| Summary: | <p>Ischaemic stroke is a major cause of death and disability worldwide. Cerebral autoregulation, which can be impaired during acute stroke, and collateral flow to brain tissue through the circle of Willis, both play a role in preventing tissue infarction. The configuration of the arterial circle varies between individuals. Thus, personalised modelling of the cerebral arterial network, to determine the potential for collateral flow, can be of significant value in the clinical context of stroke.</p> <p>The interaction between autoregulation and collateral flow remains poorly understood. In this study, steady-state physiological models of the cerebral arterial network, including several common variants of the circle of Willis, were coupled to a spatially variable mathematical representation of cerebral autoregulation. The resulting model was used to simulate various arterial occlusions, as well as bilateral and unilateral impairment of autoregulation, in each structural variant. The work identified few circle of Willis variants that present either particularly high-risk or particularly low-risk of cerebral ischaemia. Instead it was found that most variants are dependent upon the bilateral function of autoregulation to facilitate collateral flow and preserve cerebral blood flows. When autoregulation was impaired unilaterally, downstream of an occlusion, blood flows in the contralateral hemisphere were preserved at the expense of the ipsilateral tissue at risk.</p> <p>Arterial network models have in the past been personalised using structural, rather than functional, angiography measurements. This thesis presents a novel model-based method for absolute blood volume flow rate quantification in short arterial segments using dynamic magnetic resonance angiography data. The work also investigated the additional information that can be obtained from such functional angiography. The flow quantification technique was found to accurately estimate flows in shorter arterial segments than an existing technique. However, improvements to noise performance, and strategies for rejection of contaminating signals from overlapping vessels within the imaging plane, are required before the technique can be applied to personalised cerebral arterial network modelling.</p>
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