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 highresolution, 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:
- Revisit how the data are acquired and define optimal sampling schemes that minimise acquisition times.
- The reconstruction of multiple different MRI contrasts will be integrated to maximise the precision and minimise the artefact levels of the final images.
- Use generative modelling to incorporate what we know about the impact of motion and exploit inherent data redundancy (covariance) to again maximise precision and remove motion-related artefacts.
Person specification & requirements:
- Have achieved, or are predicted to achieve, a first class or upper second class honours undergraduate degree (or equivalent international qualifications or experience) in physics, engineering, biomedical or computer sciences or a related discipline. An MSc is also preferred, though not essential.
- Have experience in programming, ideally using Python, MatLab, C++ or similar.
- Demonstrably strong mathematical skills.
- Established interest in neuroscience.
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.
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