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Quantification of bone tissue growth and adaptation in longitudinal murine studies

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  • Full or part time
    Prof Lawford
    Prof Viceconti
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

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/).

Traditionally the effect of bone enhancing drugs or other interventions are investigated in murine models by sacrificing a number of animals at each time point, and then analysing bone 3D histomorphometry with micro-Computed Tomography (microCT) on dissected bones. Within an NC3R project, led by Prof Bellantuono, we recently introduced new methodology whereby in vivo microCT is used to follow the evolution of bone tissue in the same mouse over time. Current methods make the assumption that except for small local changes that are the adaptation to be measured, the bone remains the same at two distinct time points. Thus, rigid image registration is used to align multiple 3D images of the same animal at different time points, and then Boolean operators are used to compute the tissue adaptation [Lambers, 2011; Lu, 2015]. Unfortunately, most studies use 10-15 week old mice but this is an age where the long bones are still growing. Thus, the tissue adaptation produced by the intervention, is superimposed on the normal growth of the bone. To address this problem we need to develop an elastic registration algorithm that essentially simultaneously computes an affine scaling that can describe the growth component, and a local displacement scaling that quantifies the actual tissue adaptation.

The problem is further complicated by the fact that these 3D images are acquired at a high resolution (8 billion voxels), which makes the elastic registration and image interpolation computationally intensive. In this project we aim to optimise the standard ShIRT elastic registration algorithm to a) register such large datasets efficiently, and b) accurately separate growth from adaptation. We will explore the use of both multi-resolution techniques and global-local methods that are able to tackle both problems at the same time.

The developed methods will be tested for accuracy on a series of digital phantoms, and on a collection of repeated scans performed on the same mouse at the same time (zero-growth accuracy). They will then be applied to the entire cohort of experimental results collected during the NC3R project, and from any follow-up projects. The resulting registration algorithm will be packaged as a high-throughput big data analytics pipeline to be used by Insigneo biological researchers to analyse their bone research results.

Funding Notes

Salary (£14,057 per annum), fees (EU and UK) and consumables will be covered by the EPSRC.
All candidates must be eligible according to the EPSRC rules www.epsrc.ac.uk/skills/students/help/eligibility/



Entry Requirements:
Candidates must have a first or upper second class honors degree or significant research experience. Strong mathematical background is required; experience image registration and microCT will be advantageous.

Enquiries:
Interested candidates should in the first instance contact Prof Pat Lawford ([email protected])

References

References:
Lambers FM, Schulte FA, Kuhn G, Webster DJ, Müller R. Mouse tail vertebrae adapt to cyclic mechanical loading by increasing bone formation rate and decreasing bone resorption rate as shown by time-lapsed in vivo imaging of dynamic bone morphometry, Bone, 49(6):1340-50, 2011

Please complete a University Postgraduate Research Application form available here: www.shef.ac.uk/postgraduate/research/apply

Please clearly state the prospective main supervisor in the respective box and select “Cardiovascular Science” as the department.

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