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10 Jun

Understanding Aortic Dissections Through Patient-specific Modelling and Simulation in Combination with Imaging and Clinical Data

Current Projects

Aortic dissection is a life threatening cardiovascular condition in which the aorta vessel wall splits forming an additional false vessel.This affects the flow of blood through the aorta and the circulation system downstream causing various complications. The condition is associated with high mortality rates; it is very patient specific and its progression depends on the haemodynamic characteristics of the dissection. Detailed knowledge of the dissection flow-related variables can provide insight on disease progression and severity and aid clinicians to tailor the treatment to individual patients, decide when to intervene surgically and optimise the management of the disease. Computational fluid dynamics combined with in vivo, medical imaging techniques, has the potential to revolutionise the clinical management of such diseases; however, a number of important challenges have to be overcome in order to achieve this, such as for example accuracy and computational speed.

In this study, we plan to combine in vivo medical imaging, in vitro experiments and in silico fluid-structure interaction (FSI) studies in order to understand the haemodynamics of aortic dissections. A pilot computational study of a patient-specific dissected aorta has been conducted using dynamic boundary conditions and has already highlighted the role of hemodynamic parameters in identifying regions of the dissection at risk of false lumen enlargement, rupture or malperfusion; this study will be extended to include the effect of vessel wall compliance through fluid-structure interaction personalised simulations and validated rigorously through in vitro measurements involving compliant patient-specific phantoms. The validated tool will be subsequently rolled out to a number of patient cases in order to provide insight on the effects of dissection morphology on haemodynamics and eventually clinical prognosis, with the ultimate goal to develop a reliable and computationally efficient tool that can aid the clinician in understanding and managing the disease. The proposed approach will eventually be used to develop robust and valid methodologies for in-silico analysis of other aortic diseases.

Related Methodologies

Image Reconstruction
Image Analysis
Computational Modelling
Interventional systems