Primary aldosteronism (PA), i.e. hypertension due to adnormal function of the adrenal gland, accounts for 5-12% of all hypertension and confers a higher risk for cardiovascular and cerebrovascular complications compared to age and blood pressure (BP) matched essential hypertension. Yet PA remains under-diagnosed and under-treated, largely due to the lack of definitive management options for the majority of affected individuals, often complicated by the location of the adrenal gland itself and available navigation systems to administer effective treatments. Therefore, the overall objective of this proposal is the development, optimization, and experimental evaluation of an AI guided approach using Electromagnetic (EM)-tracking and medical Image fusion to assist navigation to the adrenal gland to allow medical professionals to assess and undertake required treatments.
Ulster University is currently offering a funded PhD scholarship to work closely with colleagues at Kansas State University and the National University of Ireland, Galway, to develop a novel management option for PA. The system uses an imaging technique that can guide and position a lazer device such that abnormal lesions that are identified on the adrenal gland can be removed using focused heat.
The overall collaborative project will therefore involve the development of AI-based imaging analysis technology to assist medical professionals to accurately guide the laser device by merging pre-procedure diagnostic CT imaging with Endoscopic ultrasound (EUS) real-time imaging which will be guided by EM-tacking technology. The image fusion process involves a careful plane and point registration of both the preprocedural CT imaging with the EUS imaging, where anatomical landmarks on both data sets will be used for image registration.
This PhD studentship will focus on exploring the development of a Spiking Neural Networks (SNNs) to process real-time EUS imaging data for points of interest near the target lesion. The PhD candidate will be based at Intelligent Systems Research Centre, Ulster University (Magee Campus) and will work with post-docs and academic staff within the Computational Neuroscience and Neuromorphic Engineering (CNET) research team. The candidate will have an opportunity to learn new skills by developing novel AI-based algorithms that can identify and analyse medical images, which has a widespread application well beyond the scope of this project.
The PhD candidate will have access to modern software modelling tools and high-performance computing cluster resources (NI HPC Cluster). The PhD researcher may also spend some time at the project partner institutes.
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
- Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
- A demonstrable interest in the research area associated with the studentship
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- First Class Honours (1st) Degree
- Masters at 70%
- For VCRS Awards, Masters at 75%
- Publications - peer-reviewed
- Experience of presentation of research findings
- Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.
Funding and eligibility
This project is funded by: DfE Match (HSC R&D/MRC)
Department for the Economy (DFE)
- The scholarship will cover tuition fees and provide a maintenance allowance of £15,609 per annum for three years (subject to satisfactory academic performance). The scholarship also provides £900 per annum as a research training support grant (RTSG) allocation.
- To be eligible for the full scholarship, applicants must meet UK residency requirements. This means that you must have been resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.
- EU nationals who do not meet UK residency are eligible to apply for a fees only award which will cover tuition fees (no maintenance support is provided).
- Non-EU nationals must be ‘settled’ in the UK by the closing date of the application or have been ordinarily resident in the UK for purposes other than study for the past three years in order to be eligible for an award.
- Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Due consideration should be given to financing your studies. Further information on cost of living