|Published||April 9, 2023|
|Location||London, United Kingdom|
Using AI to enable imaging of exotic radionuclides for Molecular Radiotherapy
Primary Supervisor: Kris Thielemans
Secondary: Simon Arridge
Industrial supervisors: Fred Wilson (Blue Earth Therapeutics) and Daniel Deidda (National Physical Laboratory)
A 4 year funded MRes+PhD studentship is available in the UCL Institute of Nuclear Medicine , a joint institute between UCL and UCLH, in collaboration with Blue Earth Therapeutics . the Medical Physics group of the UK National Physical Laboratory and the UCL Centre for Medical Image Computing
The successful candidate will join the UCL CDT in Intelligent, Integrated Imaging in Healthcare (i4health) cohort as well as the postgraduate institute (PGI) at NPL, and benefit from the activities and events organised by these centres. The student will be primarily located at INM in the University College Hospital, near the UCL Bloomsbury Campus. Imaging facilities include SPECT-CT, PET-CT and PET-MRI scanners. Funding will cover fees, an enhanced stipend, consumables and travel.
Molecular Radiotherapy (MRT) is a rapidly growing cancer treatment modality where molecules that bind to cancerous cells are labelled with a radionuclide & injected into the patient for targeted delivery of radiation. Multimodality imaging using CT and nuclear imaging (SPECT/PET) can be performed to personalise the treatment doses as well as quantify absorbed doses to the tumours and organs at risk (OAR). UCL and NPL have several joint research projects on optimising imaging and dosimetry for theranostics using MRT.
There is increased interest in the use of exotic radionuclides such as Actinium-225 that have the potential to increase dose to the tumours while minimising impact on OARs, especially when combined with novel molecules that specifically target receptors or proteins in the tumours. However, imaging these radionuclides is very challenging due to their low abundance of generated gamma photons.
Machine learning techniques are revolutionising the field of image reconstruction in general, and for ultra-low signal data in particular.
The aim is to develop novel image reconstruction methods to enable quantitative imaging of alpha-emitters such as Actinium-225 and other exotic radionuclides. The approach will integrate physics-based machine learning methods into the reconstruction process, combining the use of advanced modelling of the imaging physics and data from planning scans with state-of-the-art Deep Learning. Optimisation of acquisition protocols will be investigated. Test data will including Monte Carlo simulations, phantom scans (acquired as part of this project) as well as patient data obtained as part of a clinical trial.
Person specification & requirements:
Candidates must meet the UCL graduate entry requirements which include holding at least an upper second-class degree or equivalent qualifications in a relevant subject area such as physics, biomedical engineering, computer science or applied mathematics. A Master’s degree in a relevant discipline, additional research and/or programming experience in imaging would be an advantage. Depending on experience the student will be entered into either a 4-year PhD or a 1-year MRes+3-year PhD programme.
A full studentship is available for home fee payers only.
UCL’s fee eligibility criteria can be found by following this link.
Application Deadline: 26th of April 2022.
How to Apply:
- Send an expression of interest and current CV to: firstname.lastname@example.org and email@example.com quoting Project Code:23020 in the email subject line.
- Make a formal application to via the UCL application portal . Please select the programme code Medical Imaging TMRMEISING01 and enter Project Code 23020 under ‘Name of Award 1’