MRI is a powerful technology that has revolutionized science and medicine. In this project we’re focusing on low-field MRI (LF-MRI), which uses field strengths 60x lower than a typical clinical MR scanner. LF-MRI scanners are small, portable, and eliminate the dangers posed by high magnetic fields. This makes them ideal for deployment in remote or difficult-to-reach areas. However, their clinical utility is currently limited by poor signal-to-noise ratio (SNR) and low image contrast. That’s where you come in – as part of our team, you’ll develop cutting-edge low-field magnetic resonance imaging (LF-MRI) methods to tackle these challenges and help unlock the full potential of LF-MRI.
In this project we have the opportunity to implement established techniques used in high-field MR, and also incorporate novel approaches that are impossible or unavailable on clinical scanners. Furthermore, novel machine learning (ML) or artificial intelligence may be incorporated to augment image contrast to mitigate the problem of low SNR and image artefact. As such, the direction of study in this PhD will be guided, in part, by the interests and expertise of the student.
The CISC is an ideal place to conduct this research. The centre is equipped with a top-of-the-range 3-Tesla MRI scanner to compare and validate the images collected on the LF-MRI scanner. As part of the medical school, you’ll also have the opportunity to test these developments in patient populations as well as healthy volunteers.
This project would suit a student with an interest in medical imaging and magnetic resonance imaging physics and the computation aspects will appeal to those with good mathematical skills and an interest in computer programming. Students will present their work at top-tier national and international MR physics, medical image analysis, computer vision or machine learning conferences and funding is in place for this.
The student can look forward to guidance from a diverse, friendly and encouraging supervisory team comprised of experts in every aspect of the project. Professor Itamar Ronen is a leading expert in the field of LF-MRI and has published numerous papers on the subject. Dr Ivor Simpson is a Senior Lecturer in Artificial Intelligence and an expert on machine learning, with a focus on medical image analysis. Dr Nicholas Dowell has a track record in developing MRI techniques that can provide biomarkers for disease and Dr Samira Bouyagoub who has extensive technical experience in MRI and data analysis.
The student will be based at the beautiful University of Sussex campus, nestled in the South Down National Park, just outside the vibrant city of Brighton and Hove and within an hour’s train ride of London.
Applicants for this 3-year Brighton and Sussex Medical School-funded PhD should possess or expect to be awarded a minimum of a First or Upper Second Class Honours degree (or equivalent) in a relevant discipline in the physical sciences, engineering or computer science.
Informal enquiries should be directed to Dr Nicholas Dowell ([Email Address Removed]).
Application procedure
· Formal applications can be completed online on: https://evsipr.brighton.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app_crs&_ga=2.19252792.1648748345.1571735228-239989748.1571735228
· Create an account to start your application.
· Make sure you select research degree and select “Doctoral College” as the School, and you will see the project listed to apply directly.
Application deadline: 3rd August 2023
Interview date: 11th August 2023
Start date: 1st October 2023
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