Generating virtual populations of cardiovascular shapes for in-silico trials of medical devices

医疗设备的硅片试验是一项新兴技术，它有能力彻底改变用于治疗诸如动脉瘤、血管狭窄、瓣膜狭窄等病症的设备的设计、选择和部署。进行系统的、大规模的医疗设备的计算机试验的先决条件是发展解剖学和生理学的生成模型，代表现实世界的患者群体。该项目的重点是开发心血管解剖学条件生成模型的新方法，用于经导管主动脉瓣植入术（TAVI）设备的计算机试验，用于治疗主动脉瓣狭窄。具体来说，该项目将探索生成形状组合学习作为一种有效利用不同的、不完整的、多模态成像数据中可用的心脏形状/结构的手段，以产生连贯的、解剖学上合理的心血管形状群体，这些群体以相关的患者属性为条件，并代表现实世界的患者群体。因此，该项目将涉及开发：(a)在增强心脏计算机断层扫描图像中分割主动脉瓣的新方法；(b)基于形状组成学习的新型生成形状模型；(c)新颖的条件生成形状模型，以促进根据虚拟患者群体中所需属性的心血管解剖实例化，用于TAVI设备的计算机试验。

In-silico trials of medical devices is an emerging technology that holds the power to revolutionise the design, selection and deployment of devices used to treat pathologies such as aneurysms, vessel stenosis, valve stenosis, etc. A pre-requisite for conducting systematic, large-scale in-silico trials of medical devices is the development of generative models of anatomy and physiology, representative of real-world patient populations. The focus of this project is on developing novel approaches for conditional generative modelling of cardiovascular anatomy, for in-silico trials of Transcatheter Aortic Valve Implantation (TAVI) devices, used to treat aortic valve stenosis. Specifically, this project will explore generative shape compositional learning as a means to effectively leverage cardiac shapes/structures available in disparate, incomplete, multi-modal imaging data, to generate coherent, anatomically plausible cardiovascular shape populations that are conditioned on relevant patient attributes and representative of real-world patient populations. As such, the project will involve development of: (a) a novel approach to segment aortic valves in contrast-enhanced cardiac computed tomography images; (b) novel generative shape models using shape composition learning; (c) novel conditional generative shape models to facilitate the instantiation of cardiovascular anatomies according to desired attributes in virtual patient populations, for in-silico trials of TAVI devices.