Spinal disorders such as back pain incur a substantial societal and economic burden. Unfortunately, there is lack of understanding and treatment of these disorders are further impeded by the inability to assess spinal forces in vivo. The aim of this project is to address this challenge by developing and testing a novel image-driven approach that will assess the forces in an individual's spine in vivo by incorporating information acquired from multimodal imaging (magnetic resonance imaging (MRI) and biplane X-rays) in a subject-specific model. Magnetic resonance and biplane X-ray imaging are used to capture information about the anatomy, tissues, and motion of an individual's spine as they perform a range of everyday activities. This information is then utilised in a subject-specific computational model based on the finite element method to predict the forces in their spine. The project is also utilising novel machine learning algorithms and in vitro, six-axis mechanical testing on human, porcine and bovine samples to develop and test the modelling methods rigorously.Abstract
Objectives
Methods
The shape of the vertebral bodies from L1 to L4 was assessed from lateral dual-energy x-ray absorptiometry (DXA) images using an active shape model. The output from the model was compared to measurements of areal bone mineral density in L1 to L4 (aBMD) using a stepwise linear regression model. A significant relationship was found between aBMD and vertebral shape that suggests that the method may be useful for correcting artefacts such as osteophytes.
