Management of aortic diseases has progressed dramatically since the first successful, reproducible surgical intervention in 1956;however, while our understanding of the genetic and cellular bases of these diseases has steadily grown, treatment planning still generally relies on simple risk-assessment models and clinical experience. Some pathologies have been successfully replicated in animal models, but results from such studies are not always readily extrapolated to patients. Other pathologies lack any accepted or reproducible animal model. An example is aortic dissection, in which an intimal tear in the aortic wall propagates into the media to form a false lumen within the vessel wall. Surgical treatment for aortic dissection consists of either replacement of a portion of the aorta or endovascular stent implantation to cover the affected segment. Both approaches carry significant risks, and determining the optimal choice and timing of an intervention is challenging. While aortic dissections can be induced in animal models, such models do not replicate the clinical pathology. Consequently, modeling studies of aortic dissection must use physical or computational models. Existing computational models of aortic dissection use conventional computational fluid dynamics (CFD) approaches, in which the vessel wall and flap are treated as rigid structures. Although CFD models are able to predict wall shear stress distributions, they are unable to account for the interactions between the blood and vascular tis- sues, or for the effects of such interactions on the dynamics of the dissected aorta. This project will develop fluid-structure interaction (FSI) models of both the dissected and dissecting aorta that overcome the limitations of CFD models. These predictive models will be used to perform patient-specific simulations that ultimately will aid in clinical decision making, e.g., selecting optimal medical therapies or surgical interventions. This project will develop two types of FSI models of aortic dissection. The first type of model will use a geometrically parameterized, non-patient-specific model of the vessel and lesion. Such models will be used to study systematically how geometry and driving conditions affect the dynamics of both developing dissections and fully developed lesions. The second type of model will account for the effects of subject-specific anatomy by using realistic patient anatomical geometries derived from computed tomography (CT) and/or magnetic resonance (MR) imaging studies. To characterize the mechanical response and the damage and failure characteristics of human aortic tissue, experimental tests will be performed using tissue samples collected from both normal and diseased human aortas. Data from these tests will be used to develop healthy and disease-specific constitutive models that include innovative models of tissue damage and failure. The impact of these characterizations is not limited to aortic dissection, and this work has potential applications to a range of arterial pathologies, including aneurysmal rupture. Finally, these models will be used to study the surgical and medical management of patients who require or who have undergone partial repair of a Stanford Type A dissection.

Public Health Relevance

This project will develop new mathematical and computational models for simulating aortic dissection, a condition that carries a high risk of mortality that occurs when a tear in the wall of the aorta propagates to form parallel flow paths within the vessel. The models developed in this project will be used to study fundamental questions about aortic dissection and its clinical management, and have the potential to inform work on a range of arterial pathologies, including aneurysmal rupture. Such models ultimately promise to aid in clinical decision making by determining the optimal choice and timing of medical and surgical approaches to treating aortic dissection and other aortic diseases.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Baldwin, Tim
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New York University
Internal Medicine/Medicine
Schools of Medicine
New York
United States
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Gao, Hao; Wang, Huiming; Berry, Colin et al. (2014) Quasi-static image-based immersed boundary-finite element model of left ventricle under diastolic loading. Int J Numer Method Biomed Eng 30:1199-222