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An aneurysm is defined as a localized balloon like expansion of an artery and is typified by having a diameter of 1.5 times that of the normal artery and rupture is a life threatening event with survival rates after rupture as low as 20%.
Ongoing work aims to improve the diagnostic prediction of rupture using advanced imaging to detect and characterize calcification in abdominal aortic aneurysms (AAAs). Numerical models will be generated which will include the calcification parameters and enable improved prediction of AAA rupture. Varying degrees of calcification have been found in most AAA wall tissue and it has been found to correlate with an increase in AAA rupture risk. Despite this, the inclusion of calcification in computational rupture risk assessment models has largely been ignored due to the lack of available mechanical data relating to calcified AAA tissues. Individual calcified deposits are often small and difficult to include in the material property models of the tissue. Previous work by McGloughlin’s team has implicated calcification as a key contributor to rupture risk. The development of tools capable of quantifying this mineral in vivo will lead to improved diagnostics of aneurysm behavior.
This project aims to develop a new approach to the analysis of Computer Tomography (CT) images using combined 2D and 3D deformable shape models to identify and quantify the calcification content of aneurysmal tissue.
PI: Dr. Tim McGloughlin
Co-PI: Dr. Harish Bhaskar
Funding: Al Jalila Foundation 2015-2017
Publications: Segmentation of Abdominal Aortic Aneurysm (AAA) based on topology prior model
Safa Salahat, Ahmed Soliman, Tim, McGloughlin, Nauofel Werghi, Ayman El-Baz, MIUA2017, Edinburgh July 2017