© 1999 American Heart Association, Inc.
Atherosclerosis and Lipoproteins |
The Diagnostic Accuracy of Ex Vivo MRI for Human Atherosclerotic Plaque Characterization
From The Cardiovascular Institute (M.S., J.T.F., Z.A.F., J.J.B., V.F.) and the Departments of Medicine (M.S., J.T.F., V.F.), Pathology (J.T.F.), Radiology (M.S., Z.A.F.) and Surgery (M.H., E.H.), The Mount Sinai School of Medicine (M.L., D.D.), New York, NY; and Children’s Hospital of Philadelphia (S.W.), Philadelphia, Penn.
Correspondence to Meir Shinnar, MD, PhD, Box 1030, The Mount Sinai Medical Center, One Gustave L. Levy Place, New York, NY 10029. E-mail meir_shinnar{at}smtplink.mssm.edu
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Abstract |
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Abstract—Recent evidence indicates that the type of atherosclerotic plaque, rather than the degree of obstruction to flow, is an important determinant of the risk of cardiovascular complications. In previous work, the feasibility of using MRI for the characterization of plaque components was shown. This study extends the previous work to all the plaque components and shows the accuracy of this method. Twenty-two human carotid endarterectomy specimens underwent ex vivo MRI and histopathological examination. Sixty-six cross sections were matched between MRI and histopathology. In each cross section, the presence or absence of plaque components were prospectively identified on the MRI images. The overall sensitivity and specificity for each tissue component were very high. Calcification and fibrocellular tissue were readily identified. Lipid core was also identifiable. However, thrombus was the plaque component for which MRI had the lowest sensitivity. A semiautomated algorithm was created to identify all major atherosclerotic plaque components. MRI can characterize carotid artery plaques with a high level of sensitivity and specificity. Application of these results in the clinical setting may be feasible in the near future.
Key Words: MRI • atherosclerosis • carotid endarterectomy
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Introduction |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Disruption of atherosclerotic plaques is the most frequent underlying cause of the unpredictable onset of acute thromboembolic vascular events including sudden death, myocardial infarction, unstable angina, stroke, transient cerebral ischemia, and peripheral thromboemboli.1 2 Although clinical risk factors for atherosclerosis help predict risk of these events, identification of patients with plaques vulnerable to disruption is not possible by angiography that only visualizes the lumen. There is therefore a need for an in vivo noninvasive method for characterizing atherosclerotic plaques and identifying the "vulnerable" plaque.
Previous work has shown that MRI can characterize both ex vivo3 4 5 6 7 and in vivo8 9 10 11 the composition of human atherosclerotic plaques. However, the sensitivity and specificity of MRI have not been determined.
This study reports the development of high-resolution MRI criteria for the ex vivo tissue characterization of human carotid atherosclerotic plaques and their sensitivity and specificity in comparison with histopathology. Using these criteria, a semiautomatic segmentation algorithm is developed for characterizing the constituents of an atherosclerotic plaque.
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Methods |
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Specimens
Human carotid endarterectomy specimens were studied. Specimens were obtained fresh and intact from the operating room, washed in phosphate buffered saline, grossly described, and samples taken for routine surgical pathology. The remaining 1- to 2-cm-long segments were flash frozen at -80°C until imaged. On the day of imaging, the specimens were placed in saline and slowly warmed to 37°C in a water bath. The artery was placed in either a 10- or 12-mm MR tube (Wilmad Glass) using the smallest possible tube for a given specimen. Care was taken to remove any air bubbles. Previous studies have shown no change in the T1 and T2 of atheromatous plaques under these conditions of freezing and rewarming.5
MRI
Specimens were imaged on a Bruker AM 400 wide bore (89 mm)
9.4T magnet with a gradient insert (ID 75 mm, maximal strength
50G/cm), controlled by an ASPECT 3000 spectrometer. The tube with the
specimen was placed in either a 10- or 12-mm radiofrequency probe and
positioned inside the magnet. During imaging, specimens were maintained
at 37°C. Before imaging each specimen, the magnetic field was made
homogeneous (shimmed).12 After initial scout
images, cross-sectional spin echo images of the plaque were obtained.
Four acquisitions (NEX) were averaged for each image. The field
of view was 12.4 mm. The images were acquired as a 256x256 pixel
matrix for an in-plane resolution of 48.3 µ. Images were obtained
every 1 mm with a slice thickness of 500 µ and an interslice
distance of 500 µ. For each cross section, 6 different spin echo
images, using different repetition times (TR) and echo times (TE), were
obtained (Table 1
). A spin echo
diffusion-weighted image13 14 was also obtained for each
cross section. The resolution and NEX were the same as for the spin
echo images. The TR was 2000 ms and TE 30 ms. Diffusion was measured in
1 axial direction. The diffusion parameters were a gradient
strength of 16.67 gauss/cm, duration of the gradient pulses (
) of
9.69 ms, and a separation of the gradient pulses (
) of 12.69 ms,
resulting in a diffusion weighting (b) of 1766
sec/mm2. The signal intensity of the
diffusion-weighted image was reduced from the signal intensity of the
spin echo image with the same TR and TE by e-bD,
where D is the diffusion coefficient of water. For comparison,
in most clinical diffusion-weighted imaging sequences, b is
approximately 1000 sec/mm2. A gradient strength
of 10 gauss/cm (b=636 sec/mm2) was also used for
comparison in some samples. The effect of a fat suppression pulse,
calibrated using the spectrum of the entire sample, was tested in
4 samples.
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Histopathology
At the conclusion of the MRI experiment, specimens were fixed in
10% buffered formalin. Specimens were decalcified in 1% formic acid,
serially cross sectioned every 2 mm, embedded in paraffin,
sectioned at 5 µm, and stained with a combined Mason elastic
(CME) and hematoxylin and eosin (H&E).
Matching of MRI and Histopathology
The MRI images and the histopathological slides were matched
using the known location and distance between MRI and between
histopathological cross sections. The gross morphology of each MRI
cross section was then used to optimize the match with a corresponding
histopathology cross section. We did not account for shrinkage of the
specimen, as it can vary across specimens. However, morphologically,
there seemed to be a good match between the corresponding sections. We
tried to match as many images and slides per specimen as possible.
Each histopathological specimen was divided into 4 quadrants, trying to put components such as lipid-rich necrotic core and thrombus near the middle of a quadrant. The matched MRI images were then divided into corresponding quadrants.
Image Processing and Analysis
MRI cross sections were transferred from the Bruker spectrometer
to a Macintosh workstation and analyzed with IDL (RSI).
The 7 data sets collected for each cross section were converted and
displayed by normalization and scaling to an 8-bit gray scale. In
addition, 3 parametric images (T2, T1, and diffusion
coefficient) were derived from the original MRI data sets. Inverse T2
was calculated from linear regression of the log of the data sets with
TR=2000 ms, TE=13, 30, and 50 ms against the echo times and displayed
as the T2 value. T1 was calculated by a nonlinear fitting routine using
data sets with TE=13 ms, TR=300, 700, 1000, and 2000 ms. Diffusion
coefficient of water was calculated from the log of the ratio of the
diffusion-weighted and the spin echo data sets with TR 2000 ms and TE
30 ms.
MRI criteria were developed for each plaque component using the first 5
specimens (Table 2). The developed
criteria were applied by one observer (M.S.) blinded to the
histopathology to determine the presence or absence of the following
plaque components in each of the matched MRI cross sections: 1)
calcification, 2) fibrocellular tissue, 3) fibrocellular tissue with
extracellular lipid, 4) lipid-rich core, 5) thrombus, and 6) a fibrous
cap, defined as a region of fibrocellular tissue with or without
calcification overlying a lipid-rich core. On the basis of our
histopathological slides, it was not possible to date the thrombus, and
we included both organized and unorganized thrombi. The quadrants in
which each component was located were noted.
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At a separate time, the matching histopathological cross sections were classified using standard histopathological criteria.15 16 Each of the plaque components can be reliably identified from a CME stained slide. Sudan black staining is not necessary to identify the lipid core, which is readily distinguishable from thrombus.15 This matching was done without knowledge of the MRI data. The quadrants in which a given component was located were noted. Then the MRI and histopathological classification of matched sections were cross tabulated.
Image Segmentation
Based on the developed MRI criteria, a semiautomatic
segmentation routine was developed using the 3 parametric
images and the proton density image. To determine that a plaque
component was present, multiple adjacent pixels had to satisfy the
criteria for that component because of signal noise. The effective
resolution was therefore less than the resolution of the original data
sets. User input was required to eliminate the surrounding background
and to separate saline from fibrocellular tissue.
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Results |
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Twenty-two human carotid endarterectomy specimens were imaged. In these specimens, the MRI and histopathology of 66 different cross sections were matched. The number of matched cross sections per artery averaged 3 and ranged from 1 to 4. Given the advanced nature of plaques that require endarterectomy, most plaques were complex (AHA type VI)16 and had most or all plaque components.
MRI Criteria
For the first 5 samples, we used previously published
criteria,3 which emphasize the use of T2-weighted images
(TE 50 ms) to identify lipid core. However, to classify the plaque
components accurately, these criteria were modified in 2 different
ways.
1) Identification of thrombus. T2-weighted images did not allow for the accurate detection of thrombus. Initial results on the first specimens showed that diffusion-weighted MRI does allow for the detection of thrombus on the basis of the restriction of diffusion of water in the clot.17 18
2) Distinguishing fibrocellular tissue with lipid from lipid core.
T2-weighted images did not distinguish lipid-rich core from
fibrocellular areas containing lipid. However, the use of 2 different
echo times (30 and 50 ms) did distinguish the 2 (Figure 1).
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The use of fat suppression had minimal effect on the image (Figure 2). Table 2
summarizes the
criteria used for classifying plaque components.
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Image Segmentation
The MRI criteria allowed for the development of a semiautomatic
segmentation routine (Table 3; Figures 1 and 3). First, the proton
density image was examined for calcification, ie, pixels with signal
<4x background noise. Second, lipid core was identified by a T2<17
ms on the parametric T2 image. Third, thrombus was detected by
a diffusion coefficient of <3x10-6
cm2/sec. Fibrocellular areas containing lipid
were identified by a T2 between 17 and 20 ms, and fibrocellular areas
without lipid were characterized by a T2>20 ms on the
parametric T2 image. T1 values were helpful in distinguishing
fibrocellular areas from the bathing saline solution.
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MRI Images
Figures 1 and 3 show the MRI and derived images, the
histopathology, and the segmentation image for 2
representative carotid
endarterectomy cross sections, respectively. The
MRI images show different levels of contrast among the various plaque
components. However, the proton density and the T1-weighted images show
similar levels of contrast. Figure 4
shows 8 MRI cross sections, 1 mm apart, through the entire human
carotid endarterectomy specimen used for Figure 1. The rapid change in the plaque composition over small
distances is seen.
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Sensitivity and Specificity
Table 4 summarizes results of the
sensitivity and specificity testing of the final MRI criteria used for
evaluating all 66 cross sections. The overall sensitivity and
specificity are very high. Thrombus was the plaque component for which
MRI had the lowest sensitivity. On review of the images, most thrombi
that were not identified by MRI were adjacent to calcified tissue
making the area dark on all images. The appearance of thrombus was
sufficiently variable that it could not be reliably identified
without using the diffusion-weighted image.
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Location of Components
For the purposes of this study, we did not require that the
components be identified by MRI and those identified by histopathology
be at the identical location in the image to conclude that the 2
methods agreed. This was because of methodological issues of
identifying the same location with these different techniques. As a
measure of correspondence, we divided each MRI and histopathological
specimen into quadrants. The components identified on the MRI images
and the histopathology appeared in corresponding quadrants.
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Discussion |
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Our results show that MRI tissue characterization of complex human atherosclerotic plaques can be accomplished ex vivo with a high degree of sensitivity and specificity. This investigation validates and extends previous studies.3 5 8 This study demonstrates that MRI can identify and characterize all major atherosclerotic plaque components and defines the MRI sequences and criteria needed for ex vivo plaque characterization. In vivo application of the MRI criteria may allow for the noninvasive, prospective identification of patients with plaques at high risk of clinical events and for the testing and institution of appropriate anti-atherosclerotic therapy.
There are several methodological issues and concerns that arise from this study. A previous study5 suggested that a T2-weighted image and an image to look at calcifications, either T1-weighted or proton density–weighted, would allow for the full characterization of atherosclerotic plaques. This suggests that full characterization can be done with a double echo sequence, obtaining both a T2-weighted image and a proton density–weighted image. This study shows that for accurate, full classification, the following 4 MRI images are required: 1) proton density image, 2) T2-weighted (TE=50 ms) image, 3) partially T2-weighted (TE=30 ms) image, and 4) a diffusion-weighted image.
The proton density of water is a main determinant of signal intensity
in MRI images. Calcified tissues, which have very little water, appear
dark on all MRI images. However, fibrocellular tissue and thrombus may
also be relatively dark on proton density images. Specifically, these
plaque components appear darker on T2-weighted images than their actual
T2 would suggest. One way to compensate for this problem is the use of
parametric images. Actual T2, calculated from the signal
intensity at 3 different TEs, allows for separating the effects of
proton density from relaxation. However, calculating T2 from only 3
points is prone to error. Because these components, ie, fibrocellular
and thrombus, may have relatively low signal, the low signal to noise
creates even greater errors in the estimation of T2
parameters. However, in spite of the limitations of
calculated relaxation parameters, as is evident by the
noise in the images of the relaxation parameters (Figures 1 and 3), they were still useful in the semiautomatic
segmentation routine. The application of techniques for the rapid
determination of T2 of tissues may prove clinically useful in this
regard.19
It has been suggested that a T2-weighted image alone can reliably
identify the necrotic core of an atherosclerotic plaque.5
This finding needs to be qualified because fibrocellular areas with
extracellular lipid are also black on T2-weighted (TE=50 ms) images
(see Figure 1). This study demonstrates that use of a partially
T2-weighted (TE=30 ms) image helps to differentiate between these 2
components (Table 2).
This study confirms preliminary data that diffusion-weighted MRI is a good technique for identifying thrombus and hemorrhage in plaque.17 18 Thrombus appears as a bright area in diffusion-weighted images. However, areas of thrombus adjacent to foci of calcification gave signals so low that thrombus did not appear bright on the diffusion-weighted sequence and was thus lost to identification. Complicating the identification of thrombus is the suggestion that acute thrombus may not have this characteristic bright appearance on diffusion-weighted images.18
In some plaques, the diffusion-weighted image showed a bright spot that
did not correspond to thrombus (Figure 3). These occurred in
areas where the T2-weighted images were relatively bright. The
parametric image of the diffusion coefficient was used in any
doubtful cases.
It might be difficult to implement the precise diffusion sequence used in this article on a clinical scanner because of the extremely strong gradients used. Furthermore, ex vivo samples may differ in their diffusion properties from in vivo samples, where the intact cell wall presents a barrier to diffusion. However, imaging with a diffusion weighting even stronger than our partially diffusion-weighted images are obtained routinely, suggesting that one can obtain diffusion-weighted images of plaques in vivo. There are still significant problems in obtaining high-resolution diffusion images in vivo, because of the problems of motion and low signal to noise of these images.
In this study, T1-weighted images added little additional information beyond that available from the proton density and T2-weighted images. For the plaque components of interest, the tissue contrast of the T1-weighted and the proton density–weighted images were similar.
Fat suppression pulses had little impact on the images obtained in this
ex vivo study. This confirms previous results,5 which
showed that lipids constitutes only a small portion (
11%) of the
MRI signal from the lipid-rich atheromatous core.
However, fat suppression is necessary for in vivo imaging because
periarterial fat can induce chemical shift
artifacts.20
Image Segmentation
The image segmentation routine developed in this study requires
minimal user input. Its ability to segment the image and correlate with
histopathology is further proof of the ability of MRI to identify
different plaque components (Figures 1 and 3). An
automated analysis program has several advantages: it
eliminates observer bias; it allows for consistent, objective
analysis across many samples; and segmentation allows one to
display and summarize information culled from several different MRI
images of the same tissue section. Similar segmentation routines should
be applicable to in vivo MRI not only for arterial plaques
but also for other tissues.
The user input consisted primarily of the following: First, it was difficult to distinguish automatically between the surrounding saline and the fibrocellular components. The proper setting of the parameters, especially T1, had to be done individually. This did not affect our ability to automatically distinguish the other components from fibrocellular tissue or saline. Second, user input was also necessary to determine that an area corresponding to a given plaque component was sufficiently large (at least 3x3 pixels), such that it did not represent artifact. However, this part can also be automated. Third, segmentation requires only a few minutes to perform for each cross-sectional data set.
Limitations
Specimens were obtained from patients undergoing carotid
endarterectomy. Thus the incidence of pathology was
high and the atherosclerotic lesions were advanced. All samples, for
example, had some calcification. Table 4 shows that the 95%
confidence limits for the specificity of some of the components is very
broad. Therefore, these results need to be substantiated by examination
of specimens with less severe atherosclerotic plaques, not just those
that come to endarterectomy. A
representative study may therefore require an autopsy
study, which has many other significant limitations, such as the
degradation of the sample.
Ideally, endarterectomy specimens should be studied fresh from the body and maintained at 37°C because many techniques of tissue preservation are known to change MRI characteristics. For example, fixation in formalin, used in some studies,4 20 is known to change the relaxation parameters (T1 and T2).21 For logistical reasons, the specimens used in this study were frozen and then rewarmed to 37°C before imaging. Preliminary data suggests that there is no significant change in the MRI parameters under these conditions.5 However, the lipids in an atherosclerotic plaque are known to undergo a partially irreversible phase transition when cooled.22 It is unclear whether or not this phase transition of the lipids in the specimens affected the MRI appearance of the plaque.
The classification of the different components is not done with completely independent measurements, as the same images are used to classify all the components. However, this lack of independence is not a significant problem and may even accentuate any errors. Thus, if a lipid-rich fibrocellular area is misclassified as lipid-rich core, both sensitivity of the technique for lipid-rich fibrocellular areas and the specificity for lipid-rich core are decreased. Therefore, the lack of independence may magnify the effect of any mistakes.
Finally, the MRI criteria derived from this study may not apply directly to the clinical setting typically done in a 1.5 T rather than in a 9.4 T magnet. Because T1 and T2 change with the field strength, further study is needed to determine the appropriate TE for in vivo application.
We chose 9.4 T for several reasons. First, the previous studies on T2-weighted imaging were done at 9.4 T. Second, we thought we would need the high resolution and signal to noise obtainable at 9.4 T. Our images were obtained with a 48-µ in-plane resolution. We do not yet know the resolution needed for plaque characterization in vivo. However, in our study, we ignored plaque components that were only 1 or 2 pixels large, suggesting that we may not need this high resolution. Third, we lacked the appropriate instrumentation for doing temperature controlled, high-resolution imaging on our clinical scanner.
The characterization of the atherosclerotic plaque is assuming greater importance in determining the risk of cardiovascular events. This study shows that by using multiple MRI sequences and images, atherosclerotic plaques can be completely characterized. Furthermore, the sensitivity and specificity are high. The next step is to prospectively use these sequences in vivo for the characterization of atherosclerotic plaques in patients. This may eventually allow for the noninvasive evaluation of the risk of clinical cardiovascular events in the individual patient.
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Acknowledgments |
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This work was supported in part by NIH HL54469 and funds from The Cardiovascular Institute at Mount Sinai Medical Center. We thank Veronica Gulle for her histopathological expertise.
Received June 15, 1998; accepted April 8, 1999.
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S. E. Clarke, V. Beletsky, R. R. Hammond, R. A. Hegele, and B. K. Rutt Validation of Automatically Classified Magnetic Resonance Images for Carotid Plaque Compositional Analysis Stroke, January 1, 2006; 37(1): 93 - 97. [Abstract] [Full Text] [PDF]
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 J. Cai, T. S. Hatsukami, M. S. Ferguson, W. S. Kerwin, T. Saam, B. Chu, N. Takaya, N. L. Polissar, and C. Yuan In Vivo Quantitative Measurement of Intact Fibrous Cap and Lipid-Rich Necrotic Core Size in Atherosclerotic Carotid Plaque: Comparison of High-Resolution, Contrast-Enhanced Magnetic Resonance Imaging and Histology Circulation, November 29, 2005; 112(22): 3437 - 3444. [Abstract] [Full Text] [PDF]
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B. A. Wasserman, R. J. Wityk, H. H. Trout III, and R. Virmani Low-Grade Carotid Stenosis: Looking Beyond the Lumen With MRI Stroke, November 1, 2005; 36(11): 2504 - 2513. [Abstract] [Full Text] [PDF]
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E. Larose, Y. Yeghiazarians, P. Libby, E.K. Yucel, M. Aikawa, D. F. Kacher, E. Aikawa, S. Kinlay, F. J. Schoen, A. P. Selwyn, et al. Characterization of Human Atherosclerotic Plaques by Intravascular Magnetic Resonance Imaging Circulation, October 11, 2005; 112(15): 2324 - 2331. [Abstract] [Full Text] [PDF]
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C. L. Higgins, S. A. Marvel, and J. D. Morrisett Quantification of Calcification in Atherosclerotic Lesions Arterioscler Thromb Vasc Biol, August 1, 2005; 25(8): 1567 - 1576. [Abstract] [Full Text] [PDF]
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R. L. Wolf, S. L. Wehrli, A. M. Popescu, J. H. Woo, H. K. Song, A. C. Wright, E. R. Mohler III, J. D. Harding, E. L. Zager, R. M. Fairman, et al. Mineral Volume and Morphology in Carotid Plaque Specimens Using High-Resolution MRI and CT Arterioscler Thromb Vasc Biol, August 1, 2005; 25(8): 1729 - 1735. [Abstract] [Full Text] [PDF]
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M. Ouhlous, H. Z. Flach, T. T. de Weert, J. M. Hendriks, M. R. H. M. van Sambeek, D. W. J. Dippel, P. M. T. Pattynama, and A. van der Lugt Carotid Plaque Composition and Cerebral Infarction: MR Imaging Study AJNR Am. J. Neuroradiol., May 1, 2005; 26(5): 1044 - 1049. [Abstract] [Full Text] [PDF]
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J.K. Lovett, J.N.E. Redgrave, and P.M. Rothwell A Critical Appraisal of the Performance, Reporting, and Interpretation of Studies Comparing Carotid Plaque Imaging With Histology Stroke, May 1, 2005; 36(5): 1085 - 1091. [Abstract] [Full Text] [PDF]
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V. C. Cappendijk, K. B. J. M. Cleutjens, A. G. H. Kessels, S. Heeneman, G. W. H. Schurink, R. J. T. J. Welten, W. H. Mess, M. J. A. P. Daemen, J. M. A. van Engelshoven, and M. E. Kooi Assessment of Human Atherosclerotic Carotid Plaque Components with Multisequence MR Imaging: Initial Experience Radiology, February 1, 2005; 234(2): 487 - 492. [Abstract] [Full Text] [PDF]
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T. Saam, M.S. Ferguson, V.L. Yarnykh, N. Takaya, D. Xu, N.L. Polissar, T.S. Hatsukami, and C. Yuan Quantitative Evaluation of Carotid Plaque Composition by In Vivo MRI Arterioscler Thromb Vasc Biol, January 1, 2005; 25(1): 234 - 239. [Abstract] [Full Text] [PDF]
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J. Viereck, F. L. Ruberg, Y. Qiao, A. S. Perez, K. Detwiller, M. Johnstone, and J. A. Hamilton MRI of Atherothrombosis Associated With Plaque Rupture Arterioscler Thromb Vasc Biol, January 1, 2005; 25(1): 240 - 245. [Abstract] [Full Text] [PDF]
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M. A. McAteer, J. E. Schneider, K. Clarke, S. Neubauer, K. M. Channon, and R. P. Choudhury Quantification and 3D Reconstruction of Atherosclerotic Plaque Components in Apolipoprotein E Knockout Mice Using Ex Vivo High-Resolution MRI Arterioscler Thromb Vasc Biol, December 1, 2004; 24(12): 2384 - 2390. [Abstract] [Full Text] [PDF]
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A. Kampschulte, M.S. Ferguson, W.S. Kerwin, N. L. Polissar, B. Chu, T. Saam, T.S. Hatsukami, and C. Yuan Differentiation of Intraplaque Versus Juxtaluminal Hemorrhage/Thrombus in Advanced Human Carotid Atherosclerotic Lesions by In Vivo Magnetic Resonance Imaging Circulation, November 16, 2004; 110(20): 3239 - 3244. [Abstract] [Full Text] [PDF]
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 D. J. Pennell, U. P. Sechtem, C. B. Higgins, W. J. Manning, G. M. Pohost, F. E. Rademakers, A. C. van Rossum, L. J. Shaw, and E. K. Yucel Clinical indications for cardiovascular magnetic resonance (CMR): Consensus Panel report Eur. Heart J., November 1, 2004; 25(21): 1940 - 1965. [Full Text] [PDF]
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B. Chu, T. S. Hatsukami, N. L. Polissar, X.-Q. Zhao, L. W. Kraiss, D. L. Parker, J. C. Waterton, J. S. Raichlen, W. Hamar, and C. Yuan Determination of Carotid Artery Atherosclerotic Lesion Type and Distribution in Hypercholesterolemic Patients With Moderate Carotid Stenosis Using Noninvasive Magnetic Resonance Imaging Stroke, November 1, 2004; 35(11): 2444 - 2448. [Abstract] [Full Text] [PDF]
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E. Trogan, Z. A. Fayad, V. V. Itskovich, J.-G. S. Aguinaldo, V. Mani, J. T. Fallon, I. Chereshnev, and E. A. Fisher Serial Studies of Mouse Atherosclerosis by In Vivo Magnetic Resonance Imaging Detect Lesion Regression After Correction of Dyslipidemia Arterioscler Thromb Vasc Biol, September 1, 2004; 24(9): 1714 - 1719. [Abstract] [Full Text] [PDF]
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 S. Dhawan, K. C. Dharmashankar, and T. Tak Role of Magnetic Resonance Imaging in Visualizing Coronary Arteries Clin. Med. Res., August 1, 2004; 2(3): 173 - 179. [Abstract] [Full Text] [PDF]
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 S.E. Nissen Identifying patients at risk: novel diagnostic techniques Eur. Heart J. Suppl., July 1, 2004; 6(suppl_C): C15 - C20. [Abstract] [Full Text] [PDF]
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J. W. Olin, J. A. Kaufman, D. A. Bluemke, R. O. Bonow, M. D. Gerhard, M. R. Jaff, G. D. Rubin, and W. Hall Atherosclerotic Vascular Disease Conference: Writing Group IV: Imaging Circulation, June 1, 2004; 109(21): 2626 - 2633. [Full Text] [PDF]
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C. M. Kramer, L. A. Cerilli, K. Hagspiel, J. M. DiMaria, F. H. Epstein, and J. A. Kern Magnetic Resonance Imaging Identifies the Fibrous Cap in Atherosclerotic Abdominal Aortic Aneurysm Circulation, March 2, 2004; 109(8): 1016 - 1021. [Abstract] [Full Text] [PDF]
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S. Zhang, J. Cai, Y. Luo, C. Han, N. L. Polissar, T. S. Hatsukami, and C. Yuan Measurement of Carotid Wall Volume and Maximum Area with Contrast-enhanced 3D MR Imaging: Initial Observations Radiology, July 1, 2003; 228(1): 200 - 205. [Abstract] [Full Text] [PDF]
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R. E. Murphy, A. R. Moody, P. S. Morgan, A. L. Martel, G.S. Delay, S. Allder, S. T. MacSweeney, W. G. Tennant, J. Gladman, J. Lowe, et al. Prevalence of Complicated Carotid Atheroma as Detected by Magnetic Resonance Direct Thrombus Imaging in Patients With Suspected Carotid Artery Stenosis and Previous Acute Cerebral Ischemia Circulation, June 24, 2003; 107(24): 3053 - 3058. [Abstract] [Full Text] [PDF]
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M.E. Kooi, V.C. Cappendijk, K.B.J.M. Cleutjens, A.G.H. Kessels, P.J.E.H.M. Kitslaar, M. Borgers, P.M. Frederik, M.J.A.P. Daemen, and J.M.A. van Engelshoven Accumulation of Ultrasmall Superparamagnetic Particles of Iron Oxide in Human Atherosclerotic Plaques Can Be Detected by In Vivo Magnetic Resonance Imaging Circulation, May 20, 2003; 107(19): 2453 - 2458. [Abstract] [Full Text] [PDF]
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G. J. Blake, R. J. Ostfeld, E. K. Yucel, N. Varo, U. Schonbeck, M. A. Blake, M. Gerhard, P. M. Ridker, P. Libby, and R. T. Lee Soluble CD40 Ligand Levels Indicate Lipid Accumulation in Carotid Atheroma: An In Vivo Study With High-Resolution MRI Arterioscler Thromb Vasc Biol, January 1, 2003; 23(1): e11 - 14. [Abstract] [Full Text] [PDF]
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H. Yabushita, B. E. Bouma, S. L. Houser, H. T. Aretz, I.-K. Jang, K. H. Schlendorf, C. R. Kauffman, M. Shishkov, D.-H. Kang, E. F. Halpern, et al. Characterization of Human Atherosclerosis by Optical Coherence Tomography Circulation, September 24, 2002; 106(13): 1640 - 1645. [Abstract] [Full Text] [PDF]
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J.-M. Cai, T. S. Hatsukami, M. S. Ferguson, R. Small, N. L. Polissar, and C. Yuan Classification of Human Carotid Atherosclerotic Lesions With In Vivo Multicontrast Magnetic Resonance Imaging Circulation, September 10, 2002; 106(11): 1368 - 1373. [Abstract] [Full Text] [PDF]
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R. P. Choudhury, V. Fuster, J. J. Badimon, E. A. Fisher, and Z. A. Fayad MRI and Characterization of Atherosclerotic Plaque: Emerging Applications and Molecular Imaging Arterioscler Thromb Vasc Biol, July 1, 2002; 22(7): 1065 - 1074. [Abstract] [Full Text] [PDF]
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B. A. Wasserman, W. I. Smith, H. H. Trout III, R. O. Cannon III, R. S. Balaban, and A. E. Arai Carotid Artery Atherosclerosis: In Vivo Morphologic Characterization with Gadolinium-enhanced Double-oblique MR Imaging—Initial Results Radiology, May 1, 2002; 223(2): 566 - 573. [Abstract] [Full Text] [PDF]
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C. Di Mario Vulnerable plaques: let's stop sinking on submerged icebergs? Eur. Heart J., March 1, 2002; 23(5): 349 - 351. [Full Text] [PDF]
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C. Yuan, S.-x. Zhang, N. L. Polissar, D. Echelard, G. Ortiz, J. W. Davis, E. Ellington, M. S. Ferguson, and T. S. Hatsukami Identification of Fibrous Cap Rupture With Magnetic Resonance Imaging Is Highly Associated With Recent Transient Ischemic Attack or Stroke Circulation, January 15, 2002; 105(2): 181 - 185. [Abstract] [Full Text] [PDF]
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B. D. Coombs, J. H. Rapp, P. C. Ursell, L. M. Reilly, and D. Saloner Structure of Plaque at Carotid Bifurcation: High-Resolution MRI With Histological Correlation Stroke, November 1, 2001; 32(11): 2516 - 2521. [Abstract] [Full Text] [PDF]
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C. Yuan, L. M. Mitsumori, M. S. Ferguson, N. L. Polissar, D. Echelard, G. Ortiz, R. Small, J. W. Davies, W. S. Kerwin, and T. S. Hatsukami In Vivo Accuracy of Multispectral Magnetic Resonance Imaging for Identifying Lipid-Rich Necrotic Cores and Intraplaque Hemorrhage in Advanced Human Carotid Plaques Circulation, October 23, 2001; 104(17): 2051 - 2056. [Abstract] [Full Text] [PDF]
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X.-Q. Zhao, C. Yuan, T. S. Hatsukami, E. H. Frechette, X.-J. Kang, K. R. Maravilla, and B. G. Brown Effects of Prolonged Intensive Lipid-Lowering Therapy on the Characteristics of Carotid Atherosclerotic Plaques In Vivo by MRI: A Case-Control Study Arterioscler Thromb Vasc Biol, October 1, 2001; 21(10): 1623 - 1629. [Abstract] [Full Text] [PDF]
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 S. M. Schwartz, T. S. Hatsukami, and C. Yuan Molecular Markers, Fibrous Cap Rupture, and the Vulnerable Plaque: New Experimental Opportunities Circ. Res., September 14, 2001; 89(6): 471 - 473. [Full Text] [PDF]
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Z. A. Fayad and V. Fuster Clinical Imaging of the High-Risk or Vulnerable Atherosclerotic Plaque Circ. Res., August 17, 2001; 89(4): 305 - 316. [Abstract] [Full Text] [PDF]
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P. Schoenhagen, K. M. Ziada, D. G. Vince, S. E. Nissen, and E. M. Tuzcu Arterial remodeling and coronary artery disease: the concept of "dilated" versus "obstructive" coronary atherosclerosis J. Am. Coll. Cardiol., August 1, 2001; 38(2): 297 - 306. [Abstract] [Full Text] [PDF]
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M.-L. M. Gronholdt, B. G. Nordestgaard, T. V. Schroeder, S. Vorstrup, and H. Sillesen Ultrasonic Echolucent Carotid Plaques Predict Future Strokes Circulation, July 3, 2001; 104(1): 68 - 73. [Abstract] [Full Text] [PDF]
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L. Marcu, M. C. Fishbein, J.-M. I. Maarek, and W. S. Grundfest Discrimination of Human Coronary Artery Atherosclerotic Lipid-Rich Lesions by Time-Resolved Laser-Induced Fluorescence Spectroscopy Arterioscler Thromb Vasc Biol, July 1, 2001; 21(7): 1244 - 1250. [Abstract] [Full Text] [PDF]
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J.-M. Serfaty, L. Chaabane, A. Tabib, J.-M. Chevallier, A. Briguet, and P. C. Douek Atherosclerotic Plaques: Classification and Characterization with T2-weighted High-Spatial-Resolution MR Imaging—An in Vitro Study Radiology, May 1, 2001; 219(2): 403 - 410. [Abstract] [Full Text]
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S. Schroeder, A. F. Kopp, A. Baumbach, C. Meisner, A. Kuettner, C. Georg, B. Ohnesorge, C. Herdeg, C. D. Claussen, and K. R. Karsch Noninvasive detection and evaluation of atherosclerotic coronary plaques with multislice computed tomography J. Am. Coll. Cardiol., April 1, 2001; 37(5): 1430 - 1435. [Abstract] [Full Text] [PDF]
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C. Napoli and W. Palinski Maternal hypercholesterolemia during pregnancy influences the later devolopment of atherosclerosis: clinical and pathogenic implications Eur. Heart J., January 1, 2001; 22(1): 4 - 9. [PDF]
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 W. Moshage, S. Achenbach, and W. G. Daniel Novel approaches to the non-invasive diagnosis of coronary-artery disease Nephrol. Dial. Transplant., January 1, 2001; 16(1): 21 - 28. [Full Text] [PDF]
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 A. Fischer, D. E Gutstein, Z. A Fayad, and V. Fuster Predicting plaque rupture: enhancing diagnosis and clinical decision-making in coronary artery disease Vascular Medicine, August 1, 2000; 5(3): 163 - 172. [Abstract] [PDF]
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Z. A. Fayad, V. Fuster, J. T. Fallon, T. Jayasundera, S. G. Worthley, G. Helft, J. G. Aguinaldo, J. J. Badimon, and S. K. Sharma Noninvasive In Vivo Human Coronary Artery Lumen and Wall Imaging Using Black-Blood Magnetic Resonance Imaging Circulation, August 1, 2000; 102(5): 506 - 510. [Abstract] [Full Text] [PDF]
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Z. A. Fayad, T. Nahar, J. T. Fallon, M. Goldman, J. G. Aguinaldo, J. J. Badimon, M. Shinnar, J. H. Chesebro, and V. Fuster In Vivo Magnetic Resonance Evaluation of Atherosclerotic Plaques in the Human Thoracic Aorta : A Comparison With Transesophageal Echocardiography Circulation, May 30, 2000; 101(21): 2503 - 2509. [Abstract] [Full Text] [PDF]
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C. Yuan, L. M. Mitsumori, K. W. Beach, and K. R. Maravilla Carotid Atherosclerotic Plaque: Noninvasive MR Characterization and Identification of Vulnerable Lesions Radiology, November 1, 2001; 221(2): 285 - 299. [Abstract] [Full Text] [PDF]
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G. Helft, S. G. Worthley, V. Fuster, Z. A. Fayad, A. G. Zaman, R. Corti, J. T. Fallon, and J. J. Badimon Progression and Regression of Atherosclerotic Lesions: Monitoring With Serial Noninvasive Magnetic Resonance Imaging Circulation, February 26, 2002; 105(8): 993 - 998. [Abstract] [Full Text] [PDF]
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