Jessica Bagnall

Title: Deep Learning for Magnetic Resonance Quantitative Susceptibility Mapping of carotid plaques

Supervision Team: Caitríona Lally, TCD / Catherine Mooney, UCD / Brooke Tornifoglio, TCD and Karin Shmueli, UCL

Description: Carotid artery disease is the leading cause of ischaemic stroke. The current standard-of-care involves removing plaques that narrow a carotid artery by more than 50%. The degree of vessel occlusion, however, is a poor indication of plaque rupture risk, which is ultimately what leads to stroke. Plaque mechanical integrity is the critical factor which determines the risk of plaque rupture, where the mechanical strength of this tissue is governed by its composition. Using machine learning approaches and both in vitro and in vivo imaging, and in particular Quantitative Susceptibility Mapping metrics obtained from MRI, we propose to non-invasively determine plaque composition and hence vulnerability of carotid plaques to rupture. This highly collaborative project has the potential to change diagnosis and treatment of vulnerable carotid plaques using non-ionizing MR imaging which would be truly transformative for carotid artery disease management.