Automatic mesh morphing of whole bone finite element models to clinical CT data

将全骨有限元模型自动网格变形为临床 CT 数据

• 批准号: 1803184
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1803184
• 关键词: Automatic; mesh; morphing; whole; bone

This project will be part of an EPSRC funded PhD network (three projects in total) where the candidates will form, together with the supervisors and co-supervisors, a research group focused on the development and improvement of current elastic registration methods in musculoskeletal applications. The network will be part of the INSIGNEO institute for in silico medicine (http://insigneo.org/). Insigneo controls one of the most refined patient-specific modelling protocols in the world to predict the strength of patient's bones simply from appropriately calibrated clinical computed tomography (CT) images, of the patient's skeleton. This fully validated protocol is used by our researchers to investigate osteoporosis, bone metastases, back pain, osteogenesis imperfecta, identification of paediatric abuse fractures, juvenile idiopathic arthritis, knee replacement, etc. Unfortunately, the protocol requires approximately one day's work by a skilled operator to transform the patient's images into a finite element model capable to accurately predicting bone strength. Most of the operator's time is spent segmenting the bone geometry from CT images, and then decomposing this geometry into a well-conditioned finite element mesh. Even after developing with Ansys a mesh-morphing algorithm that could "fit" a generic finite element mesh to the bone geometry of each patient, manual segmentation of the images is still required. Furthermore, the resulting mesh is not optimal: in a recent study we found that by using this mesh morphing as opposed to the manual protocol the ability of our patient-specific models to classify fractured patients accurately decreased by nearly 10%. The candidate will be taught how to perform the required image processing in order to convert a CT image of the proximal femur into a finite element mesh. In order to optimize and make efficient the meshing procedure the candidate will be taught how to use an elastic registration algorithm developed in Sheffield (ShIRT v2) that computes the registration transformation in parametric space. With ShIRT v2 we can a) directly register finite element meshes onto 3D images, and b) optimize the mesh conditioning based on a template mesh. This method has already been demonstrated in a similar problem in cardiac modelling, but has not as yet been used for bone modelling. As a further advancement, we will explore how the regularisation term can be used to improve the handling of the tissue property heterogeneity over the integration domain.

该项目将成为EPSRC资助的博士网络（共三个项目）的一部分，候选人将与导师和共同导师组成一个研究小组，专注于开发和改进当前肌肉骨骼应用中的弹性注册方法。该网络将成为insineo计算机医学研究所（http://insigneo.org/）的一部分。insineo控制着世界上最精细的患者特异性建模协议之一，可以简单地从患者骨骼的适当校准的临床计算机断层扫描（CT）图像中预测患者骨骼的强度。我们的研究人员使用这一完全有效的方案来研究骨质疏松症、骨转移、背痛、成骨不全症、儿童虐待性骨折的鉴定、青少年特发性关节炎、膝关节置换术等。不幸的是，该方案需要熟练的操作员大约一天的工作，将患者的图像转换为能够准确预测骨强度的有限元模型。操作员的大部分时间都花在从CT图像中分割骨骼几何形状上，然后将这些几何形状分解成条件良好的有限元网格。即使在与Ansys一起开发了一种网格变形算法，可以“适合”每个患者的骨骼几何形状的通用有限元网格，仍然需要对图像进行手动分割。此外，所得到的网格不是最佳的：在最近的一项研究中，我们发现，通过使用这种网格变形，而不是手动协议，我们的患者特异性模型准确分类骨折患者的能力下降了近10%。候选人将学习如何执行所需的图像处理，以便将股骨近端CT图像转换为有限元网格。为了优化和提高网格划分过程的效率，候选人将学习如何使用在Sheffield （ShIRT v2）开发的弹性配准算法，该算法计算参数空间中的配准变换。与衬衫v2，我们可以a)直接注册有限元网格到3D图像，b)优化基于模板网格的网格调节。这种方法已经在心脏建模的类似问题中得到证明，但尚未用于骨建模。作为进一步的进展，我们将探索如何使用正则化项来改善对积分域上组织特性异质性的处理。