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Measuring placental function in fetal growth restriction

School of Biomedical Engineering & Imaging Sciences

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Dr A Melbourne No more applications being accepted Funded PhD Project (European/UK Students Only)

About the Project

Babies that have fetal growth restriction fail to grow properly in the womb and have dangerously low oxygen levels and increased risk of poor pregnancy outcome such as stillbirth. King's College London and University College London are jointly developing advanced imaging techniques to measure perfusion and oxygenation throughout the fetus and placenta, and applying these techniques to understand our most at-risk pregnancies. This project will take advantage of the significant links between our institutions and our global research partners in Belgium, Australia and New Zealand. Our large volume of pre-existing data and data collection across our global collaborators make this a robust post-Covid research opportunity.

Project description

This project will compare advanced magnetic resonance imaging (MRI) techniques from appropriately grown and fetal growth-restricted (FGR) pregnancies to understand why FGR pregnancies are associated with poor outcome (eg stillbirth, cerebral palsy). Τhis project presents a unique opportunity to shed light in a most troubling aspect of pregnancy – abnormal placental function. The combination of advanced MRI imaging, extensive datasets, and advanced technological tools make this an exciting project with large scope for potential impact. 

Working in our research team, the student will learn state-of-the-art imaging and data analysis techniques and explore advanced computer programming skills and machine learning methods to develop new ways to understand this important pregnancy complication [1,2,3,4]. The technical nature of this project will ensure that the student develops substantial transferrable skills for their future career. King’s College London specialist programme in healthcare technologies will provide relevant taught modules, ensuring the student develops a versatile and wide-ranging skill set. They will also participate in Patient Public Engagement to understand impact of healthcare engineering innovation.

Making use of our large pre-existing datasets on placental insufficiency and twin pregnancy, completion of this research will produce new results on the analysis of the placenta and its importance in FGR and complicated twin pregnancy. The novelty of the data will ensure academic publications in scientific journals and conference publications.

Specifically, the student will develop expertise in data analysis:

  • methods for automated motion correction and imaging artefact identification in this advanced MRI data [5,6]
  • development of new compartmental mathematical models of placental function and machine learning methods for the measurement of blood flow and oxygenation parameters and the estimation of parameter precision.
  • to optimise and combine acquisition protocols for our ongoing and future global studies [1,2]
  • to understand the differences in placental function between different placental types.

The student will use this knowledge to:

  • develop new multi-compartment mathematical models of fetal and maternal circulation for placental MRI, with supporting data from other imaging methods such as microCT and ultrasound [7], including developing new markers of placental functional heterogeneity and redundancy.
  • develop stochastic models of blood vessel growth in normal and pathological placentation for prediction of intervention.
  • measure differences (with clinical guidance), between fetal organs in FGR and twin pregnancy such as the lung, liver, kidney, brain and how these differences change with gestational age and disease severity.
  • apply these methods to new and existing data being acquired by our collaborators at the University of Auckland and at UZ Leuven.

The student will be immersed in a multi-disciplinary environment with the opportunity to work alongside engineers, clinicians and scientists. This research is highly mathematical and will help the student develop their postgraduate skills including advanced statistical analysis and machine learning with the potential for direct patient impact.

Only home UK or EU/EEA candidates fulfilling the 3-year UK residency requirement are eligible for the EPSRC DTP studentships. EU/EEA applicants are only eligible for a full studentship if they have lived, worked or studied in the UK for 3 years prior to the funding commencing.

How to apply

Please submit an application for the Biomedical Engineering and Imaging Science Research MPhil/PhD (Full-time) programme using the King’s Apply system.

Please include the following with your application: 

  • A PDF copy of your CV should be uploaded to the Employment History section.
  • A PDF copy of your personal statement using this template should be uploaded to the Supporting statement section.
  • Funding information: Please choose Option 5 “I am applying for a funding award or scholarship administered by King’s College London” and under “Award Scheme Code or Name” enter BMEIS_DTP. Failing to include this code might result in you not being considered for this funding scheme.

Application closing date

2 April 2021 (Applications may close early if a suitable candidate is found)

Contact information for enquiries

Dr Andrew Melbourne at [Email Address Removed]

Funding Notes

Stipend: ca £17,000 + generous consumables budget
Only home UK or EU/EEA candidates fulfilling the 3-year UK residency requirement are eligible for the EPSRC DTP studentships. EU/EEA applicants are only eligible for a full studentship if they have lived, worked or studied in the UK for 3 years prior to the funding commencing.
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