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UCL Mechanical Engineering
Faculty of Engineering Sciences

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PhD: Macro-to-molecular correlative X-ray imaging of strain during spinal joint loading

Department :
Mechanical Engineering
Location:
Harwell Campus
Duration:
4 years
Stipend:
The funding covers an annual tax-free stipend (at least £17,009 p.a.) and home tuition fees
Contact names:
Peter D Lee, Federico Bosi and Himadri S Gupta
Contact email:
peter.lee@ucl.ac.uk

Project Background

Fibrillar composites are ubiquitous in biological structural organs, with their mechanobiology critical for physiological function, and their multiscale structural/ mechanical changes in disease or injury often critical to loss of function. Treatment for these conditions imposes a huge healthcare burden. The bioengineering challenge is to determine the correlated 3D deformation and structural changes at the molecular-, fibrillar-, and cell-matrix length-scales under physiological load in intact tissue, and how these alter in ageing, injury and disease. Phase-contrast tomography (pCT) and small-angle X-ray scattering (SAXS) powerful tools for supramolecular changes and fibre-array architecture, whose integration would enable a step-change in understanding such multiscale biophysical dynamics.

This is a joint project between University College London (UCL) and Queen Mary University of London (QMUL), funded by Engineering and Physical Sciences Research Council (EPSRC), in collaboration with Diamond Light Source (www.diamond.ac.uk), ESRF (www.esrf.eu), University of Manchester and Oregon State University (USA). It aims to develop a path-breaking new X-ray bioengineering imaging modality (Tomo-SAXS) combining synchrotron phase-contrast tomography (micro) and X-ray scattering (nano) imaging in the same platform to visualize biophysical structural dynamics from the molecular to the macroscale in hydrated collagenous tissues concurrently.

You will be part of a multidisciplinary team of engineers, X-ray physicists and biologists seeking to develop and apply this technique to understand mechanisms of musculoskeletal injury and ageing in biomedically critical joints like the intervertebral disc (IVD) in the spine. Your role will be to apply tomography combined with digital volume correlation (DVC) image analysis methods to understand the mechanisms of injury in the IVD, starting with idealised synthetic collagen mimics and progressing to deformation of whole-joint animal models. Deep Learning and other AI techniques may be used to couple modalities. Development of the methodology will lead to application in a biomedical challenge, e.g. combination of tomography, DVC and SAXS to understand multiscale mechanics of injured intact joints. Computational modelling of the experimental data may be included depending on the student’s interests. Your project will be aligned with the groups developments in novel tomographic and Deep Learning imaging techniques, and you will be working with groups at the UCL main campus (see  https://mecheng.ucl.ac.uk/hip-ct/), QMUL, Harwell Campus (https://www.rc-harwell.ac.uk) and ESRF (www.esrf.eu). Most of the group sit at Harwell Campus; you could be based at UCL Bloomsbury, but will need to spend a significant amount of time at Harwell and ESRF when developing and performing experiments.

Research Aims

To combine two imaging modalities, phase contrast tomography (pCT) and digital volume correlation (DVC), testing it first on model collageneous constructs and intact IVD from animal models to predict the fibre-structure. Using in situ biomechanical loading coupled with the above methods to develop and image injurious loading protocols which disrupt the native tissue matrix structure in physiologically relevant ways. Analyse these injury-systems using pCT/DVC to predict the functional alterations in micromechanics in intact, injured joints, providing new insights into the function of the human body in health and disease.

Requirements

•    Have achieved (or are predicted) a first class or upper second class honours undergraduate degree (or equivalent international qualifications or experience). An MSc is also preferred, though not essential.
•    Our preferred subject areas are Physical Sciences (Computer Science, Engineering, Mathematics and Physics) with a preferred route through any core Engineering discipline (e.g. Bioengineering/Biomedical Engineering, Mechanical Engineering, Chemical, Electrical Engineering, etc.). All applicants must be able to demonstrate strong mathematical skills and ideally have experience in image analysis or modelling.
•    Applicants should have an interest in bioengineering combined with medical imaging as this is core to our projects.
•    Applicants whose first language is not English are usually required to provide evidence of proficiency in English by UCL, see also this website.

Funding

Please note that the funding available supports Home students or EU nationals who have obtained ‘settled’ or ‘pre-settled status’ via the EU Settlement Scheme Please refer to the UCL website for further details about the EU Settlement Scheme. Confirmation of settlement status will be required at the point of application and should be provided as an additional document during the application process.

Further guidance relating to UKRI funding eligibility found here.

How to Apply

Please send an expression of interest and current CV to Prof Peter Lee (peter.lee@ucl.ac.uk)

Deadline
Applications considered on a rolling basis until position is filled. Latest start date available Sept 2021.

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