Modeling Mechanical Stress in Vulnerable Plaques to Reduce Strokes

Science Highlights
February 26, 2010

Every 40 seconds someone in the United States has a stroke. Without warning, an artery in the brain gets blocked, blood flow ceases, and oxygen fails to reach the critical structures responsible for speech, movement, sight, and sometimes life itself. Strokes are the third leading cause of death in the nation and a leading cause of long-term serious disability.

The carotid arteries, two arteries located on either side of the neck, supply the brain with blood. More than 60% of all strokes are caused when plaque located in one of the arteries ruptures. In these cases, the plaque – composed of fats, fibrous tissue, and pebble-like calcifications – breaks apart, sending pieces careening through the artery. Plaque fragments and blood clots formed at the rupture site may be carried toward the brain. If the fragments pile up, they can block blood flow within an artery, causing a stroke.

This 3D view of a human carotid plaque shows plaque components and the presence of ulceration (a). Band plots of plaque wall stress (Stress-P1) show that 3D critical plaque wall stress (CPWS) predicted the actual rupture site (b)
This 3D view of a human carotid plaque shows plaque components and the presence of ulceration (a). Band plots of plaque wall stress (Stress-P1) show that 3D critical plaque wall stress (CPWS) predicted the actual rupture site (b)

Current diagnostic tools are limited in their ability to identify which plaques are at risk of rupture. In an effort to improve patient outcomes, researchers from Worcester Polytechnic Institute, Worcester, Mass.; Washington University, St. Louis, Mo.; and the University of Washington, Seattle, have developed magnetic resonance imaging (MRI) techniques and computational models that will help identify which plaques are likely to burst. Information from patient MRIs is used to create a 3D fluid-structure interaction (FSI) model to show 3D stress/strain plots of plaques within the carotid arteries. The FSI model also describes plaque wall stress and flow shear stress each plaque may experience.

Patients with narrowing of the carotid vessel typically are followed over time and may undergo carotid endarterectomy (a surgical procedure to remove plaque) when the vessels become 70 to 80% blocked. “Seventy percent of patients who undergo surgery are asymptomatic. We know as surgeons there are some patients who don’t need surgery, but there is no way to predict which plaques are unstable or could become vulnerable. Any imaging study that identifies these patients could be of tremendous use,” says Gregorio Sicard, M.D., chief of the vascular surgery section at Washington University, St. Louis, and a collaborator on the new modeling technique.

Improving MRI Studies

Chun Yuan, Ph.D., professor of radiology, and Thomas Hatsukami, M.D., professor of vascular surgery, both at the University of Washington, have developed high-resolution MRI techniques that can be used to differentiate plaque tissue components and assess carotid plaque structure, composition, and inflammatory activity.

“Current imaging techniques used in clinical practice to evaluate carotid disease do not give us information about the structure, composition, and inflammatory activity of the carotid plaque,” says Dr. Hatsukami, a collaborator on the FSI study. Their group and others have shown that intraplaque hemorrhage, larger lipid-rich necrotic cores, and thin/ruptured fibrous caps – the fibrous material covering the plaque – are associated with a higher likelihood of having a future transient ischemic attack (a mini-stroke) or stroke.

Calculating Plaque Stress

“The FSI model adds a new mechanical stress/strain dimension to the current image-based procedure for assessing plaque rupture risk,” says Dalin Tang, professor of computational mathematics and biomedical engineering at Worcester Polytechnic Institute and principal investigator of the research. The FSI model enhances MRI plaque analyses by providing additional information about the mechanical stresses that may predispose a plaque to rupture. The model predicts which plaques are likely to burst based on calculated critical stress/strain conditions. Tang and his group can calculate the plaque wall stress as well as the flow shear stress acting on the plaque. In a recent study of 12 patients, the researchers found that plaques with high stress areas – points where previous ruptures occurred – are more likely to burst than intact plaques. They also found that high critical stress conditions occur at ulcer sites in the plaque. Plaque wall stress was 86% higher at ulcer sites than at non-ulcer sites, and flow shear stress at ulcer sites was 170% higher than at non-ulcer sites.

“The processes leading to rupture are complicated, but this study gives in vivo evidence that structural stress plays a key role as a trigger in plaque rupture,” says Tang. “By focusing on localized stress conditions, the model can provide more specific information about each plaque.”

To obtain plaque wall stress and flow shear stress values, the MR images undergo two separate image analyses. First, MR images of patient carotid artery plaque are divided into multiple segments using custom analysis tools that identify the plaque’s lipid-rich core, fibrous tissue, calcification, clots, bleeding, and the presence or absence of ulcers. In a second analysis, the segmented plaque data are used to construct 3D frameworks that account for each plaque component as well as fluid and structural details. This approach provides a complete mechanical analysis. Blood pressure readings from each patient provide pressure conditions for the involved arteries.

Classifying Plaque Stress

As more patients are recruited for the MR imaging studies and more data accumulate, Tang is refining a quantitative index of critical stress that doctors could use to assess plaques. The index is similar to the American Heart Association’s lesion classification system, a set of guidelines for assessing blockages in the heart’s arteries. Tang’s index would provide a scale to classify plaques from stable (small lipid core less than 30% of the plaque with a cap thickness greater than 200 µm) to most vulnerable (very large necrotic core greater than 40% and a thin fibrous cap less than 150 µm). Yuan’s group is also investigating a carotid atherosclerosis scoring system that may be enhanced by Tang’s index.

“For clinical use, you want an index that is highly reproducible, provides data that can be easily quantified, and has proven predictive value for future ischemic events,” says Hatsukami. One of the challenges to developing such an index is its dependence on accuracy of MR data. “Our numerical numbers are deceivingly accurate,” says Tang. “They are ultimately only as accurate as the MR data, even though numerically we can be accurate to eight decimals.”

Another challenge is obtaining tissue samples for histological comparison. “More patients are getting stented rather than undergoing endarterectomy, so we have to wait for patients who will undergo surgery,” explains site principal investigator Pamela Woodard, M.D., and professor of radiology at Washington University. Stenting involves widening an artery by placing a tiny mesh tube in the constricted area.

Improving Patient Health

In the future, Tang and his collaborators want to incorporate time and cellular reactions into the plaque models. By simulating plaque progression, the FSI model could demonstrate how mechanical factors affect plaque growth and its final rupture. Modeling how cells react to different forces would allow for experiments to determine the role that genes play in plaque development. Tang also is extending the modeling to coronary arteries to quantify the mechanical forces that may lead to heart attack. He’s working on commercializing the modeling package so that it can be widely disseminated and can be tested in large randomized clinical trials. “Now, more than ever, third party payers are looking at whether the technology works and how it impacts patient care and outcomes,” says Woodard. “We need large numbers of patients so that we can show that adding atherosclerotic modeling to current diagnostic standards impacts a surgeon’s decision to treat.”

The FSI model may not be available for clinical use for several years. In the meantime, Tang and his group will focus on automating the modeling and analysis process. Creating a screening tool that could prevent strokes and heart attacks through early detection would make the initial challenges worthwhile. “I’m very committed to this work. Individuals with asymptomatic, sub-clinical carotid disease may not know they are walking time bombs,” says Tang. “With early detection we might be able to avoid 50% or even more of heart attacks and strokes through changes in diet and exercise, and other preventive treatment.”

This work is supported in part by the National Institute of Biomedical Imaging and Bioengineering.


Dong L, Kerwin WS, Ferguson MS, Li R, Wang J, Chen H, Canton G, Hatsukami TS, Yuan C. Cardiovascular magnetic resonance in carotid atherosclerotic disease. Journal of Cardiovascular Magnetic Resonance. 2009:11(1):53. [Epub ahead of print]

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Tang D, Teng Z, Canton G, Yang C, Ferguson M, Huang X, Zheng J, Woodard P, Yuan C. Sites of rupture in human atherosclerotic carotid plaques are associated with high structural stresses: An in vivo MRI-based 3D fluid-structure interaction study. Stroke. 2009: 40: 3258-3263.

Tang D, Teng Z, Canton G, Hatsukami TS, Dong L, Huang X, Yuan C. Local critical stress correlates better than global maximum stress with plaque morphological features linked to atherosclerotic plaque vulnerability: An in vivo multi-patient study. BioMedical Engineering OnLine. 2009: 8:15.

Teng Z, Canton G, Yuan C, Ferguson M, Yang C, Huang X, Zheng J, Woodard P, Tang D. 3D critical plaque wall stress is a better predictor of carotid plaque rupture sites than flow shear stress: An in vivo MRI-based 3D FSI study. J. Biomechanical Engineering, in press.

American Stroke Association

Health Terms
Modeling and Simulation