PhD with full funding King’s College London. All candidates are required to read carefully and understand instructions before submitting their applications.
About the Project
This project aims at improving the quality of brain MRI data available for meningioma management in sub-Saharan Africa (SSA). This will be done through the development of novel resource-efficient artificial intelligence algorithms.
Access to MRI in SSA is restricted and its quality is limited by the hardware available locally. While high-resolution high-field-strength volumetric brain MRI is typically acquired in developed countries. A typical approach used in SSA relies on acquiring stacks of low-resolution low-field-strength slices as these are faster to acquire and more suitable for locally available MR scanners.
Meningiomas are the most common primary brain tumours in adults. Also, the wide range of presentation they are associated with spans from slow-growing lesions to highly aggressive ones. As a result, therapeutic approaches becomes challenging. This makes it highly dependent on radiological findings, the later directly depending on MRI quality.
Previous work
We have shown in previous work for fetal brain and abdominal MRI . Where slice-based acquisitions are used in developed countries to combat motion artifacts, that slice to volume approaches can effectively be used to reconstruct high-quality volumetric MRI from stacks of 2D MRI slices. Yet, these approaches typically necessitate important computational resources which would lead to a bottleneck for their clinical translation in SSA. Therefore, in this project, the student will design efficient learning-based approaches for this problem and validate its usefulness using real-world meningioma data acquired in Nigeria. Also, a close collaboration with local radiographers and clinicians associated with the Medical Artificial Intelligence Laboratory (MAI Lab) in Lagos will ensure practical relevance.
Supervisors
The supervisory team combines the wide range of inter-disciplinary expertise required to successfully deliver on this project. Tom Vercauteren (first supervisor) brings his machine learning background and track record in developing translational image computing solutions. Jonathan Shapey (second supervisor) brings his neurosurgical expertise and experience in integrating AI-defined devices in the clinical workflow. Udunna Anazodo (lead external partner) brings her quantitative neuroimaging expertise and her leadership in improving access to diagnostic imaging for global health. Below are two relevant papers from the team illustrating the strength of the track record for the project both in terms of engineering methodology and global health impact.
This project is a collaboration with the Medical Artificial Intelligence Laboratory (MAI Lab), in Lagos, Nigeria (https://mailab.io/). As part of this proposed project, the MAI Lab is committing to providing local expertise as well as meningioma data. We will also seek to organise at least one on-site visit / short placement from the PhD student to the MAI Lab in Lagos.
How to apply this PhD with full funding at King’s College London
For further details and how to apply please visit the studentship webpage.
Funding Notes
This studentship is fully funded for 4 years. This includes tuition fees, stipend and generous project consumables.
Stipend: Students will receive a tax-free stipend at the UKRI rate of £21,237 (AY 2024/25) per year as a living allowance.
Research Training Support Grant (RTSG)
A generous project allowance (£5,000 / annum) will be provided for research consumables and also for attending UK and international conferences.
Tuition fees: Overseas
Other (please outline): Visa and HIS fees.
Deadline: March, 02, 2025.
