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. Author manuscript; available in PMC: 2010 Oct 1.
Published in final edited form as: Stroke. 2009 Jul 23;40(10):3258–3263. doi: 10.1161/STROKEAHA.109.558676

Sites of Rupture in Human Atherosclerotic Carotid Plaques are Associated with High Structural Stresses: an In Vivo MRI-Based 3D Fluid-Structure Interaction Study

Dalin Tang 1,*, Zhongzhao Teng 1,*, Gador Canton 2, Chun Yang 3, Marina Ferguson 2, Xueying Huang 1,*, Jie Zheng 4, Pamela K Woodard 4, Chun Yuan 2
PMCID: PMC2753753  NIHMSID: NIHMS139853  PMID: 19628799

Abstract

Background and Purpose

It has been hypothesized that high structural stress in atherosclerotic plaques at critical sites may contribute to plaque disruption. To test that hypothesis, 3D fluid-structure interaction models were constructed based on in vivo magnetic resonance imaging (MRI) data of human atherosclerotic carotid plaques to assess structural stress behaviors of plaques with and without rupture.

Methods

In vivo MRI data of carotid plaques from 12 patients scheduled for endarterectomy were acquired for model reconstruction. Histology confirmed that five of the twelve plaques had rupture. Plaque Wall Stress (PWS) and Flow Maximum Shear Stress (FMSS) were extracted from all nodal points on the lumen surface of each plaque for analysis. A Critical PWS (CPWS) (maximum of PWS values from all possible vulnerable sites) was determined for each plaque.

Results

Mean plaque wall stress (PWS) from all ulcer nodes in ruptured plaques was 86% higher than that from all non-ulcer nodes (123.0 vs. 66.3 kPa, p<0.0001). Mean FMSS from all ulcer nodes in ruptured plaques was 170% higher than that from all non-ulcer nodes (38.9 vs. 14.4 dyn/cm2, p<0.0001). Mean CPWS from the 5 ruptured plaques was 126% higher than that from the 7 non-ruptured ones (247.3 vs. 108 kPa, p=0.0016 using log transformation).

Conclusion

The results of this study show that plaques with prior ruptures are associated with higher critical stress conditions: both at ulcer sites and when compared with non-ruptured plaques. With further validations, plaque stress analysis may provide additional stress indicators helpful for image-based plaque vulnerability assessment.

Keywords: carotid artery, atherosclerosis, MRI, plaque rupture, fluid structure interaction

1. Introduction

Atherosclerotic plaque vulnerability assessment and the ability to predict possible future plaque rupture are of vital importance for early diagnosis, prevention and treatment of cardiovascular disease. It has been hypothesized that critical stress conditions (maximum principal stress values at critical sites where rupture is likely to occur) in the plaque may be closely related to plaque rupture and can be combined with current image-based assessment techniques for more accurate plaque evaluation and vulnerability assessment.1-2 A major challenge for all available plaque assessment schemes is the lack of realistic lesion information based on in vivo patient data where both ruptured and non-ruptured plaques are compared and analyzed. In vivo patient image data showing rupture verified by histological information provided by excised specimens could help to establish an in vivo benchmark for image-based and/or stress-based assessment schemes.

Currently, screening and diagnosis of patients with atherosclerotic plaques is based on medical images such as magnetic resonance image (MRI), ultrasound, intravascular ultrasound (IVUS), and computerized tomography (CT). In particular, MRI techniques have been shown to be able to non-invasively characterize plaque morphology and composition, and in particular, to identify fibrous cap rupture.3-12 Chu et al. performed serial high-spatial-resolution, multisequence MRI studies identifying fibrous cap rupture and a penetrating ulcer into carotid atherosclerotic plaque.10

In addition to plaque morphology, mechanical forces, considered to be potential rupture triggers, and plaque composition are key factors in the rupture process and should be considered in an integrated way for plaque assessment.12 It has been hypothesized that image-based computational fluid-structure interaction (FSI) models with accurate stress and flow information may have potential to improve the current histology- and image-based plaque assessment schemes.1-2 MRI-based computational simulations for plaque rupture investigation and vulnerability assessment have been conducted by several groups with interesting and significant results reported.13-26 Steinman et al. investigated effects of plaque geometrical features on flow behaviors using realistic plaque geometries.13-14 Holzapfel et al. introduced multi-layer anisotropic plaque models and showed that stress predictions from their models could be different from single-layer isotropic models by 50%-200% or more.15-16 Lee et al. and Loree et al. studied effects of plaque cap thickness and material properties on stress level and plaque rupture.17-18 Vengrenyuk and Weinbaum et al. demonstrated that plaque cap with microcalcification inclusions are associated with elevated stress levels and may be related to plaque rupture. 19-20 Influence of curvature dynamics and cyclic bending on coronary plaque stress and flow behaviors was investigated by Prosi et al. and Tang et al. 21-22. Li et al. performed structure analysis based on in vivo MRI of carotid arteries and found that wall stress was higher in symptomatic patients than in asymptomatic patients.23 Groen and Wentzel et al. in a case report indicated that plaque rupture in the carotid artery was localized at the high shear stress region.24 In the introduction of image-based FSI models, Tang et al. demonstrated that fluid-structure interactions, axial stretch, cyclic bending, and a pre-shrink process for in vivo imaged-based models have considerable effects on accuracy of stress/strain predictions.1-2,22,25-26

To date, evidence from 3D FSI models based on in vivo MRI data of ruptured human atherosclerotic carotid plaques linking plaque rupture to corresponding stress and flow shear stress conditions is still lacking in the current literature due to the difficulties in obtaining in vivo MRI data with indication of plaque ruptures and constructing 3D FSI models. In this study, 3D computational multi-component models with FSI were constructed based on in vivo MRI data of carotid plaques acquired from 12 patients with advanced lesions. Disruption of the plaque surface considered in this paper was defined as the presence of ulcerated regions in the MR images. We evaluated plaque wall maximum principal stress (PWS) and flow maximum shear stress (FMSS) values on all lumen surface nodal points to study the associations between mechanical stress and flow shear stress conditions and plaque rupture sites.

2. Methods

2.1 MRI Data Acquisition

3D in vivo MR images of human atherosclerotic carotid plaques from 12 patients scheduled for carotid endarterectomy (age: 54-82, mean=68, 11M, 1F; 5 plaques with ulcerations which are indication of prior ruptures, 7 plaques without prior rupture) were acquired before the surgery at the University of Washington, Seattle (n=11) and Washington University, St. Louis (n=1), and after informed consent. The institutional review boards of each institution approved the consent forms and study protocols. Patients were imaged with a 1.5-T MR scanner (Signa Horizon EchoSpeed, General Electric Health Care). Pre-contrast MR images included double-inversion-recovery T1-, proton density-, and T2-weighted (T1W, PDW, and T2W; respectively), and time-of-flight (TOF). Post-contrast double-inversion-recovery T1W MR images of carotid arteries were obtained with a previously published standardized protocol (T1W: repetition time/echo time/inversion time, 800/10/650 ms; PDW and T2W: repetition time/echo time, 3RR, 20/40 ms; TOF: repetition time/echo time, 23/3.8 ms).7-10 A gadolinium-based contrast agent (Omniscan, Amersham Health), 0.1 mmol/kg (0.2 mL/kg) body weight, was injected intravenously with a power injector, and acquisition of postcontrast T1W images occurred 6 to 10 minutes after injection. All images were obtained with the following parameters: 16 cm field-of-view, 256×256 matrix size, and 2 mm slice thickness. After interpolation, the in-plane resolution is 0.31×0.31mm2. The longitudinal coverage covered the bulk region of each carotid lesion.

MR images were segmented using custom made analysis tools (CASCADE27 developed at the Vascular Imaging Laboratory at University of Washington, and APIA, from Washington University) to identify lipid-rich necrotic core (LRNC), loose matrix (LM), calcification (Ca), thrombus, hemorrhage and the presence or absence of ulcer, as the indicator of disruption4-5,7-8,10. Disruption was confirmed by the histology after matching to the MR images.9 The histology-validated MRI analysis of the status of the fibrous cap resulted in 5 arteries with ulcerated plaques. Figure 1 shows a plaque slice with TOF, T1-, and PD weighted MR and histological images showing an ulcer. Figure 2 gives a set of 10 slices of PD-weighted MR images and segmented contour plots showing 3D re-construction process.

Figure 1.

Figure 1

A human carotid plaque sample with corresponding TOF, T1-, and PD weighted MR and histological images showing an ulcer.

Figure 2.

Figure 2

PD-weighted MR images and segmented contour plots showing the 3D re-construction process.

2.2. 3D Re-Construction, Shrink-Stretch Process, and Mesh Generation Process

Under the in vivo condition, the artery is axially stretched and pressurized, thus axial and circumferential shrinking was applied a priori to generate the start-shape for the computational simulation. The shrinkage in axial direction was 9% so that the vessel would regain its in vivo length with a 10% axial stretch. Circumferential shrinkage for lumen and outer wall was determined so that: 1) total mass volume was conserved; 2) plaque geometry after 10% axial stretch and pressurization had the best match with the original in vivo geometry. Because advanced plaques have complex irregular geometries with component inclusions that are challenging for mesh generation, a component-fitting mesh generation technique was developed to generate mesh for these models. Using this technique, the 3D plaque domain was divided into hundreds of small “volumes” to curve-fit the irregular plaque geometry with plaque component inclusions.22 Ulcers were replaced with lipid cores covered by a thin cap. This is our best approximation for the pre-rupture geometry of these plaque samples. 3D surfaces, volumes and computational mesh were created with ADINA (ADINA R & D, Inc., Watertown, MA, USA) computing environment. Each plaque model required about 3600 small volumes to be created to fit the shape of wall and adjacent components, and about 1000 small volumes were needed for the corresponding fluid domain.

2.3. FSI Computational Model

Both the artery wall and plaque components were assumed to be hyperelastic, isotropic, incompressible and homogeneous. Blood flow was assumed to be laminar, Newtonian, viscous and incompressible. The incompressible Navier-Stokes equations with arbitrary Lagrangian-Eulerian (ALE) formulation were used as the governing equations. A no-slip condition between all interfaces was assumed. Patient-specific systolic and diastolic pressure conditions from the last hospital admission were used to impose pulsatile pressure conditions at the inlet and outlet of the artery. The modified Mooney-Rivlin model was used to describe the material property of each component in the plaque,25-26,28

W=c1(I13)+c2(I23)+D1[exp(D2(I13))1], (1)

Where I1 = ∑Cii, I2 = ½ [I12 − CijCij] are the first and second strain invariants, C =[Cij] = XTX is the right Cauchy-Green deformation tensor, X=[Xij] = [∂xi/∂aj], {xi} is current position, {ai} is original position, ci and Di are material parameters chosen to match experimental measurement.25-26 In this paper, the following parameter values were chosen: vessel/fibrous cap, c1=36.8KPa, D1=14.4KPa, D2=2; calcification, c1=368KPa, D1=144KPa, D2=2.0; lipid core/hemorrhage/ulcers, c1=2KPa, D1=2KPa, D2=1.5; loose matrix, c1=18.4KPa, D1=7.2 kPa; D2=1.5. c2 = 0 was set for all materials.

2.4. Solution Method

The coupled FSI plaque models were solved by a commercial finiteelement package ADINA. This software uses unstructured finite element methods for both fluid and solid models. Nonlinear incremental iterative procedures were used to handle fluid-structure interactions. The governing finite element equations for both the solid and fluid models were solved by the Newton-Raphson iteration method. Details of the models and methods are given in Tang et al.25-26 and Bathe.28

2.5. Plaque Stress/Strain and Flow Shear Stress Data Extraction

Data for PWS and FMSS were extracted from 3D FSI solutions for all integral nodes (total nodes: structure=7028; fluid=6418) on lumen surfaces of 12 plaque models for analysis. Each node was assigned a node type (lipid, calcification, ulcer, loose matrix, and wall if the node was not covering any component) according to its location and nearby component. Mean values of PWS, PWSN and FMSS for each node type from each patient were calculated for analysis and comparisons. A t-test (P<0.05) was used to determine if there was a significant difference between the data compared.

Using previously published critical site selection method and critical stress approach,2 a critical PWS (CPWS) is determined for each plaque. CPWS was defined as the maximum of PWS values from all possible vulnerable sites which included sites with local stress concentration (local maximum), especially where a thin cap was covering a plaque component, but excluded healthy sites where rupture is unlikely, even if a local stress maximum occurred there.

3. Results

Figure 3 gives an overview of solution features from 3D FSI models using the plaque sample given in Fig. 2. Maximum principal stress (Stress-P1) on stacked cross-section slices, a bifurcation cut (B-cut) surface and a cross-section cut (at S7 location) were plotted showing the site of rupture. Maximum Stress-P1 was observed at the site of rupture. To analyze mechanical conditions (structural stress and flow shear stress) on the lumen surface corresponding to different tissue types and to identify differences between ulcer and non-ulcer nodes, data were extracted from the full 3D FSI solutions and mean values of PWS and FMSS of ulcer, lipid core, calcification (Ca), wall, and all non-ulcer nodes are summarized in Tables 1-2. kPa and dyn/cm2 were used as the units for PWS and FMSS, respectively (1 kPa=104 dyn/cm2).

Figure 3.

Figure 3

3D plaque wall (structural) stress plots showing maximum critical stress at the ulcer site.

Table 1.

Summary of mean plaque wall stress (PWS) values of ulcer, lipid core, calcification, wall, all nodes, and all non-ulcer nodes for the 12 plaque samples. The p-values are for the ulcer vs. non-ulcer comparisons. Unit: kPa.

Case Ulcer Wall Lipid Ca All Nodes All Non-
Ulcer
mean # mean # mean # mean # mean # mean # P-value
P1 157.8 18 73.3 110 99.8 65 90.1 193 83.1 175 <0.0001
P2 87.3 116 71.7 410 60.0 15 30.4 23 70.5 626 66.7 510 <0.0001
P3 141.6 7 62.3 431 59.0 167 60.0 16 62.3 621 61.4 614 0.0237
P4 104.5 28 70.6 428 76.2 182 50.6 33 74.3 772 73.1 744 0.0008
P5 164.1 95 72.0 537 59.2 87 82.7 719 70.3 624 <0.0001
All 5
Rup
123.0 264 69.5 1916 70.3 516 46.2 72 74.0 2931 66.3 2667
P6 33.8 276 35.4 147 35.0 442 35.0 442
P7 74.6 393 79.0 114 45.2 15 74.2 531 74.2 531
P8 57.0 426 74.6 73 59.6 499 59.6 499
P9 71.0 440 80.6 83 72.6 523 72.6 523
P10 63.7 424 70.0 130 65.2 554 65.2 554
P11 77.2 743 49.6 226 70.8 969 70.8 969
P12 60.0 484 64.5 95 60.7 579 60.7 579
7 No
Rup
65.2 3186 60.8 868 45.2 15 64.1 4097 64.1 4097
All
12 P
123.0 264 66.8 5102 64.3 1384 46.0 87 68.0 7028 66.1 6764 <0.0001

Table 2.

Summary of mean flow maximum shear stress (FMSS) values of ulcer, lipid core, calcification, wall, all nodes, and all non-ulcer nodes for the 12 plaque samples. P-values are for ulcer vs. non-ulcer comparisons. Unit: dyn/cm2.

Case Ulcer Wall Lipid Ca All Nodes All Non-
Ulcer
mean # mean # mean # mean # mean # mean # P-value
P1 39.3 5 30.6 109 42.4 46 34.2 160 34.1 155 0.6848
P2 54.7 89 8.7 260 2.5 10 1.0 16 17.3 416 7.1 327 <0.0001
P3 23.6 6 13.9 431 22.4 170 11.8 15 16.3 622 16.2 616 <0.0001
P4 49.0 27 12.8 425 6.5 181 7.2 30 11.3 764 9.9 737 <0.0001
P5 10.8 57 14.6 362 41.2 53 17.1 472 18.0 415 <0.0001
All 5
Rup
38.9 184 14.0 1587 19.9 460 6.7 61 16.2 2434 14.4 2250
P6 7.3 229 8.2 110 7.5 350 7.5 350
P7 11.9 307 25.6 105 7.8 8 15.1 426 15.1 426
P8 11.1 426 13.3 73 11.4 499 11.4 499
P9 11.4 448 9.0 83 11.0 531 11.0 531
P10 7.4 435 9.6 130 7.9 565 7.9 565
P11 59.4 1039 65.6 226 60.5 1265 60.5 1265
P12 15.0 288 19.0 60 15.7 348 15.7 348
7 No
Rup
26.6 3172 28.6 787 7.8 8 26.9 3984 26.9 3984
All
12 P
38.9 184 22.4 4759 25.4 1247 6.8 69 22.8 6418 22.4 6234 <0.0001

3.1. Ulcer Nodes Are Associated with Higher PWS Values Compared to Non-Ulcer Nodes

For the 5 plaques with ulcers, the mean PWS value from all ulcer nodes was 123.0 kPa, which is 86% higher than the mean PWS value (66.3 kPa) from all non-ulcer nodes (p<0.0001). The differences between the individual mean PWS values from ulcer and non-ulcer nodes for each of the 5 plaque samples were also statistically significant. Excluding the ulcer nodes, the differences between the mean PWS values from lipid, wall and all non-ulcer nodes are modest (<5%).

3.2. Overall Results Showing Ulcer Nodes Are Associated with Higher Flow Maximum Shear Stress (FMSS)

Table 2 Indicated that mean FMSS from all ulcer nodes (38.9 dyn/cm2) was 170% higher than that (14.4 dyn/cm2) from all non-ulcer nodes for the 5 plaques with ulcerations. Individual plaque mean FMSS data showed mixed results. Three plaques showed mean FMSS values on ulcer nodes were greater than those on non-ulcer nodes (23.6, 49.0, 54.7) vs. (16.2, 9.90, 7.13), p<0.0001; one plaque showed mean FMSS (10.8) on ulcer nodes was less than mean FMSS (18.0) on non-ulcer nodes (p<0.0001); another plaque showed mean FMSS (39.3) was greater than that (34.1) on non-ulcer nodes, but the difference was not statistically significant (p=0.6848).

3.3. Comparison of Plaques with Ruptures (n=5) and Plaques without Rupture (n=7)

Table 1 and Table 2 list mean values of PWS and FMSS of the two groups of plaques on: different node types; all nodes combined; and all non ulcer nodes combined. Excluding the ulcer nodes, differences of those mean values between the ruptured and non-ruptured groups are not significant.

3.4. Critical Stress Values from Plaques with Rupture Were Significantly Higher Than Those from Non-Ruptured Plaques

CPWS values from the 5 plaques with rupture were (unit: kPa): 457.85, 241.90, 195.51, 161.92, and 179.14 with an average=247.3±121.4. CPWS values from the 7 non-ruptured plaques were: 81.68, 129.31, 103.68, 143.70, 94.32, 102.88, and 108.03 with average = 109.1±21.0. The average CPWS from the 5 ruptured cases was 126% higher than that from the 7 non-ruptured cases (p=0.0016 using log transformation).

4. Discussion

4.1. In Vivo Evidence Linking Localized Stress Conditions to Plaque Vulnerability

This is the first study with a relative large in vivo patient data in a mechanical analysis. The results showed evidence that high plaque stresses are indeed linked to sites of plaque rupture. The lesions studied were all advanced complex plaques, five of them having an ulcerated surface. Evidence linking local critical stress conditions with actual plaque rupture sites based on in vivo data is of great importance in establishing the bench mark for atherosclerotic plaque mechanical assessment.2

4.2. The Role of Flow Shear Stress in Plaque Rupture Process

Numerous studies have attempted to determine a solid link between flow shear stress and plaque progression and rupture. Groen and Wentzel et al. reported a follow-up case study showing high flow shear stress region was associated with site of plaque rupture.24 The overall results from this study are supporting their findings. Individual differences suggest that larger-scale study is needed to further clarify the role of FMSS in the rupture process. Considering that critical structural stress is at about 100-400 kPa level and FMSS is only at < 10 Pa, contribution from flow shear stress acting as a rupture trigger may be much smaller than that from structural stress. However, high flow shear stress may act on the lumen surface in a long-term process leading to endothelial dysfunction and lumen surface weakening.

4.3. Limitations

The models used in the study made use of patient-specific plaque morphology and systolic/ diastolic pressure conditions with the intent for potential patient-screening applications. However, the models lack patient-specific vessel and plaque component material properties due to an inability to measure these material properties in vivo using the current imaging format. Accurate anisotropic vessel and plaque component material property measurements will improve the model predictions. The accuracy of the computational models when compared to histology is also compromised by the limited in vivo MRI resolution (0.31×0.31×2.0 mm3).

It is known that blood flow in carotid arteries with severe stenosis may become disturbed or even turbulent.29 While many flow and geometric parameters may affect flow behavior, considerable turbulence can be present when stenosis severity (based on the diameter measurement) exceeds 70%.29 Stenosis severity (based on diameter) of the 12 plaques was <40% in 7 cases, < 55% in 4 cases, < 75% in one case. While the choice of laminar flow model may be justified for this study, as only one case exceeded the stenosis degree that guarantees turbulent Reynolds number, a turbulent model will be considered in future studies for better predictions. Local disturbed flow behaviors were complex and may be reported in future studies.

5. Conclusion

The results from 3D FSI models based on in vivo MRI data for human carotid plaques (5 with ruptures, 7 non ruptures) show that plaques with prior ruptures are associated with higher critical plaque wall stress conditions when compared with non ruptured plaques and that those high critical stress conditions occur at ulcer sites. These findings, when further validated in a larger study, may provide additional stress indicators helpful for image-based plaque vulnerability assessment.

Acknowledgement

This research was supported in part by NSF grant DMS-0540684 and NIH grant R01 EB004759.

Footnotes

Conflict of Interest Disclosure: None.

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