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LEAD SUPERVISOR: Professor Peter Jezzard, Nuffield Department of Clinical Neurosciences

Co-supervisor: Professor Thomas Okell, Nuffield Department of Clinical Medicine

Commercial partner: Siemens Healthcare Ltd (part of Siemens Healthineers), Camberley

Vascular disease is a major cause of mortality and morbidity, leading to pathologies across many organ systems. In the brain it can lead to transient ischaemic attack, stroke, and vascular dementia. A pre-cursor to overt pathology is a gradual stiffening of blood vessels, which itself can lead to diffuse pathological tissue damage, for example causing white matter hyperintensities in the brain, due to harmful pulsatile pressure waves reaching further into the tissue bed. Non-invasive assessment of vascular stiffness has been possible through measurement of the pulse wave velocity (PWV) of the cardiac pressure wave as it passes through the vascular tree, although most measures to date are systemic in nature.

This project seeks to develop non-invasive MRI methodologies that target the intracranial vasculature specifically, by measuring PWV within the brain’s major arteries. This more specific measure of vessel stiffness should correlate much better with other neurovascular and diffuse brain tissue diseases than conventional measures of vessel stiffness that only provide a whole-body average. An additional measure of vascular stiffness that may also be examined as part of the project is the extent and amplitude of frequency components in the power spectra of 'resting-state’ functional MRI time series, a physiological fluctuation that is usually discarded as ‘noise’ in conventional functional MRI.

The successful student would have a background in physics, engineering, computer science or a related field, and would have an interest in applying their training to a medical application.

High spatial and temporal resolution phase-contrast MRI angiography sequences that incorporate simultaneous multi-slice acquisition will be developed and optimized to sample the cardiac waveform at multiple vessel locations throughout the brain. Analysis approaches will be investigated and optimized to measure the subtle time shifts present between the PWV waveforms collected at different points in the intracranial vascular tree, including the investigation of combined spatio-temporal image reconstruction algorithms that use prior information from the simultaneous nature of the underlying acquisition. Additional analysis methods will be developed to model from angiographic data the vascular pathlengths between key nodes in the vascular tree. Finally, the methods will be deployed with clinical collaborators for validation in patients with evidence of existing small vessel disease versus healthy subjects. Additional correlations will be made with other markers of small vessel disease, such as white matter hyperintensity load, blood pressure, and extent and amplitude of resting-state fMRI power spectra at the cardiac frequency.

This research supports the MRC priority areas of methodology development “better methods, better research”, and contributes to the research area of neurosciences and mental health. It also has relevance to the MRC focus themes of “prevention and detection” of disease, and the mission for “precision medicine”, by yielding quantitative and personalised measures of brain vascular health. It also aligns well with Siemens’ R&D priorities, where improved methods to detect and monitor small vessel disease is already part of their future strategy. Siemens also has a strong track record of training the next generation of R&D scientist, many of whom end up working for Siemens themselves.

Apply using course: DPhil in Clinical Neurosciences

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