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(Stroke. 2006;37:2162.)
© 2006 American Heart Association, Inc.

Research Reports

An Integrated Automated Analysis Method for Quantifying Vessel Stenosis and Plaque Burden From Carotid MRI Images

Combined Postprocessing of MRA and Vessel Wall MR

Isabel M. Adame, MSc; Patrick J.H. de Koning, MSc; Boudewijn P.F. Lelieveldt, PhD; Bruce A. Wasserman, MD; Johan H.C. Reiber, PhD Rob J. van der Geest, MSc

From the Division of Image Processing (I.M.A., P.J.H.d.K., B.P.F.L., J.H.C.R., R.J.v.d.G), Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands, and the Russell H. Morgan Department of Radiology and Radiological Sciences (B.A.W.), Johns Hopkins Hospital, Baltimore, Md.

Correspondence to Isabel Maria Adame, Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center (LUMC), Albinusdreef 2, PO Box 9600, 2300 RC Leiden, The Netherlands. E-mail I.M.Adame{at}lumc.nl

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Background and Purpose— We report the evaluation of a semiautomated method for in vivo assessment of the severity of carotid atherosclerosis with minimal user interaction that combines 3-dimensional contrast-enhanced magnetic resonance angiography (CE-MRA) and vessel wall magnetic resonance imaging (MRI).

Methods— Lumen and outer-wall contours were automatically detected, and stenosis and plaque burden were estimated. The method was tested on 22 subjects (352 postcontrast, T1-weighted cross sections and 3-dimensional CE-MRA).

Results— We observed good correlation with expert contours: lumen and outer-wall area (r=0.96) and the degree of stenosis (r=0.97).

Conclusions— The fusion of MRA and MRI reduces user interaction and improves contour detection, providing reproducible parameters to assess the severity of atherosclerosis.

Key Words: atherosclerosis • carotid artery • magnetic resonance angiography • magnetic resonance imaging

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Contrast-enhanced magnetic resonance angiography (CE-MRA) determines the location and severity of stenotic lesions,¹ whereas vessel wall magnetic resonance imaging (MRI) can depict outward remodeling and distinguish among plaque components.

The purpose of this work was to develop an automated method for quantitative assessment of atherosclerosis from the combined data of CE-MRA and postcontrast T1-weighted MRI images (PC-T1W MRI) of the vessel wall. Automated detection of luminal contours is performed in both datasets (2 initialization points are needed), followed by automated detection of the outer-wall contours on the PC-T1W MRI images. The degree of stenosis is derived from the luminal dimensions, whereas plaque burden is quantified from wall thickness and area measurements on the PC-T1W MRI images.

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Data for this analysis was part of the Atherosclerosis Risk in Communities study. The study was approved by the institutional review board. Twenty-two participants (aged 55 to 74 years) were randomly selected for this study. Three-dimensional (3D) CE-MRA (pixel size, 0.94 mm) and 16 axial PC-T1W MRI slices per subject (pixel size, 0.54 mm) of the carotid arteries were acquired on a 1.5-T whole-body scanner (EXCITE, GE Medical Systems) equipped with a 4-element phased-array carotid coil. Gadodiamide was administered intravenously (0.1 mmol/kg) at a rate of 2 mL/s.

Description of the Algorithm
MRA Segmentation
The user needs to place 2 points to define the vessel segment of interest. A 3D pathline is then automatically detected connecting these points and follows the center line of the vessel. The threshold-based vessel segmentation is based on the detected pathline. The threshold is derived from the maximum intensity at a particular cross section with a full-width 30% maximum criterion.

Lumen and Outer-Wall Contour Detection
The algorithm works on 2-dimensional (2D) images on a slice-by-slice basis. After automated registration, a minimum-cost approach (dynamic programming²) is performed on the vessel wall image to refine the lumen contour,³ which was obtained from MRA. A geometrical model (ellipse) is used to automatically trace the outer boundary of the vessel, as previously described.³ This contour is also refined by using dynamic programming.² To assess accuracy, all automatically detected contours were compared with manual contours drawn by radiologists blinded to the results of the algorithm.

Stenosis Estimation
The North American Symptomatic Carotid Endarterectomy Trial (NASCET) and the European Carotid Surgery Trial (ECST) criteria were used to measure the degree of stenosis.^4,5

Plaque Burden Estimation
Plaque index (percentage of wall volume relative to the volume of the whole vessel lumen and vessel wall inclusively) and vessel wall thickness were measured on the PC-T1W images. Wall volume was estimated in the segment covered by the PC-T1W slices by multiplying the average wall area by the slice thickness.

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Lumen and Outer-Wall Contour Detection
The descriptive statistics yielded by the automated method were 36.53±22.58 mm² for lumen area and 75.71±31.24 mm² for outer-wall area. Those measurements derived from manually traced contours were 40.90±23.45 mm² for lumen area and 73.57±31.09 mm² for outer-wall area. Those expert contours were used as a standard to assess the accuracy of the automated detection method.

A high correlation between automated and manual area measurements was observed for both the lumen and outer wall (r=0.96). The average paired difference between the automatic-manual measurement pairs was 4.35±5.59 mm² (11.74±15.08%; P=0.0002) for lumen area and –2.13±10.62 mm² (–2.97±14.80%; P=0.0001) for outer-wall area. The automated vessel wall contour detection (both on CE-MRA and PC-T1W-MRI) takes less than 20 seconds per subject.

Stenosis and Plaque Burden Measurements
Table 1 presents estimates of the severity of stenosis. The average paired difference between the ECST/NASCET combination measurement pairs was 7.42±8.71% (P=NS) and between the ECST/NASCET MRA, it was 7.58±11.14% (P=NS). Table 1 also shows values for plaque index and vessel wall thickness. In 6 cases (I-II, American Heart Association classification⁶) where CE-MRA found almost no stenosis, analysis of the PC-T1W MRI images showed that there was an abnormal vessel wall, corresponding to the early stages of atherosclerosis: low plaque burden and marginal luminal reduction (Figure, B). The rest of the subjects, who had higher degrees of stenosis (c, d; III-VI), had a high plaque index and increased vessel wall thickness. Table 2 presents statistics for interobserver and intraobserver reproducibility. The measurements from different analyses were also compared with Student’s 2-sided paired t test and showed no statistically significant difference (P>0.05).

View this table: [in this window] [in a new window]   TABLE 1. Stenosis and Plaque Burden Measurements

View larger version (135K): [in this window] [in a new window]   Automatically detected contours superimposed over the maximum-intensity projection (MIP). A, Severely stenotic vessel. B, Nonstenotic vessel with outward remodeling. CCA indicates common carotid artery; ICA, internal carotid artery.

View this table: [in this window] [in a new window]   TABLE 2. Reproducibility

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Only a few studies have been reported that combine different MRI sequences to characterize plaque composition.^7,8 In the present study, CE-MRA and PC-T1W MRI are combined to delineate the contours of the vessel wall. Reproducibility was higher for the combined approach (Table 2), which demonstrates that the combination of CE-MRA and vessel wall PC-T1W images reduces subjectivity from the process of stenosis estimation. Table 1 demonstrates that with the combined approach, the effect of outward remodeling can be observed, as different subjects showed a low stenosis percentage by CE-MRA (type c in Table 1 and panel B of the Figure), despite a large amount of plaque detected by PC-T1W MRI.

Although the average paired difference between automatic/manual measurement pairs was statistically significant (P<0.05), this is not assumed to be clinically relevant, as the differences are very small (lumen, 2 mm²; outer-wall, 4 mm²) in comparison with the average values (lumen, 37 mm²; outer-wall, 76 mm²).

In conclusion, the fusion of MRA and MRI reduces user interaction and improves contour detection, providing reproducible parameters to assess the severity of atherosclerosis. Nevertheless, further work needs to be done including more patients (multicenter study) and comparing the reported algorithm with ultrasound data.

	Acknowledgments

 
Source of Funding

This work was supported by the Dutch Science Foundation under innovational research incentive grant No. 016.026.017.

Disclosures

None.

Received March 17, 2006; accepted May 11, 2006.

	References

Top Abstract Introduction Subjects and Methods Results Discussion References

 
1. de Koning PJH, Schaap JA, Janssen JP, Westenberg JJM, van der Geest RJ, Reiber JHC. Automated segmentation and analysis of vascular structures in magnetic resonance angiographic images. Magn Reson Med. 2003; 50: 1189–1198.[CrossRef][Medline] [Order article via Infotrieve]

2. Sonka M, Hlavac V, Boyle R. Object recognition: fuzzy systems: In: Image Processing, Analysis, and Machine Vision, 2nd ed. Brooks/Cole Publishing Co; 1999: 336–343.

3. Adame IM, van der Geest RJ, Wasserman BA, Mohamed M, Reiber JHC, Lelieveldt BPF. Automatic segmentation and plaque characterization in atherosclerotic carotid artery MR images. MAGMA. 2004; 16: 227–234.[CrossRef][Medline] [Order article via Infotrieve]

4. North American Symptomatic Carotid Endarterectomy Trial Collaborators. Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. N Eng J Med. 1991; 325: 445–453.[Abstract]

5. European Carotid Surgery Trialists’ Collaborative Group. Randomised trial of endarterectomy for recently symptomatic carotid stenosis: final results of the MRC European Carotid Surgery Trial (ECST). Lancet. 1998; 351: 1379–1387.[CrossRef][Medline] [Order article via Infotrieve]

6. Stary HC, Chandler AB, Dinsmore RE. A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis: a report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association. Circulation. 1995; 92: 1355–1374.[Abstract/Free Full Text]

7. Mitsumori LM, Hatsukami TS, Ferguson MS, Kerwin WS, Cai J, Yuan C. In vivo accuracy of multisequence MR imaging for identifying unstable fibrous caps in advanced human carotid plaques. J Magn Reson Imaging. 2003; 17: 410–420.[CrossRef][Medline] [Order article via Infotrieve]

8. Clarke SE, Beletsky V, Hammond RR, Hegele RA, Rutt BK. Validation of automatically classified magnetic resonance images for carotid plaque compositional analysis. Stroke. 2006; 37: 93–97.[Abstract/Free Full Text]

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This Article Abstract Full Text (PDF) All Versions of this Article: 37/8/2162    most recent 01.STR.0000231648.74198.f7v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Adame, I. M. Articles by van der Geest, R. J. Search for Related Content PubMed PubMed Citation Articles by Adame, I. M. Articles by van der Geest, R. J. Related Collections Imaging Carotid Stenosis Angiography Computerized tomography and Magnetic Resonance Imaging