Simulation-based optimisation for the generative design of a shunt.

基于仿真的分流器生成设计优化。

• 批准号: 2597528
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597528
• 关键词: Simulation; based; optimisation; generative; design

This project falls within the EPSRC research area of healthcare technologies while also including topics pertinent to mathematical and engineering sciences. The project will be under the supervision of Prof Antoine Jerusalem (engineering) and Prof Sarah Waters (mathematics) and will work with optimisation tools provided by Prof Jose-Maria Peña through Lurtis Ltd, with clinical guidance provided by Mr Jay Jayamohan. Hydrocephalus is a serious medical condition affecting one in every thousand births. Though arising from various causes, hydrocephalus describes an excess of fluid within the skull, causing a build-up of inter-cranial pressure. If left untreated, the condition will worsen, causing headaches, balance problems, and potentially proving fatal within years. The most common treatment is to implant a permanent drainage shunt into the brain to remove excess fluid to the stomach, where it can be safely cleared. However, this treatment has the risk of vascular brain tissues such as the Choroid Plexus (CP) being dragged into the shunt during drainage, causing both shunt blockages and bleeds in the brain, and additionally preventing the shunt from being easily replaced. A new shunt design is needed to avoid this. The intention of the DPhil is, therefore to create a new, patentable (potentially personalisable) design framework for hydrocephalus shunts, suitable for clinical use. The project will first develop a computational fluid model for the shunt-CP system within the brain, before using generative design principles to optimise the configuration for a given CP idealised geometry. Generative design is a novel approach which combines traditional engineering with advanced artificial intelligence methods (mostly machine learning and heuristic optimisation methods) to produce new tentative designs and refine the ones proposed by the user. Here a heuristic optimisation algorithm developed in partnership with Lurtis is used (based on hybrid self-adaptive optimisation techniques that combine population-based and local search strategies). For this optimisation framework, this DPhil may also explore different surrogate-based approaches that would complement the optimisation mechanism for this particular computational model problem. The model is posed as a fluid-structure interaction problem where outflow through the shunt creates fluid stresses, which causes deflection of the CP tissue. With a sufficiently generalisable mesh, various material parameters can be varied to investigate reduction in this deflection, hence minimising the likelihood of the tissue being entangled in the shunt holes. The optimisation code acts as a black-box wrapper, which can be coupled to the shunt model to investigate parameters of interest and personalised for a given morphology. Work during the short project provided a proof of concept with a simplified 2D model. This project developed an investigative model which successfully simulated the deformation of CP in the hydrocephalus scenario. The optimisation algorithm was used successfully to propose new hole sizes and positions, and suggested that larger diametrically opposite holes would minimise tissue deflection. There is also potential within the scope of a DPhil to create a software tool which could apply these methods to general medical components of a similar remit. We may want to explore and justify our models by comparing simulations to physical experiments run with the engineering department. If the project progresses sufficiently, we may also want to establish contact with medical companies to discuss patent and manufacturing opportunities.

该项目属于EPSRC医疗保健技术研究领域的福尔斯，同时也包括与数学和工程科学相关的主题。该项目将在Antoine耶路撒冷教授（工程）和Sarah沃茨教授（数学）的监督下进行，并将使用Jose-Maria Peña教授通过Lurtis Ltd提供的优化工具，以及Jay Jayamohan先生提供的临床指导。脑积水是一种严重的医学疾病，每千名新生儿中就有一名患有脑积水。虽然由各种原因引起，但脑积水描述了颅骨内过量的液体，导致颅内压力积聚。如果不及时治疗，病情会恶化，导致头痛，平衡问题，并可能在几年内致命。最常见的治疗方法是在大脑中植入永久性引流分流器，将多余的液体转移到胃中，在胃中可以安全地清除。然而，这种治疗存在血管脑组织（如脉络丛（CP））在引流过程中被拖入分流管的风险，导致分流管堵塞和脑出血，并且还阻止了分流管的更换。需要一种新的分流设计来避免这种情况。因此，哲学博士的目的是为脑积水分流器创建一个新的、可专利的（可能个性化的）设计框架，适合临床使用。该项目将首先为大脑内的分流CP系统开发一个计算流体模型，然后使用生成设计原理来优化给定CP理想几何形状的配置。生成式设计是一种新的方法，它将传统的工程与先进的人工智能方法（主要是机器学习和启发式优化方法）相结合，以产生新的尝试性设计并改进用户提出的设计。这里使用了与Lurtis合作开发的启发式优化算法（基于混合自适应优化技术，该技术结合了基于群体的联合收割机和局部搜索策略）。对于这个优化框架，本哲学博士还可以探索不同的基于代理的方法，这些方法将补充这个特定计算模型问题的优化机制。该模型被视为流体-结构相互作用问题，其中通过分流器的流出产生流体应力，这导致CP组织偏转。使用足够通用的网格，可以改变各种材料参数以研究这种偏转的减少，从而最大限度地减少组织缠绕在分流孔中的可能性。优化代码充当黑盒包装器，其可以耦合到分流模型以调查感兴趣的参数并针对给定形态进行个性化。在短项目期间的工作提供了一个简化的2D模型的概念证明。该项目开发了一个研究模型，成功地模拟了脑积水情况下CP的变形。优化算法成功用于提出新的孔尺寸和位置，并建议直径相对较大的孔将最大限度地减少组织偏转。在哲学博士的范围内也有可能创建一个软件工具，可以将这些方法应用于类似职权范围的一般医疗组件。我们可能希望通过将模拟与工程部门运行的物理实验进行比较来探索和证明我们的模型。如果项目进展顺利，我们可能还希望与医疗公司建立联系，讨论专利和制造机会。