UCL EPSRC Centre for Doctoral Training in Intelligent Integrated Imaging in Healthcare


4-year PhD studentship: PET-MR Imaging for amyloid burden estimation in dementia research

Now Closed


7 May 2019

Primary supervisor: Prof Frederik Barkhof

Secondary supervisors: Dave Cash, Prof Nick Fox

Industrial supervisor: Gill Ferrar, GE healthcare

Project summary

A 4-year PhD studentship is available in the UCL Centre for Medical Image Computing, working closely with the Brain Repair and Rehabilitation Department at UCL’s Institute of Neurology. The funding covers an annual tax-free stipend (£17,280) and tuition fees. 
As the studentship is partially funded by the EPSRC the standard EPSRC eligibility criteria apply, please see 
EPSRC website for further details. The successful candidate will join the UCL CDT in Medical Imaging cohort and benefit from the activities and events organised by the centre.


Alzheimer’s disease (AD) is the biggest threat to healthcare and characterized by the deposition of the amyloid protein in the brain. Non-invasive methods of measuring amyloid plaques using positron emission tomography (PET) offers a unique tool the increase our understanding of the molecular pathology of dementia. Detecting amyloid burden longitudinally (over a time of a few years) using PET with the aid of magnetic resonance (MR), enables observing the subtle changes of amyloid deposition in different brain regions, thus helping to better understand the initiation and spread of pathology, but also to monitor the response to newly developed therapies. The accuracy and precision of amyloid PET data quantification is limited by several physiological and technical factors, such as the weak signal of small changes in amyloid deposition over time, head motion, and the limited image resolution.  Furthermore, such data is usually collected across multiple sites while using different amyloid PET tracers, introducing additional variability in the final image-based statistics.

Research aims

The proposed project departs from the current methods, which usually consist of successive and independent postprocessing steps. Instead, the project aims at creating a unified framework in which the image reconstruction, with all the quantitative corrections and modelling being considered together to enhance the statistical power of longitudinal analysis. High statistical power is necessary for subsequent investigation of detailed spatio-temporal molecular patterns of amyloid deposition across different brain regions. Such investigation will be supported by one of the largest collections of amyloid PET datasets available at UCL and gathered from across Europe, USA and Australia, using four different amyloid PET radiotracers. In collaboration with GE Healthcare, we will develop tools that will facilitate a more accurate definition of abnormality and early detection of pathological changes, as well as more confident identification of subjects at risk of developing dementia.


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 C++ and/or Python and/or MATLAB is desirable. Some experience with medical imaging is also desirable.

To apply

Please send a CV and Covering Letter expressing your interest to Professor Frederik Barkhof f.barkhof@ucl.ac.uk