Stroke is the third leading cause of death in the United States which is often caused by atherosclerotic carotid plaque rupture. Stroke may lead to various brain damages, brain malfunctions and disability. A large number of victims who are apparently healthy die suddenly without prior symptoms. Available screening and diagnostic methods are insufficient to identify the victims before the event occurs. The objectives of this project are to integrate computational modeling, Magnetic Resonance Imaging (MRI) technology, ultrasound/Doppler technology (US), mechanical testing, and pathological analysis to perform quantitative mechanical analysis to atherosclerotic carotid plaques, to quantify critical blood flow and plaque stress/strain conditions under which plaque rupture is likely to occur, and to seek the potential that quantitative mechanical analysis can be integrated into state-of-the-art imaging technologies for better screening and diagnostic applications. Those objectives are consistent with the mission of the NIBIB. Forty patients scheduled to undergo carotid endarterectomy will be recruited to participate in this study with proper consent. Fifty additional cadaveric or ex-vivo endarterectomy carotid plaque samples will also be used in the study.
The specific aims are: (1) Develop and integrate 3D MRI, US technologies and computational methods to quantify plaque structure and vessel material properties; (2) Develop 3D multi-component computational models with blood- vessel interactions based on in vitro, ex vivo and in vivo measurements obtained by MRI, US technologies and intra- operative measurements; (3) Perform complete mechanical analysis for atherosclerotic plaques and identify correlations between critical stress/strain conditions and plaque morphology and composition, vessel mechanical properties and blood flow pressure conditions; (4) Quantify and validate correlations between critical stress/strain conditions and plaque vulnerability and identify critical stress/strain indicators which could be used by doctors to make clinical and diagnostic decisions. This will establish the base for future software development and technology integration. With large-scale patient study validations, the long term goal is to establish that integration of computational modeling with MRI and US imaging technologies can lead to better interpretation of the information already contained in MRI and ultrasound images, new development of imaging software for more accurate assessment of plaque vulnerability, and potential early prediction of possible stroke. With improved accuracy of predictions, certain unnecessary operations may be avoided, cost of medical care can be reduced, and some fatal events may be prevented. The integrated computational mechanical image analysis improves current image analysis techniques and can serve as basis for many further research activities involving complex biological structures with multi-component interactions.
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