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

为医疗设备的计算机试验生成心血管形状的虚拟群体

基本信息

批准号：
2488018
负责人：
金额：
--
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2488018
关键词：
Generating virtual populations cardiovascular shapes

项目摘要

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.

医疗器械的计算机模拟试验是一项新兴技术，能够彻底改变用于治疗动脉瘤、血管狭窄、瓣膜狭窄等疾病的器械的设计、选择和部署。进行系统性、大规模的医疗器械计算机模拟试验的先决条件是开发解剖学和生理学的生成模型，代表真实世界的患者人群。该项目的重点是开发心血管解剖结构条件生成建模的新方法，用于治疗主动脉瓣狭窄的经导管主动脉瓣植入（TAVI）器械的计算机模拟试验。具体而言，该项目将探索生成形状组成学习作为有效利用不同，不完整，多模态成像数据中可用的心脏形状/结构的一种手段，以生成连贯的，解剖学上合理的心血管形状人群，这些人群以相关患者属性为条件，并代表真实世界的患者人群。因此，该项目将涉及开发：（a）一种在对比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）使用形状组成学习的新生成形状模型;（c）新的条件生成形状模型，以根据虚拟患者人群中的期望属性促进心血管解剖结构的实例化，用于TAVI器械的计算机模拟试验。