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Reduced model aided fluid-structure interaction design framework for shunt systems
Elizabeth Hayman1, Van Dung Nguyen2, Ian S McFarlane1
1Department of Engineering Science, University of Oxford, United Kingdom; Mathematical Institute, University of Oxford, United Kingdom.
None:
Traditionally, clinical devices are designed, tested and improved through lengthy and expensive laboratory experiments and clinical trials [1]. More recently, computational methods have allowed for rapid testing, speeding up the design process and enabling far more complete searches of design space. While computational models cannot fully capture the complexities of biological systems, they provide valuable insights into crucial underlying mechanisms, such as the effects of fluid-structure interactions (FSIs). In this paper we present a modular, partitioned, computational FSI pipeline whereby 2D reduced order models guide the 3D design of the problem of interest. This framework is applied to the problem of hydrocephalus shunt occlusion. Hydrocephalus is a medical condition characterised by an excess of cerebrospinal fluid (CSF) in the brain, and is commonly treated with the insertion of a shunt system. This system includes a ventricular catheter component - a hollow tube with inlet holes arranged in the tube wall close to the closed tip - which is positioned in the lateral ventricles of the brain. Despite recent improvements in the catheter material, this treatment still has high failure rates, most often due to the blockage of the catheter by the Choroid Plexus (ChP) tissue. We use an idealised FSI model to compare existing catheter designs by considering the deformation of the ChP under CSF flow in the ventricle environment in an hydrocephalus scenario. To the best of our knowledge, this is the first computational framework to directly incorporate the deformation of the ChP to discriminate between catheter designs. The faster 2D model is used in a comprehensive parameter sweep of the catheter design domain, and motivates a new design, then confirmed to be an improvement when tested in the full 3D domain. This approach demonstrates the success of using reduced order methods to guide the design of a more complex problem.
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