Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.718527
Title: An integrated imaging and modelling approach for the management of aortic dissection
Author: Noorani, Alia Shahzadi
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2016
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Abstract:
Aortic dissection is a complex dynamic disease and is the commonest manifestation of the acute aortic syndrome. Outcomes for this disease have not changed significantly despite advances in therapy. Several negative prognostic indicators have been identified but these are generally morphological entities. It is likely that aortic haemodynamics in this pathological state are complex and more than likely contribute to morphological changes seen over time. This is of particular concern in patients who are deemed stable and yet go on to develop complications. A one size fits all strategy is unrealistic in this cohort of patients. There have been considerable advances in non-invasive diagnostic techniques and although CT is reliable, quick and readily available, MRI provides excellent anatomical detail and application of black blood techniques has the potential to provide vessel wall and therefore tear morphology. Additionally, MRI delivers dynamic functional data. Coupled with computational fluid dynamics there is significant potential to develop a method of noninvasively assessing the haemodynamics in aortic dissection. The aorta is a complex organ that is subject to a variety of forces thereby undergoing deformation throughout the cardiac cycle. In the setting of a dissection with a fragile intimal flap the variation of motion of the flap can be significant. Dual phase imaging may have the potential to understand the dynamic variability in aortic motion and the deformation it experiences. In this work, four studies based on the aforementioned factors were undertaken with the aim of highlighting the need for individualised management strategy in aortic dissection.
Supervisor: Figueroa Alvarez, Carlos Alberto Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.718527  DOI: Not available
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