A 4-year PhD studentship is available in the UCL Centre for Advanced Biomedical Imaging (CABI), working closely with UCL Department of Computer Science. The funding covers a total tax-free stipend per annum of £17,609 plus GSK top up £3,000 over 4 years and tuition fees. As this is a GSK/BBSRC funded studentship the studentship is only open to Home Fee paying applicants, please see the UCL student fee status website for details. The successful candidate will be affiliated with the UCL CDT in Intelligent Integrated Imaging in Healthcare (i4health) and benefit from the being part of a cohort of PhD students as well as participation in the activities and events organised by the centre.
Chimeric antigen receptor (CAR)-T cells are T cells that are engineered to recognise specific tumour antigens leading to rapid CAR-T cell expansion and tumour toxicity providing a potential treatment for glioblastoma (GBM) tumour patients. However, patient responses have been highly varied and there is currently no reliable way to map CAR-T cells tumour infiltration and expansion in tissues. We have validated 89-Zirconium-oxine as a rapid cell labelling agent for Positron Emission Tomography (PET) imaging and we have also validated a genetic biosensor called Tyrosinase, an enzyme that produces melanin, for photoacoustic imaging (PAI). By dual labelling CAR-T cells, 89Zirconium PET can quantify the initial tumour infiltration of CAR-T cells directly after injection, whereas the Tyrosinase biosensor will map CAR-T cell expansion. By co-registering these PET and PAI images to anatomical MRI images we aim to provide a conclusive 3D map of cell localisation and behaviour.
Anatomical MRI will be obtained using a 9.4T Bruker Horizontal bore magnet, Tyrosinase PA Images using a VisualSonics LAZR-X scanner and 89Zirconium PET via a Mediso Nanoscan PET/CT system. The project will develop a imaging co-registration software pipeline using a common mouse brain atlas to provide 3D maps of PA signals within specific brain regions to the hyperintense region of the GBM tumour bulk via machine learning. The MRI/PA mask will then be co-registered to Mediso Nanoscan PET/CT images using the skull for anatomical localisation. These 3D maps of cell localisation in correlation with imaging outputs of disease progression will enable assessment of therapeutic efficacy.
Applicants are expected to have a first degree in Physics, Computer Science or Biomedical Engineering or relevant Physical Sciences based subject passed at 2:1 level (UK system or equivalent) or above. Good working knowledge of computer programming is required. Knowledge of MATLAB, AI and some experience with medical imaging and/or medical image analysis is also desirable.
To Apply: Please send a CV and Covering Letter expressing your interest to Dr Tammy Kalber [Email Address Removed]
APPLICATION CLOSING DATE: 16th September 2022