. Takaya, D. Xu, N.L. Polissar, T.S. Hatsukami, C. Yuan
Objective—This study evaluates the ability of MRI to quantify all major carotid atherosclerotic plaque components in vivo.
Methods and Results—Thirty-one subjects scheduled for carotid endarterectomy were imaged with a 1.5T scanner using
time-of-flight–, T1-, proton density–, and T2-weighted images. A total of 214 MR imaging locations were matched to
corresponding histology sections. For MRI and histology, area measurements of the major plaque components such as
lipid-rich/necrotic core (LR/NC), calcification, loose matrix, and dense (fibrous) tissue were recorded as percentages of
the total wall area. Intraclass correlation coefficients (ICCs) were computed to determine intrareader and inter-reader
reproducibility. MRI measurements of plaque composition were statistically equivalent to those of histology for the
LR/NC (23.7 versus 20.3%; P⫽0.1), loose matrix (5.1 versus 6.3%; P⫽0.1), and dense (fibrous) tissue (66.3% versus
64%; P⫽0.4). Calcification differed significantly when measured as a percentage of wall area (9.4 versus 5%;
P⬍0.001). Intrareader and inter-reader reproducibility was good to excellent for all tissue components, with ICCs
ranging from 0.73 to 0.95.
Conclusions—MRI-based tissue quantification is accurate and reproducible. This application can be used in therapeutic
clinical trials and in prospective longitudinal studies to examine carotid atherosclerotic plaque progression and
regression. (Arterioscler Thromb Vasc Biol. 2005;25:234-239.)
Key Words: atherosclerosis 䡲 magnetic resonance imaging 䡲 carotid artery 䡲 plaque
A
therosclerosis and its thrombotic complications are the
leading cause of morbidity and mortality in industrialized countries. Therefore, the need for new medical therapies
and technology to treat and prevent cardiovascular atherosclerotic disease is enormous.
Accurate information of atherosclerotic plaque morphology and plaque composition is necessary to identify the
“vulnerable plaques” that are likely to cause embolic events.
A noninvasive imaging modality that could provide such
information would be an invaluable tool in studies of the
relationship between plaque composition/morphology and
plaque progression/regression. Furthermore, such imaging
techniques may be used in clinical trials to monitor the effects
of drugs on diseased arteries.
B-Mode ultrasonography has been used widely in plaque
progression/regression trials that involve either lipidlowering drugs or calcium channel blockers.1 However, this
modality is highly operator dependent, has limited soft tissue
contrast, and requires a large number of subjects to detect a
significant change in the intima-media thickness.1 Intravascular ultrasound (IVUS) is used increasingly in atherosclerosis regression/progression trials that study coronary arteries.2
Although IVUS is highly reproducible3 and provides tomographic information about the vessel wall,3 it is an invasive
procedure and has limited capacity to discriminate between
fibrous and fatty plaques.4
Recent publications5–11 have shown that in vivo MRI can
identify the main components of the atherosclerotic plaque
such as the lipid-rich/necrotic core (LR/NC), calcification,
and hemorrhage. In addition, morphological information
about the status of the fibrous cap12 and the American Heart
Association (AHA) lesion type13 can be obtained noninvasively. Moreover, the tomographic orientation of MRI enables the full cross-sectional view of the vessel wall, which
can be measured accurately14 and reproducibly.15 It has been
demonstrated that ex vivo MRI of endarterectomy specimen
is able to identify16 and quantify17,18 plaque components with
high diagnostic accuracy. This study is aimed at evaluating
the ability of MRI to quantify all major carotid atherosclerotic
plaque components in vivo, using histology as the standard of
reference.
Methods
Study Population
Data sets were obtained from 40 consecutively selected subjects
(mean age 68 years; SD 9 years; 2 females) scheduled for carotid
endarterectomy at either the University of Washington Medical
Center or Veterans Administration Puget Sound Health Care System.
Original received July 7, 2004; final version accepted October 26, 2004.
From the Department of Radiology (T.S., M.S.F., V.L.Y., N.T., D.X., C.Y.), University of Washington; Mountain-Whisper-Light Statistical Consulting
(N.L.P.); and the Department of Surgery, University of Washington, and VA Puget Sound Health Care System (T.S.H.), Seattle, Wash.
Correspondence to Chun Yuan, Professor, Department of Radiology, 1959 NE Pacific St, Box 357115, Seattle, WA 98195. E-mail
cyuan@u.washington.edu
© 2005 American Heart Association, Inc.
Arterioscler Thromb Vasc Biol. is available at http://www.atvbaha.org
DOI: 10.1161/01.ATV.0000149867.61851.31
234
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Saam et al
TABLE 1.
Tissue Classification Criteria
TOF
T1W
PDW
T2W
o
o/⫹
o/⫹
⫺/o
Quantitative Plaque Composition by MRI
235
literature review of atherosclerosis publications.5– 8,16,18 All signal
intensities (SIs) are relative to the adjacent sternocleidomastoid
muscle. Five tissue types were identified.
LR/NC with
No or little hemorrhage
Fresh hemorrhage
⫹
⫹
⫺/o
⫺/o
Recent hemorrhage
⫹
⫹
⫹
⫹
Calcification
⫺
⫺
⫺
⫺
Loose matrix
o
⫺/o
⫹
⫹
Dense (fibrous) tissue
⫺
o
o
o
The classification into the subgroups is based on the following SIs relative
to adjacent muscle: ⫹, hyperintense; o, isointense; ⫺, hypointense.
Nine subjects were excluded because of either poor image quality
(n⫽7) or the disruption of the histological specimen during excision
or processing (n⫽2), resulting in 31 evaluated subjects. Institutional
review boards of each facility approved the consent forms and study
protocols. Selected subjects either had symptomatic carotid disease
on the side of a ⬎50% internal carotid artery (ICA) stenosis (by
duplex), or an asymptomatic ⬎80% ICA stenosis. Subjects were
considered symptomatic if they had a history of transient ischemic
attack, amaurosis fugax, or stroke attributed to the distribution of the
index carotid artery within 120 days before the MRI scan. Subjects
underwent a carotid artery MRI examination within 1 week before
their surgical procedure.
1. The LR/NC is generally located in the bulk of the plaque and
is isointense to hyperintense on T1W and PDW images.
However, depending on the amount and age of hemorrhage
present, the LR/NC has varied SIs on T2W and TOF images
(Table 1).
2. Areas of hemorrhage were identified as described previously by Chu et al.11 Briefly, fresh intraplaque hemorrhage
appears as a hyperintense signal on T1W and TOF images
and as an isointense signal on T2W/PDW images. Recent
hemorrhage is identified by a hyperintense signal on all 4
contrast weightings. Area measurements of hemorrhage
collected in this study combine area measurements of fresh
and recent hemorrhage.
3. Calcification is characterized by defined areas with a
hypointense signal on all 4 weightings.
4. Loose matrix is hyperintense on T2WI and PDWI, isointense to hypointense on T1WI and isointense on TOF
images.
5. Dense fibrous tissue is hypointense on TOF images and
isointense on T1W, PDW, and T2W images. The area of
dense (fibrous) tissue was calculated by subtracting the
areas of LR/NC, calcification, and loose matrix from the
total wall area.
MRI Protocol
Histology Processing and Criteria
Subjects were imaged using 1.5T MR scanners (Signa Horizon
EchoSpeed; General Electric Health Care) and phased-array surface
coils (Pathway MRI, Inc.). Fast spin-echo (FSE)– based T1-weighted
(T1W), proton density–weighted (PDW), and T2-weighted (T2W)
images as well as time-of-flight (TOF) images of bilateral carotid
arteries were obtained using a previously published standardized
protocol.19 The scan was centered on the bifurcation of the operative
side. In most cases, this scan covered the complete atherosclerotic
plaque. Average scan time was 35 to 45 minutes. All images were
obtained using a field-of-view of 13 to 16 cm, matrix size of 256,
slice thickness of 2 mm (1 mm for 3D TOF) and 2 signal averages.
A zero-filled Fourier transform was used to reduce pixel size
(0.25⫻0.25 or 0.31⫻0.31 mm2) and to minimize partial-volume
artifacts.
Carotid endarterectomy specimens were fixed in 10% neutralbuffered formalin, decalcified, and embedded en bloc in paraffin.
Serial sections (10-m thick) were taken every 1 mm in the common
carotid artery, every 0.5 mm in the ICA throughout the length of the
specimen, and stained with hematoxylin-eosin and Mallory’s
Trichrome. Histological classification of the specimens was performed using criteria established by the AHA Committee on Vascular Lesions. A reviewer (M.S.F; unaware of the MRI data) used
standard histopathologic criteria to examine all histology
sections.21,22
Image Review
Before review, an image-quality rating (5-point scale: 1⫽poor and
5⫽excellent) for each contrast weighting was assigned to all MR
images9 by a radiologist (T.S.). Imaging locations with an image
quality ⱕ2 in T1W, T2W, or TOF (indicating severe blurring
attributable to subject motion or low signal-to-noise ratio) were
excluded from the study. TOF images were acquired every millimeter, whereas FSE images were acquired every 2 mm. Because of the
different slice distance and thus the higher number of TOF images,
only every second TOF image (the one with the best anatomic match
with the corresponding FSE images) was used. For each imaging
location, 4 contrast-weighted images were reviewed. Average review
time was 30 to 60 minutes per artery.
All MR images were examined by a radiologist (T.S.) who was
blinded to the histology findings. To assess intrareader and interreader reproducibility, MR images of 20 randomly selected subjects
were re-evaluated 9 months after the initial review by 2 reviewers
(T.S. and N.T.).
Area measurements of the lumen, outer wall, and the tissue
components were obtained using the custom-designed imaging
analysis tool QVAS.20 The outer wall boundary included lumen and
wall area. Wall area was calculated as the difference between outer
wall boundary and lumen area.
The tissue type classification scheme (Table 1) was derived from
our experience of reviewing MR images9,11 and an extensive
MRI and Histology Matching
The process of matching images to histology used landmarks such as
the relative distance from the common carotid bifurcation and gross
morphological features such as lumen size and shape, wall size and
shape, plaque configuration, as well as calcifications (Figure 1).
Shrinkage of the specimens because of processing is inconsistent
throughout the specimen, and multiple internal landmarks were
required for accurate matching.9 Histology was sectioned every 0.5
to 1 mm, and therefore, 1 to 4 histological sections could be matched
to 1 MR imaging location (slice thickness 2 mm). If ⬎1 histology
section matched to 1 MR imaging location, we calculated the mean
of the areas measured in the multiple histology sections and
compared it with the corresponding MR image. All MR image
locations that could be matched to corresponding histology sections
were included in the study. In total, 273 MR image locations (5 to 10
locations per subject; mean 6.9; SD⫽1.6; 1 artery per subject) were
matched and compared with the corresponding 600 histological sections
from serially sectioned endarterectomy specimens (Figure 1).
Data Analysis
Sensitivity, specificity, and Cohen’s kappa (; based on MRI
locations as the unit of observation) were calculated for all plaque
components independently of their size and separately for plaque
components ⬎2 mm2, using histology as a gold standard. For MRI
results to agree with histology results, we required that the components identified by MRI were present in the histology section that
was matched to the MR imaging location. The 2 mm2 threshold for
plaque components is comparable to the one used by Chiesa et al23
and takes into account the limited spatial resolution of in vivo MRI.
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236
Arterioscler Thromb Vasc Biol.
January 2005
plaque component was calculated as percentage of the vessel wall
[%plaque component⫽(area plaque component/wall area) 䡠100%].
The paired t test was used to compare the plaque composition as
percentage of the vessel wall between MRI and histology.
To determine intrareader and inter-reader reproducibility measurements, the intraclass correlation coefficient (ICC) with 95% CI was
calculated to measure the level of agreement between 2 measurements repeated within subjects compared with the variation in the
measurement across subjects.
Results
Sensitivity and Specificity
Figure 1. Example of histological validation of MRI at 4 consecutive locations spanning the bifurcation. Multiple histological
sections (at 0.5 to 1.0 mm separation) generally correspond to
each 2-mm-thick MR image. Contours have been drawn for
lumen (red), outer wall (cyan), LR/NC (yellow), calcification
(black), loose matrix (pink/white), and hemorrhage (orange). H&E
indicates hematoxylin-eosin.
Because of the multiple locations for each artery within a patient, and
the possibility of statistical dependence, the 95% confidence intervals (CIs) for sensitivity, specificity, and Cohen’s were calculated
with the bootstrap method (with 10 000 resamples).24
To lessen the impact of mismatch between MRI and histology and
to accommodate for possible statistical dependence of multiple
locations per artery, we used the artery as the unit of observation for
all calculations based on continuous variables, such as area measurements or percentages. Artery-based data were normalized by dividing the sum of all areas/percentages across locations by the number
of locations (or sections for histology) per subject. As an initial
estimate of accuracy of prediction of MRI area measurements
compared with histology, the mean difference (bias) between the
MRI and histology was calculated (according to the method described by Bland and Altman25 [Figure 2]) for the plaque
components.
Overall agreement between area measurements was estimated by
Pearson correlation coefficients (r) to accommodate the shrinkage
and distortion that occurs during histological processing. Each
Overall plaque component sensitivity ranged from 64% to
92% (Table 2). Plaque component specificity ranged from
65% to 86%. When only areas ⬎2 mm2 were considered,
sensitivity and specificity were 95% and 76% for the LR/NC,
84% and 91% for calcification, 87% and 84% for hemorrhage, and 79% and 77% for loose matrix. This resulted in
Cohen’s values of 0.73 for the LR/NC, 0.75 for calcification, 0.71 for hemorrhage, and 0.53 for loose matrix. Table 2
also indicates the number of locations displaying a specified
feature as determined by histology. Of note, 38 of 104 (37%)
of all loose matrix areas found in this study were ⬍2 mm2,
compared with only 13% of LR/NC areas, 21% of calcification areas, and 17% of hemorrhage areas.
Correlation Analysis
There was a strong correlation between MRI and histology area
measurements (mean per location) for lumen (r⫽0.81; P⬍0.001),
wall (r⫽0.84; P⬍0.001), outer wall (r⫽0.82; P⬍0.001), LR/NC
(r⫽0.75; P⬍0.001), calcification (r⫽0.74; P⬍0.001), and loose
matrix (r⫽0.70; P⬍0.001) (Table 3). There was a moderate
correlation between MRI and histology measurements of dense
(fibrous) tissue (r⫽0.55; P⫽0.001) and a moderate to strong
correlation between measurements of hemorrhage (r⫽0.66;
P⬍0.001).
Plaque Composition and Plaque Shrinkage
Figure 2. Bland–Altman plots for area measurements (mean per
location) of the plaque components (n⫽31 arteries). Histology
consistently underestimates the size of the LR/NC and of loose
fibrous matrix, with increasing bias and variance at larger sizes.
There is no bias for calcification measurements, whereas hemorrhagic areas are larger by histology, showing increasing bias
with size.
Figure 3 shows the mean prevalence of plaque components as
percent wall area of the 31 arteries measured by histology and
by MRI. Plaque composition calculated as percentage of the
vessel wall was comparable for MRI and histology for the
LR/NC (23.7 versus 20.3%; P⫽0.1), loose matrix (5.1 versus
6.3%; P⫽0.1), and dense (fibrous) tissue (66.3 versus 64%;
P⫽0.4). Calcifications appeared to be underestimated by
MRI (5.0% by MRI but 9.4% by histology; P⬍0.0001).
However, area measurements of calcification did not differ
significantly (mean area per location 2.7 mm2 by MRI and
3.5 mm2 by histology; mean difference⫾SD ⫺0.8⫾2.8 mm2;
P⫽0.1).
Using histology as the gold standard, MRI measurements
of lumen, wall, LR/NC, loose matrix, and dense (fibrous)
tissue were found to be consistently overestimated (Figure 2).
MRI showed a systematic underestimation of the hemorrhage
area (4.3 mm2 by MRI and 6.7 mm2 by histology; mean
difference⫾SD ⫺2.4⫾4.9 mm2; P⫽0.008). This underestimation increased with the size of hemorrhage measured
(Figure 2).
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Saam et al
TABLE 2.
Quantitative Plaque Composition by MRI
237
Sensitivity and Specificity Based on 214 Locations*
Tissue Type
Sensitivity/Specificity
(all areas)
Sensitivity/Specificity
(95% CI)
(areas⬎2 mm2 by histology)
LR/NC
92%/65% (n⫽165)
95%/76% (n⫽144)
0.73 (0.62–0.82)
Calcification
76%/86% (n⫽123)
84%/91% (n⫽97)
0.75 (0.66–0.84)
Hemorrhage
82%/77% (n⫽158)
87%/84% (n⫽131)
0.71 (0.61–0.80)
Loose matrix
64%/72% (n⫽104)
79%/77% (n⫽66)
0.53 (0.41–0.64)
*No. of locations with the specified tissues component is noted in parentheses.
Reproducibility
Intrareader reproducibility (ICC; 95% CI) was excellent for
area measurements of LR/NC (0.89; 0.75 to 0.95) and
calcification (0.9; 0.77 to 0.96), and good for hemorrhage
(0.74; 0.45 to 0.89) and for loose matrix (0.79; 0.54 to 0.91).
Inter-reader reproducibility (ICC; 95% CI) was excellent for
area measurements of LR/NC (0.92; 0.82 to 0.97) and
calcification (0.95; 0.88 to 0.98), and good for hemorrhage
(0.73; 0.44 to 0.88) and for loose matrix (0.79; 0.55 to 0.91).
Discussion
This study demonstrates the capabilities of in vivo MRI to
quantify all the major components of the advanced human
carotid atherosclerotic plaque. Each plaque component measured in this study provides unique information about the
status of atherosclerotic disease: LR/NC size may play an
important role in the pathogenesis of plaque rupture;26 intraplaque hemorrhage may stimulate the progression of atherosclerotic plaque;27 calcification can provide a general
indication of plaque burden;28 and loose matrix is involved in
coronary plaque healing in subjects with silent plaque rupture,29 which can result in increased stenosis.29 In vivo
identification and quantification of those atherosclerotic
plaque components enables us to study the underlying mechanisms of atherosclerotic disease in humans prospectively.
This may help us to (1) better understand basic concepts of
atherosclerotic disease progression/regression in humans, (2)
monitor antiatherosclerotic treatment effects more efficiently,
(3) develop new antiatherosclerotic treatment strategies, and
(4) improve and develop strategies to prevent atherosclerotic
disease.
MRI tissue characterization of complex human carotid
atherosclerotic plaques can be accomplished in vivo with
high sensitivity (79% to 95%) and moderate to high speci-
ficity (76% to 91%) for plaque components ⬎2 mm2 with
Cohen’s ranging from 0.53 to 0.75 (Table 2). Sensitivity
and specificity decreased slightly for all components if areas
⬍2 mm2 were included. Overall, sensitivity and specificity
were slightly lower than in an ex vivo study by Shinnar et al16
that used 9.4T MRI. This is most likely attributable to the
inferior spatial resolution of 1.5T MRI, and we expect
significant improvement of sensitivity and specificity with
improvements in hardware, particularly with the higher resolution expected from 3T MRI.
The correct identification of each plaque component is
based on signal intensity relative to the adjacent muscle. The
additional challenge for quantification of plaque components
is its accurate delineation of boundaries. The results of this
study suggest that both MRI reviewers had few difficulties in
delineating the boundaries of the LR/NC and calcification,
resulting in excellent intrareader and inter-reader reproducibility. Furthermore, the correlation of MRI to histology for
area measurements of the LR/NC and calcification were high
and (Table 3) comparable to those obtained for lumen and
wall areas.
Boundaries of hemorrhage are at times not clearly demarcated because hemorrhage diffuses into the LR/NC. Loose
matrix areas were generally small in size, and their often
TABLE 3. Artery-Based Correlation Between MRI and
Histology (Average Area per Location)
No. of
Measurements
r
P
Lumen
31
0.81
⬍0.001
Wall
31
0.84
⬍0.001
Outer wall
31
0.82
⬍0.001
LR/NC
31
0.75
⬍0.001
Calcification
31
0.74
⬍0.001
Hemorrhage
31
0.66
⬍0.001
Loose matrix
31
0.70
⬍0.001
Dense (fibrous) tissue
31
0.55
0.001
Figure 3. Plaque composition calculated as the percentage of
the vessel wall area, calculated per artery, and then averaged
across all arteries for MRI and histology.
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238
Arterioscler Thromb Vasc Biol.
January 2005
juxtaluminal location complicates differentiation from flow
artifacts. Therefore, intrareader and inter-reader reproducibility of the MRI-based review for hemorrhage and loose matrix
was slightly lower than that for LR/NC and calcification. The
correlation of MRI to histology for area measurements of
loose matrix and hemorrhage was lower than that for area
measurements of LR/NC and calcification.
Dense (fibrous) tissue was indirectly calculated by subtracting specified tissue types from the wall area. Therefore,
false-positive or false-negative classification of other tissue
types and any measurement errors of areas could bias the
calculation of areas of dense (fibrous) tissue. This might
explain the moderate correlation of MRI to histology for area
measurements of dense (fibrous) tissue areas.
Overall, the findings in this study for the quantification of
plaque components are comparable to a recent ex vivo MRI
study18 that used endarterectomy specimen to quantify plaque
components. The authors of the latter study reported good to
excellent results for correct classification of necrosis and
calcification but had difficulty with correctly classifying
loose connective tissue and fibrous tissue.18
The composition of the plaque calculated as proportion of
the vessel wall area did not differ significantly between MRI
and histology for the LR/NC (23.7 versus 20.3%; P⫽0.1),
loose matrix (5.1 versus 6.3%; P⫽0.1), and dense (fibrous)
tissue (66.3 versus 64%; P⫽0.4; Figure 3). Calcification as
percentage of the vessel wall appeared to be underestimated
by MRI (5.0% by MRI but 9.4% by histology; P⬍0.001).
However, area measurements of calcification did not differ
significantly (2.7 versus 3.5 mm2; P⫽0.1). There are 2
possible reasons for the underestimation of calcification by
MRI: (1) calcification may shrink less than other components
during histological processing, and (2) MRI possibly underestimates areas with hypointense signal because of signal
averaging of voxels that only partially contain calcification.
As demonstrated in Table 1, no single contrast weighting
was used to identify the different plaque components accurately. Rather, combined information from TOF, T1W, PDW,
and T2W images provided the most comprehensive evaluation. The criteria for identifying the LR/NC was slightly
modified from previously published work.9 Although we
found that the majority of the LR/NC regions are hyperintense on T1W images, LR/NCs containing little or no
hemorrhage appear more isointense than hyperintense on
T1W images. Furthermore, consistent with previous reports8,9,16 in the literature, many of the LR/NC regions
appeared hypointense to isointense on PDW and T2W images. However, we found that LR/NCs in other regions,
which contained recent hemorrhage by histology, were hyperintense on PDW and T2W images. This study provided
further evidence that LR/NCs can potentially display a
variety of different signal features, depending on the amount
and the age of hemorrhage within the LR/NC (Table 1).
image represents a composite of 200 histology sections. Thus,
in complex specimens containing lesions that may change
significantly in size or composition from section to section, it
can be difficult to obtain precise coregistration. To lessen the
impact of mismatch, correlation analysis was performed by
using the sum of areas per artery (for each plaque component)
divided by the number of locations rather than comparing
area measurements on a section-by-section basis. However,
for the calculation of sensitivity and specificity, locationbased data were used. Therefore, mismatch between MRI and
histology decreased any values given for sensitivity and
specificity compared with a calculation of these values on the
basis of perfect (true) registration.
Histology specimens were obtained from subjects undergoing carotid endarterectomy. Thus, the incidence of pathology was high, and the atherosclerotic lesions were advanced
and complex. It would be desirable to substantiate the results
by the examination of specimens with less severe atherosclerotic disease. Although specimens could be obtained by
autopsy studies, MRI in vivo scans are rarely available for
comparison, and ex vivo MRI scans of excised autopsy
tissues have other limitations, such as a potential change in
tissue contrast resulting from tissue degradation and
dehydration.16
Only MRI exams of at least average image quality were
considered for the review, resulting in the exclusion of 7
arteries from analysis. Because of recent improvements in the
imaging protocols, such as the time-efficient multislice double inversion recovery30 and the contrast-enhanced images
with quadruple inversion recovery,31 the number of exclusions attributable to poor image quality is lower in our current
studies and should further decline with improvements in
pulse sequence design and in hardware (eg, higher-field
MRI).
Conclusions
The results of this study revealed good agreement between in
vivo MRI and histology for quantitative measurements of the
main plaque components such as LR/NC, calcification, loose
matrix, and hemorrhage. Furthermore, the results have shown
that quantification of the main plaque components is feasible,
with good to excellent intrareader and inter-reader reproducibility. MRI-based tissue quantification can be used in therapeutic clinical trials and in prospective longitudinal studies
to examine carotid atherosclerotic plaque progression and
regression.
Acknowledgments
This work is supported in part by grants from the National Institutes
of Health R01HL56874 and R01HL072262 and from Pfizer, Inc. The
authors wish to acknowledge Andrew An Ho for his help in
preparing this manuscript.
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Quantitative Evaluation of Carotid Plaque Composition by In Vivo MRI
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Arterioscler Thromb Vasc Biol. 2005;25:234-239; originally published online November 4,
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doi: 10.1161/01.ATV.0000149867.61851.31
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