Multimodality imaging-driven multifidelity modeling of aortic dissection

项目摘要。主动脉夹层是造成年轻人和老年人显著发病率和死亡率的原因。 老人们一样。尽管A型（升主动脉）夹层通过手术积极治疗，B型 （降胸主动脉）夹层通常被长时间监测以确定最佳治疗。 这些损伤可以停止传播（即，稳定或愈合），或者它们可以进一步传播， 向内并再次与真腔连接以形成再进入撕裂或向外翻转并导致 外膜受损病例。尽管这些后来的事件很重要， 我需要更好地理解引发解剖的早期过程，以及驱动其初始传播的过程 以确定壁内血栓的存在是否对早期或持续的 传播在过去的5年里，我们的合作团队开发了许多新的多模态成像技术， 技术、生物力学测试方法和跨多个尺度的计算建模方法 这使我们能够更好地了解早期主动脉夹层的过程， 早期壁内血栓形成的作用。在这个项目中，我们建议使用9个 互补的小鼠模型，以获得广泛的理解，导致生物化学机械过程， 主动脉夹层，并引入一种新的基于机器学习的多保真度建模方法， 解剖的预测概率多尺度模型。这些模型将被告知，培训和验证 通过从独特的体外生物力学表型实验（其中我们 可以，第一次，量化的初始分层过程下，良好的控制条件和区域 材料性能）和体外和体内分层/夹层的新型多模态成像。 vivo.例如，我们将考虑不同弹性层状几何形状的作用;我们将评估单独的 局灶性蛋白水解激活和高度带负电荷的粘液物质汇集的作用， 降解或膨胀的壁分别;我们将模拟和评估早期血栓沉积的影响 在一个假腔内我们提出，我们的新的概率范式，基于统计自回归 计划，并通过机器学习工具，可以是变革性的，并导致范式转变， 疾病预测，其中历史数据、动物实验和有限的临床输入（例如，多组学）可以是 协同地用于稳健的预后，从而用于介入规划。我们的工作也有望引领 自然地，通过预测， 这些模型得到了诊断成像中预期的“分辨率革命”的帮助。

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.