Mathematical modeling and computer simulation of aortic dissection

主动脉夹层的数学建模和计算机模拟

• 批准号: 8581495
• 负责人: Boyce Eugene Griffith
• 金额: $ 51.65万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-26 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8581495
• 关键词: Accounting; Acute; Affect; Anatomy; Animal Model; Aorta; Aortic Diseases; Automobile Driving; Blood; Blood Pressure; Blood Vessels; Caliber; Chronic; Clinical; Clinical Management; Clinical Pathology; Computer Simulation; Data; Descending aorta; Disease; Dissection; Distal; Elasticity; Ensure; Failure; Genetic; Goals; Health; Human; Human Characteristics; Image; Implant; Injury; Intervention; Lesion; Liquid substance; Location; Magnetic Resonance Imaging; Mechanics; Medical; Methodology; Methods; Modeling; Operative Surgical Procedures; Outcome; Pathology; Patients; Pattern; Physicians; Plant Roots; Play; Property; Research; Residual state; Risk; Risk Assessment; Role; Rupture; Shapes; Simulate; Site; Stents; Stress; Structure; Study models; Surgical Flaps; Syndrome; Testing; Thoracic aorta; Time; Tissue Model; Tissue Sample; Tissues; Work; X-Ray Computed Tomography; aortic arch; ascending aorta; base; clinical decision-making; experience; hemodynamics; high risk; implantation; improved; in vivo; innovation; mathematical model; mortality; physical model; predictive modeling; pressure; public health relevance; repaired; response; shear stress; simulation; treatment planning; treatment strategy; valve replacement

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：自1956年首次成功、可重复的外科干预以来，主动脉疾病的治疗取得了显著进展;然而， 虽然我们对这些疾病的遗传和细胞基础的了解已经稳步增长，但治疗计划通常仍然依赖于简单的风险评估模型和临床经验。一些病理学已经在动物模型中成功复制，但这些研究的结果并不总是容易外推到患者身上。其他病理学缺乏任何可接受或可重复的动物模型。一个例子是主动脉夹层，其中主动脉壁中的内膜撕裂传播到中膜中以在血管壁内形成假腔。手术 主动脉夹层的治疗包括置换主动脉的一部分或血管内支架植入以覆盖受影响的节段。这两种方法都有很大的风险，确定干预的最佳选择和时机具有挑战性。虽然可以在动物模型中诱导主动脉夹层，但这种模型不能复制临床病理学。因此，主动脉夹层的建模研究必须使用物理或计算模型。现有的主动脉夹层的计算模型使用传统的计算流体动力学（CFD）方法，其中血管壁和皮瓣被视为刚性结构。尽管CFD模型能够预测壁面剪切应力分布，但它们无法解释血液和血管组织之间的相互作用，或此类相互作用对夹层主动脉动力学的影响。本项目将开发克服CFD模型局限性的夹层和夹层主动脉的流体-结构相互作用（FSI）模型。这些预测模型将用于执行患者特定的模拟，最终将有助于临床决策，例如，选择最佳的药物治疗或手术干预。 本计画将发展两种不同类型的主动脉夹层FSI模型。第一种类型的模型将使用血管和病变的几何参数化、非患者特异性模型。这些模型将用于系统地研究几何形状和驱动条件如何影响发展中的夹层和充分发展的病变的动力学。第二种类型的模型将通过使用从计算机断层扫描（CT）和/或磁共振（MR）成像研究导出的真实患者解剖几何结构来解释受试者特定解剖结构的影响。为了表征人体主动脉组织的机械响应以及损伤和失效特征，将使用从正常和患病人体主动脉采集的组织样本进行实验测试。来自这些测试的数据将用于开发健康和疾病特异性组成模型，其中包括组织损伤和失败的创新模型。这些特征的影响不仅限于主动脉夹层，这项工作有潜在的应用范围的动脉病变，包括动脉瘤破裂。最后，这些模型将用于研究需要或已经接受部分修复斯坦福大学A型夹层的患者的手术和医疗管理。