Description of field of research

Biomechanics is a research field in which the human musculoskeletal system is studied using advanced engineering techniques, including computational modelling. One of the current challenges in this field is representing each individual, or patient, as accurately as possible to enable the development of personalised medicine and tailored treatments. This is a crucial requirement for improving orthopaedic practices and rehabilitation programmes, for example. The first step for studying the musculoskeletal system of an individual is the availability of an accurate representation of its anatomy at the skeletal level.

This project aims to develop a statistical model of the femur using a large dataset of lower limb bone surfaces obtained from medical images. A statistical shape model is a geometrical model that can describe the variability of a shape within a population. An example is provided in the attached figure, which represents a statistical shape model of the proximal tibia obtained from 35 bones. In this project, we are looking to develop a model of the femoral geometry from a larger training dataset and use it to reconstruct new bone anatomies using minimal geometrical information collected without imaging equipment.

 

School

Biomedical Engineering

Research Area

biomechanics, biomedical engineering, digital health,

This is a computational project that will be run in collaboration with Dr Luca Modenese in the Computational Biomechanics group. 

The project will be performed using open source software (listed in the reference material) and publicly available data. Computational resources will be made available by the supervisory team if required.

The expected outcome of the project is the generation of a statistical model of the femur and its use to reconstruct bone geometries outside the training dataset.

Previous publication on similar research topics:

  • Nolte, Daniel, et al. ""Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling."" Gait & posture 77 (2020): 269-275. https://doi.org/10.1016/j.gaitpost.2020.02.010
  • Zhang, Ju, et al. ""Lower limb estimation from sparse landmarks using an articulated shape model."" Journal of biomechanics 49.16 (2016): 3875-3881. https://doi.org/10.1016/j.jbiomech.2016.10.021

Software and online learning resources: