Qualification Type: | PhD |
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Location: | London |
Funding for: | UK Students |
Funding amount: | Funding will be at least the UCL minimum. For stipend, please see advert |
Hours: | Full Time |
Placed On: | 3rd May 2023 |
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Closes: | 3rd August 2023 |
Reference: | 23018 |
Primary Supervisor: Professor Martina Callaghan
A 4 year funded PhD studentship is available in the Physics Group at the Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology. Funding will be at least the UCL minimum. Stipend details can be found here.
The successful candidate will join the UCL CDT in Intelligent, Integrated Imaging in Healthcare (i4health) cohort and benefit from the activities and events organised by the centre.
Project Background:
Quantitative MRI of the brain offers a unique opportunity to characterise its microstructural organisation and to reveal the neurobiology of disease. The sensitivity of ultra-high field MRI, such as 7T, means that this can be done at unprecedented spatial resolution, e.g. characterising the layers of the cortex. However, this can be time consuming and the efficacy of the approach can be degraded by inevitable participant motion during the lengthy image acquisition times. For example, in collaboration with Siemens Healthineers, we have shown that at 7T these measurements become increasingly sensitive to motion-induced changes in the transmit field of the MRI system.
This PhD studentship is focused on addressing these sensitivities to ultimately provide a high-resolution, high-precision quantitative MRI protocol for cognitive neuroscience research and clinical neurology applications. The work will be conducted within the Physics Group at the world-leading Department of Imaging Neuroscience with access to our 7T and two 3T MRI scanners, all dedicated to neuroimaging research. The project will be conducted in close collaboration with Siemens Healthineers, who are part-funding the studentship.
Research Aims:
This project will exploit our expertise in developing all aspects of the neuroimaging chain and in generative modelling to address the challenge of motion corruption of quantitative MRI estimates. It will:
Person specification & requirements:
This studentship is available for home fee payers only.
UCL’s fee eligibility criteria can a be found by following this link.
How to apply:
Please complete the following steps to apply.
Type / Role:
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