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Development of MRI brain tumour fingerprinting for clinical assessment of brain tumour biology

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  • Full or part time
    Dr M Grech-Sollars
    Prof D-M Koh
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

Brain tumours are one of the four cancers which are hardest to treat and for which there is a strong need for further research (Cancer Research UK). In particular, clinical imaging of brain tumours is limited by a lack of quantitative imaging techniques which can study the physical properties of the underlying tissue, and hence provide in-vivo biomarkers of changes within the tumour. Magnetic Resonance Fingerprinting (MRF) is a novel rapid imaging technique which aims to assess the physical properties of different tissues using one short MRI sequence. MRF was developed as a generic tool and limited literature exists of its application to brain tumours. There is a need to develop this tool specifically for cancer imaging, taking into consideration the clinical challenges faced by Radiologists. MRF could be tailored to produce quantitative values representing the biology specific to the tumour imaged – such as blood flow and microstructure.

Within this PhD you will develop new MRI brain tumour fingerprinting (BTF) tools to answer challenging clinical questions faced by Neuroradiologists, such as the early determination of tumour transformation in patients with lower grade tumours and tumour response to treatment. Current imaging methods have limited accuracy in differentiating between pseudo-progression and true progression or pseudo-response and true-response and we aim for these clinical questions to be addressed using machine learning and BTF. Machine learning tools will be used as part of the image reconstruction and post-processing pipeline. As part of this project you will receive training in MRI pulse sequence design, machine learning and neuro-oncology. You will also collaborate with colleagues within the Department of Computing at Imperial College London to integrate the machine learning tools and ensure the outcome of the project is a sequence, or set of sequences, that are optimised for use within a clinical environment for imaging brain tumours.

Initially, you will use currently available implementations of MR Fingerprinting sequences on Siemens scanners at Imperial College London and the Institute of Cancer Research. Sequence development will account for the needs and current limitations for cancer imaging as determined across the clinical, biological and physical sciences. Following satisfactory setup and tests in healthy volunteers your developed BTF sequences will be applied to patients with primary brain tumours. The feasibility of BTF to assess the variety of different tissue presentations in brain tumours will then be explored. In developing BTF, you will be supported by a team of clinical radiologists and physicists/engineers at Imperial College London/Imperial College Healthcare NHS Trust and the Institute of Cancer Research/the Royal Marsden Hospital.

Download a PDF of the complete project proposal:

For more information please contact Matthew Grech-Sollars:

Funding Notes

Full funding is available

Candidate profile

Candidates must have a first class or upper second class bachelor’s or master’s degree in Physics, Engineering or Computer Science. Applications from registered clinical scientists or those wishing to pursue Route 2 of the Clinical Science training scheme are welcome.

How to apply

Full details about these studentship projects, and the online application form, are available on our website, at: Applications for all projects should be made online. Please ensure that you read and follow the application instructions very carefully.

Closing date: Monday 3rd December 2018

Please apply via the ICR vacancies web portal

email contact: [Email Address Removed]
supervisor emails [Email Address Removed] and [Email Address Removed]

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