Multimodality imaging-driven multifidelity modeling of aortic dissection

项目总结。主动脉夹层导致青年和青少年的显著发病率和死亡率。 年纪大的人都一样。而A型(升主动脉)夹层是通过手术积极治疗的，而B型 (降主动脉)夹层经常被长期监测，以确定最佳治疗方法。 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，或者转向 向内，并再次与真正的管腔连接，形成折返性撕裂或向外旋转，导致 外膜受损的病例。尽管这些后来的事件很重要，但有一个紧迫的问题 需要更好地了解启动解剖并推动其初始传播的早期过程 以确定壁间血栓的存在是否对早期或持续性具有保护作用 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式成像 跨多尺度的技术、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和可能的 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 补充小鼠模型以广泛了解导致 针对主动脉夹层，提出了一种新的基于机器学习的多保真建模方法 解剖的预测性概率多尺度模型。这些模型将得到通知、培训和验证 通过从独特的体外生物力学表型实验组合获得的数据(其中我们 可以第一次量化初始分层过程，在良好控制的条件和区域 材料特性)以及体外和体内分层/解剖的新的多模式成像 活着。例如，我们将考虑不同弹性片层几何形状的作用；我们将分别评估 局部蛋白分解激活和聚集高负电荷粘液物质的作用，这可以 我们将模拟和评估早期血栓沉积的影响。 在一个假腔内。我们认为，我们基于统计自回归的新概率范式 方案，并由机器学习工具实现，可能是变革性的，并导致 疾病预测，包括历史数据、动物实验和有限的临床输入(例如，多重组学) 协同用于强健的预后，从而制定介入计划。我们的工作也有望引领 自然地最终通过预测性地更好地理解与夹层相关的慢性过程 这些模型得到了诊断成像领域预期的“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.