Biomechanics and Biotribology of the Haemophilic Ankle - A Computational Study

血友病是一种遗传性疾病，由凝血因子缺乏引起，出现反复出血；80%的出血发生在肌肉骨骼系统。在这些肌肉骨骼出血中，脚踝出血的发生率很高。据推测，这是由于日常活动中对脚踝的高度压力，从血友病患者24个月开始活动时就开始了。临床上，血友病患者表现出较早的骨关节炎发作。然而，随着疾病的进展，踝关节的发病机制和形态变化的研究很少。本项目旨在建立针对患者的血友病踝关节的有限元模型，以探索随着疾病的进展，踝关节形态的变化与生物摩擦学的变化之间的关系。踝关节的复杂性导致了具有挑战性的计算方法，包括非均匀表面上的非线性接触力学。该项目将根据临床图像确定骨骼形态的变化，并定义关键的临床特征，以及它们如何在患者特定的方法中及时发展。从临床级纵向MR图像数据开发特定于患者的模型还有更多的工程挑战，这是因为必须同时捕获每个患者的特异性和患者之间的巨大差异，并提取成像协议的差异。该项目将研究如何捕捉患者之间的特定形态变化，同时保持一个计算模型，该模型可以解决患者之间的形态变化，例如通过研究哪些关键临床特征对生物摩擦学变化影响最大。还将开发描述关节生物摩擦学随疾病进展的纵向变化的方法，以便将不同患者组的模式与临床结果进行强有力的比较。本项目开发的方法将提供一套新的强大的计算工具，可用于开发患者队列方法，以检查早期发病的骨关节炎的可能性。这些特征化方法还将提供新的信息，说明哪些临床特征可能是血友病患者队列中具有挑战性和高危人群的骨性关节炎的良好候选图像标记。这将为更多的靶向预防方法的临床工作提供工具和方法。

Haemophilia is a genetic disorder, caused by a deficiency in coagulation factors, in which recurring bleeds occur; eighty percent of these bleeds occur in the musculoskeletal system. Of these musculoskeletal bleeds, a high number of incidences occurs in the ankle. This is hypothesised to be due to the high stresses on the ankle in everyday activities, starting as young as 24 months when the haemophiliac becomes mobile.Clinically, haemophilia patients present with early onset of osteoarthritis. However the pathogenesis and morphological changes in the ankle as the disease progresses are little studied.This project aims to develop patient-specific finite element models of the haemophilic ankle in order to explore how changes in the morphology of the ankle joint relate to a change in the biotribology as the disease progresses. The complexity of the ankle joint leads to challenging computational approaches including non-linear contact mechanics on non-uniform surfaces. This project will identify changes in bone morphology based on clinical images and define key clinical features and how they develop in time in a patient-specific approach. There are further engineering challenges in developing patient-specific models from clinical-grade longitudinal MR image data due to the necessity to capture both the specificity of each patient and the large variation from patient to patient, with abstraction of the difference in imaging protocols. This project will investigate means to capture the specific morphological changes between patients while keeping a computational model which is solvable for the large variation of morphology between patients, for example by studying which key clinical features have the most impact on biotribology changes. Methods to characterise the longitudinal variance of the joint biotribology as the disease progresses will also be developed so that the pattern of different patient groups can be robustly compared to clinical outcomes.The methods developed in this project will provide a new suite of robust computational tools that can be used to develop patient cohorts approaches to examine the likelihood of early onset of osteoarthritis. The characterisation methods will also provide new information on which clinical features may be good candidates as image markers of osteoarthritis in the challenging and at-risk cohort of haemophilic patients. This will provide tools and methods for more clinical work in targeting prevention methods.