Ultrahigh resolution imaging and analysis of osteoarthritic joints using hierarchical phase-contrast (HiP-CT), computational modelling, and machine learning, enabling scaling of small animal models to humans

Project ID: 2228cd1310 (You will need this ID for your application)

Under Offer

UCL Lead department: Mechanical Engineering

Lead Supervisor: Peter Lee

Project Summary:

Age-related osteoarthritis (OA) affects over 500 million people worldwide. Despite this, there is a lack of effective personalised interventions for individuals with OA. Promising studies of OA in mouse models relate joint mechanics, tissue microarchitecture and their degeneration during OA processes; however, scaling to humans is uncertain.

This project will enable in situ dynamic experimental measurements of microarchitecture and strains in large animal and human joints with micron-level resolution, to tens of nanometers in strain. This will be achieved using a transformation synchrotron imaging technique called Hierarchical Phase-Contrast Tomography(HiP-CT, mecheng.ucl.ac.uk/HiP-CT). This will allow the first measurements of enhanced growth plate bridging, damage to articular cartilage and intact joint mechanics under physiologically realistic loading conditions.

The overarching aim of this project is to develop an imaging and computational framework to analyse how the progression of OA impacts joint mechanics, stress concentration, and hence pain. This initial platform would then be used by others towards patient-specific prediction of patient joint pain and its relief.

We seek highly motivated students wanting to make a significant contribution to the society through healthcare research, with the knowledge and expertise in imaging, computational modelling and machine learning (ML).

The project is in collaboration with the Royal Veterinary College(RVC), the European Synchrotron Radiation Facility (requiring regular travel to Grenoble-France to perform HiP-CT), Diamond Light Source and supporting Research Fellows at UCL. The project will start with scanning the specimens from RVC under stepped loading with HiP-CT, creating unique datasets. The large-scale datasets will first be used to quantify morphology, strains (using digital-volume-correlation) and informing computational models. The dataset will be used to train ML models to characterise materials and speed-/scale-up computational models.

The supervisory team–Profs Lee(imaging), Torii(modelling), and Pitsillides(OA expert) will guide the student applying state-of-the-art technology, advancing our understand of osteoarthritis.