Multimodality imaging-driven multifidelity modeling of aortic dissection

多模态成像驱动的主动脉夹层多保真建模

• 批准号: 10242915
• 负责人: Jay D. Humphrey
• 金额: $ 55.94万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-05 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10242915
• 关键词: Acute; Address; Animal Experiments; Animal Model; Aorta; Aortic Rupture; Arteries; Attention; Biological; Biomechanics; Biomedical Engineering; Blood; Blood Vessels; Blunt Trauma; Carotid Arteries; Categories; Cervical; Cessation of life; Charge; Chest; Child; Chronic; Clinical; Coagulation Process; Collaborations; Communities; Computer Models; Coupling; Data; Defect; Deposition; Development; Diagnostic Imaging; Dilatation - action; Disease; Dissection; Elderly; Event; Foundations; Geometry; Glycosaminoglycans; Goals; Heritability; Human; Hypertension; Image; Imaging Techniques; In Vitro; Individual; Infusion procedures; Intervention; Knowledge; Lead; Lesion; Long-Term Effects; Machine Learning; Mechanics; Medical Imaging; Methods; Modeling; Monitor; Morbidity - disease rate; Motivation; Multimodal Imaging; Operative Surgical Procedures; Optical Coherence Tomography; Outcome; Phase; Phenotype; Platelet aggregation; Play; Positioning Attribute; Prevention; Process; Prognosis; Property; Research; Resolution; Risk Factors; Role; Rupture; Scheme; Site; Solid; Statistical Models; Testing; Thoracic aorta; Thrombus; Time; Training; Tunica Adventitia; Ultrasonography; Uncertainty; Video Microscopy; Work; ascending aorta; base; digital imaging; disability; experimental study; healing; hemodynamics; improved; in silico; in vivo; insight; intracranial artery; microSPECT; mortality; mouse model; mucoid; multi-scale modeling; multiple omics; normotensive; novel; novel strategies; particle; predictive modeling; public health relevance; spatiotemporal; supervised learning; tool; virtual; young adult

PROJECT SUMMARY. Aortic dissections are responsible for significant morbidity and mortality in young and old individuals alike. Whereas type A (ascending aorta) dissections are treated aggressively via surgery, type B (descending thoracic aorta) dissections are often monitored for long periods to determine the best treatment. These lesions can cease to propagate (i.e., stabilize or heal) or they can propagate further and either turn inward and connect again with the true lumen to form a re-entry tear or turn outward and result in rupture in the case of an compromised adventitia. Notwithstanding the importance of these later events, there is a pressing need to understand better the early processes that initiate the dissection and drive its initial propagation as well as to determine whether the presence of intramural thrombus is protective or not against early or continued propagation. Over the past 5 years our collaborative team has developed numerous new multimodality imaging techniques, biomechanical testing methods, and computational modeling approaches across multiple scales that uniquely positions us to understand better the process of early aortic dissection and the possible roles played by early intramural thrombus development. In this project, we propose to use nine complementary mouse models to gain broad understanding of the bio-chemo-mechanical processes that lead to aortic dissection and to introduce a new machine learning based multifidelity modeling approach to develop predictive probabilistic multiscale models of dissection. These models will be informed, trained, and validated via data obtained from a combination of unique in vitro biomechanical phenotyping experiments (wherein we can, for the first time, quantify the initial delamination process under well-controlled conditions and regional material properties thereafter) and novel multimodality imaging of delamination / dissection both in vitro and in vivo. We will consider, for example, the roles of different elastic lamellar geometries; we will assess separate roles of focal proteolytic activation and pooling of highly negatively charged mucoid material, which can degrade or swell the wall respectively; and we will model and assess the effects of early thrombus deposition within a false lumen. We submit that our new probabilistic paradigm, based on statistical autoregressive schemes and enabled by machine learning tools, could be transformative and lead to a paradigm shift in disease prediction where historical data, animal experiments, and limited clinical input (e.g., multiomics) can be used synergistically for robust prognosis and thus interventional planning. Our work is also expected to lead naturally to an eventual better understanding of the chronic processes associated with dissection via predictive models that are aided by the expected “revolution of resolution” in diagnostic imaging.

项目摘要。主动脉夹层是导致年轻人和老年人显着发病率和死亡率的原因。 老人们都一样。 A 型（升主动脉）夹层可通过手术积极治疗，而 B 型夹层则可通过手术积极治疗。 （胸降主动脉）夹层通常需要长期监测以确定最佳治疗方案。 这些病变可以停止传播（即稳定或愈合），或者可以进一步传播，或者转变为 向内再次与真腔相通，形成再入撕裂或向外翻转，导致真腔破裂。 外膜受损的情况。尽管后来发生的这些事件很重要，但仍有一个紧迫的问题 需要更好地了解启动解剖并驱动其初始传播的早期过程 以确定壁内血栓的存在是否对早期或持续性血栓形成具有保护作用 传播。在过去的 5 年里，我们的协作团队开发了许多新的多模态成像 跨多个尺度的技术、生物力学测试方法和计算建模方法 这使我们能够更好地了解早期主动脉夹层的过程以及可能的情况 早期壁内血栓形成所起的作用。在这个项目中，我们建议使用九个 互补的小鼠模型，以获得对导致的生物化学机械过程的广泛了解 主动脉夹层并引入一种新的基于机器学习的多保真建模方法来开发 解剖的预测概率多尺度模型。这些模型将被告知、训练和验证 通过独特的体外生物力学表型实验组合获得的数据（其中我们 首次可以量化在良好控制的条件和区域下的初始分层过程 材料特性）以及体外和体内分层/解剖的新型多模态成像 体内。例如，我们将考虑不同弹性层状几何形状的作用；我们将单独评估 局部蛋白水解激活和带高负电荷的粘液物质汇集的作用，这可以 分别使墙壁退化或膨胀；我们将模拟并评估早期血栓沉积的影响 假腔内。我们提出，我们的新概率范式基于统计自回归 由机器学习工具支持的计划可能会带来变革，并导致范式转变 可以利用历史数据、动物实验和有限的临床输入（例如多组学）进行疾病预测 协同用于稳健的预后，从而制定干预计划。我们的工作也有望引领 通过预测自然地最终更好地理解与解剖相关的慢性过程 模型得到了诊断成像中预期的“分辨率革命”的帮助。