Department: Co-affiliated with LWENC @ Institute of Neurology and the Department of Medical Physics and Bioengineering
Subsection: Translational Imaging Group (TIG) within the Centre for Medical Imaging Computing (CMIC) 
Duration: 4 years 
Stipend: £16,851 per annum tax-free, full fees paid.
Closing Date for Applications: May 29th, 2017

Supervisors: Prof F Barkhof  and Dr MJ Cardoso


Applications are invited for a PhD funding opportunity at UCL, commencing in September/October 2017.


Project background
The aim of this project is to bridge the gap between advances in neuroimaging analysis and clinical applications in patients with multiple sclerosis (MS) and dementia with cerebrovascular damage. A collaboration between the UCL Institutes of Neurology and Healthcare Engineering (called quantitative neuroradiology initiative [QNI]) aims to translate the unique knowledge in imaging acquisition and analysis accumulated at UCL to normalise, segment and quantify MRI scans towards clinical applicability. Promising results have been obtained to quantify grey matter loss in Alzheimer’s disease (AD) cross-sectionally. By using reference datasets, it is now possible to perform an automatic quality control of each patient’s MRI scan, and quantify both brain volume (normalized to intracranial volume) and hippocampal volume. These parameters, which give an indication of the disease state and severity in individual patients, are automatically calculated and provided in a routine radiological report for clinical use by integrating it seamlessly into the NHS clinical picture archiving and communication system (PACS). While the dementia project is moving towards a clinical validation step, it is now time to expand the QNI concept to the cross-sectional and longitudinal assessment of MS and vascular dementia. White matter lesions and brain atrophy are interrelated processes: while the presence of lesions impacts our ability to accurately estimate brain atrophy, morphological changes results in unstable longitudinal lesion measurements. Currently, algorithms solve either the atrophy estimation or the lesion segmentation problems separately using techniques based on discrete geometry and generative models respectively.

This project will focus on the development of a fully integrated joint Bayesian model, solved using variational methods, for white matter lesion segmentation, brain parcellation and atrophy estimation, with the aim of producing unbiased, accurate and biologically plausible biomarkers across timepoints and modalities. The algorithm developed in this project will be deployed in a clinical setting, requiring their integration within the clinical workflow and an appropriate validation on relevant data through the establishment of a reliable reference dataset, such as the ADNI dataset for dementia.


Industry partnership
The QNI prototype developed for assessment of regional atrophy in dementia is being exploited by BrainMiner (, a spin-out company initiated by UCL for the use in memory clinics in the UK and abroad. The plan of this company is to expand their services in the future to other disease areas such as MS. Incorporation of WML in dementia is important for a comprehensive assessment of all cases of dementia, including vascular dementia. BrainMiner offers in-kind contributions for the development of CE-marked software, development of user requirements, integration into clinical environment, and will be happy to host the candidate for a yearly placement.

This project would best suit a student with a background in physics, applied mathematics, computer science or engineering, and a specific interest in data analysis, machine learning, mathematical modelling and/or statistics.


Funding Notes

This is a fully-funded four year PhD project, which offers Home/EU tuition fees and a stipend at research council (RCUK) rates (View Website). Students must have achieved at least an upper 2:1 in their undergraduate degree or have a Master’s degree. Students should be a UK or EU citizen but must have UK residency for 3 years prior to the start date of the programme.


How to Apply
Application should be made by emailing your CV and a personal statement explaining your interest in and suitability for the project (not over 1 page in length) to
All enquiries regarding the project itself should be made to Prof Barkhof (